EP3331436A1

EP3331436A1 - Methods and systems for acoustically stimulating brain waves

Info

Publication number: EP3331436A1
Application number: EP16757328.6A
Authority: EP
Inventors: Quentin SOULET DE BRUGIERE; Hugo MERCIER; Valentin THOREY; Olivier TRANZER
Original assignee: Rythm
Current assignee: Dreem SAS
Priority date: 2015-08-04
Filing date: 2016-08-04
Publication date: 2018-06-13
Also published as: CN108024754A; FR3039773A1; WO2017021662A1; US20180236232A1

Abstract

The invention relates to a method and system for personalized acoustic stimulation of the brain waves of a person. The system comprises an acoustic stimulation device (1) and a remote server (100). The device (1) comprises: a memory (6) storing operation parameters, a means (3) for acquiring a measurement signal (S), an analysis means (5) for assessing if the person is fit for stimulation, and an emission means (4) for emitting a sound signal. The acquisition means (3), the analysis means (5), and/or the emission means (4) operate on the basis of the operation parameters. The device (1) and the remote server (100) comprise data transmission means (7, 110) for transmitting data for operation of the device to the server and transmitting second operation parameters from the server to the device. The remote server (100) comprises a means (120) for processing operation data in order to determine the second operation parameters.

Description

METHODS AND SYSTEMS FOR ACOUSTIC WAVE STIMULATION

CEREBRALS.

FIELD OF THE INVENTION

The present invention relates to methods and systems for acoustic stimulation personalized brain waves of a person.

BACKGROUND OF THE INVENTION

Methods are known for stimulating a person's brain waves, especially during the different phases of sleep.

For example, WO 2008/039930 describes an example of such a method in which stimulation of brain waves is implemented to promote the generation of slow brain waves during deep sleep.

During such a process, a power spectrum of an encephalogram of a person is analyzed to determine the sleep stage reached by said person. When the person is considered to have reached a deep sleep stage, periodic stimulation at a predefined frequency is transmitted for a predetermined period of time. The frequency of the stimulation is defined upstream to be close to a frequency of slow brain waves.

However, such a method has drawbacks. Indeed, each person has a particular brain activity, which also evolves over time with the physical and mental state of the person. Such a stimulation process therefore works more or less according to the person to whom it is applied and, for the same person, according to the moment at which it is applied.

The present invention is intended in particular to improve this situation.

To this end, the subject of the invention is a method for acoustic stimulation personalized of the brain waves of a person comprising:

a first step of acoustic stimulation of brain waves of a person implemented by a device for acoustic stimulation of brain waves adapted to be worn by the person, the stimulation device comprising a memory storing first operating parameters, the step initial stimulation with sub-steps:

a) acquisition of at least one measurement signal representative of a physiological signal of the person,

b) analyzing the measurement signal to estimate whether the person is in a state of fitness for stimulation, and c) if it is judged that the person is in a state of fitness for stimulation, emission an acoustic signal, audible by the person, and synchronized with a predetermined temporal pattern of the brain wave of the person, in which at least one of the acquisition substeps a), analysis b) and emission c) is performed according to the first operating parameters;

a step of transmitting operating data to a remote server from the device, said operating data including at least said measurement signal;

a step of processing, on the remote server, operating data to determine second operating parameters;

a step of transmitting and storing the second operating parameters from the remote server in the memory of the device; and a second step of acoustic stimulation of the brain waves implemented by the device in which the memory of the device stores the second operating parameters, and in which at least one of the acquisition sub-steps a), analysis b ) and emission c) is performed according to the second operating parameters.

In preferred embodiments of the invention, one or more of the following may also be used:

the operating parameters comprise at least one parasitic frequency of the measurement signal,

and the acquisition substep a) comprises frequency filtering said parasitic frequency into the measurement signal;

the operating parameters comprise at least one energy threshold of a spectrum of the measurement signal, and the analysis sub-step b) comprises the comparison of an energy of a spectrum of the measurement signal with said threshold. ;

the operating parameters comprise at least one temporal frequency threshold of a predefined pattern of the measurement signal,

and the analysis sub-step b) comprises identifying said predefined pattern in the measurement signal and comparing a frequency of said pattern in the measurement signal with said threshold;

the operating parameters comprise at least a threshold of level of muscular activity,

and the analysis sub-step b) comprises determining a level of muscle activity from the measurement signal and comparing said level of muscle activity with said threshold. ; the analysis sub-step b) is implemented at least in part by an algorithm for automatic classification of measurement data determined from the measurement signal,

and the operating parameters comprise at least one parameter of said automatic classification algorithm and / or at least one class database of said automatic classification algorithm;

the operating parameters comprise at least one parameter chosen from a list comprising a sound level, a duration, a spectrum and a temporal pattern of an acoustic signal,

and the acoustic signal emitted during the transmission sub-step c) is transmitted as a function of said parameter;

the operating parameters comprise at least one parameter chosen from a list comprising a brain wave phase of the person and a predefined temporal wave pattern of the person's brain wave,

and the acoustic signal emitted during transmission sub-step c) is transmitted so as to be synchronized according to said parameter;

the processing step includes analyzing the measurement signal on the remote server to identify at least one parasitic frequency of the measurement signal,

and the second operating parameters are determined as a function of said parasitic frequency;

the processing step includes the search in the measurement signal of at least one predefined pattern indicative of an alarm clock or a beginning of awakening of the person and following the emission of an acoustic signal, so determining a wake-up indicator under the effect of the stimulation,

and the second operating parameters are determined according to said wake up indicator;

the processing step comprises comparing a portion of the measurement signal acquired after the transmission of an acoustic signal with a reference portion of the measurement signal, so as to determine an indicator of the impact of the stimulation,

and the second operating parameters are determined according to said impact indicator;

the determination of the second operating parameters as a function of the impact indicator of the stimulation comprises the implementation of an automatic classification algorithm, said automatic classification algorithm being preferably defined during a learning step automatic preliminary;

the first step of acoustic stimulation of the brain waves of a person is implemented a plurality of times by a plurality of respective devices, respectively capable of being worn by a plurality of respective persons,

the step of transmitting operating data to the remote server is implemented a plurality of times from said plurality of respective devices, so as to respectively transmit a plurality of respective operating data, respectively comprising at least one measurement signal of each said respective devices,

and the processing step comprises analyzing said plurality of operating data so as to determine second operating parameters to be transmitted and stored in the memory of at least one of the plurality of devices;

the first stimulation step is repeated a plurality of times by a device during a period of operation of said device,

and the step of transmitting operating data to a remote server from the device is carried out after said operating period, the operation data including at least one measurement signal for each of the reiterations of the first stimulation step;

the operating period of the device extends over a period of several hours, preferably at least eight hours;

transmitting an acoustic signal synchronized with a predefined temporal pattern of a brain wave comprises determining, from the measurement signal, a temporal shape of a brain wave,

determining, from said cerebral wave temporal form, at least one timing instant of synchronization between the predefined brain wave temporal pattern and a predefined temporal pattern of the acoustic signal, and

controlling an acoustic transducer of the device so that the predefined temporal pattern of the acoustic signal is transmitted at said timing instant of synchronization;

the brain wave is a cerebral slow wave having a frequency of less than 5 Hz and greater than 0.3 Hz;

- The acoustic signal is an intermittent signal and a duration of the acoustic signal is less than a period of a brain wave, preferably less than a few seconds, preferably less than one second;

- the acoustic signal is a continuous signal and a duration of the acoustic signal is greater than a period of a brain wave;

the predefined temporal pattern of brain wave corresponds to a local temporal maximum of a brain wave, a local temporal minimum of a brain, a rising edge or a falling edge of a local maximum or minimum of a wave cerebral, a predefined succession of at least a local temporal maximum and at least a local temporal minimum of a brain wave or a rising or falling edge of such a succession.

The invention also relates to a system for acoustic stimulation personalized brain waves of a person, the system comprising a device for acoustic stimulation of brain waves of a person and a remote server,

in which the device comprises:

a memory capable of storing operating parameters comprising at least one of first operating parameters and second operating parameters,

means for acquiring at least one measurement signal representative of a physiological signal of the person,

means for analyzing the measurement signal to estimate whether the person is in a state of aptitude for stimulation, and

transmission means adapted to emit an acoustic signal, audible by the person, and synchronized with a predetermined temporal pattern of the brain wave of the person if it is judged that the person is in a state of aptitude for stimulation,

in which at least one of the acquisition means, the analysis means and the means transmission is able to operate according to the operating parameters stored in the memory,

wherein the device and the remote server comprise respective data transmission means adapted for

transmitting operating data from the device to the remote server, said operating data comprising at least said measurement signal, and

transmitting second operating parameters from the remote server of the remote server to the device and storing said second operating parameters in the memory of the device,

and wherein the remote server includes operating data processing means for determining second operating parameters.

Other features and advantages of the invention will become apparent from the following description of several of its embodiments, given by way of non-limiting examples, with reference to the accompanying drawings.

On the drawings:

FIG. 1 is a schematic view of a device for acoustic stimulation of the brain waves of a person according to one embodiment of the invention,

FIG. 2 is a detailed perspective view of the device of FIG. 1 in which the device comprises, in particular, a first and a second acoustic transducer respectively capable of emitting acoustic signals respectively stimulating a right inner ear and an inner left ear of the nobody,

FIG. 3 is a block diagram of a system according to an embodiment of the invention comprising a device and a remote server,

FIG. 4 is a block diagram of a system according to another embodiment of the invention comprising a plurality of devices and a remote server,

FIG. 5 is a flowchart illustrating an embodiment of a method for personalizing an acoustic stimulation of the brain waves of a person according to one embodiment of the invention,

FIG. 6 illustrates a cerebral slow wave temporal shape, an acoustic signal and predefined temporal patterns according to an exemplary embodiment of the invention.

In the different figures, the same references designate identical or similar elements.

As illustrated in Figures 1 to 4, the invention relates to a system 1000 of acoustic stimulation personalized brain waves of a person P.

The system 1000 is able to implement a method of personalizing an acoustic stimulation of the brain waves of the person P which is illustrated in particular in FIG.

The system 1000 comprises an acoustic stimulation device 1 and a remote server 10.

The device 1 is adapted to be worn by the person P, for example during a sleep period of the person.

The device is for example adapted to be worn on the head of the person P.

To this end, the device 1 may comprise one or more support elements 2 capable of at least partially surrounding the head of the person P so as to be there maintained. The support elements 2 take for example the shape of one or more branches that can be arranged so as to surround the head of the person P to maintain the device 1.

The device 1 can also be divided into one or more elements, able to be worn on different parts of the body of the person P, for example on the head, on the wrist or on the torso.

The device 1 also comprises means 3 for acquiring at least one measurement signal, transmission means 4 designed to emit an acoustic signal audible by the person P, means 5 for analyzing the measurement signal, and less a memory 6.

The acquisition means 3, the transmission means 4, the analysis means and the memory 6 enable the device 1 to implement a step of acoustic stimulation of the brain waves of the person P which will now be described in more detail. details.

This step of acoustic stimulation of the brain waves of the person P can be repeated one or more times.

Thus, in particular, the stimulation step may be repeated a plurality of times by the device 1 during a period of operation of the device, for example during a sleep period of the person P.

Such a period of operation of the device may extend over a period of several hours, for example at least eight hours, that is to say about one night's sleep.

In one embodiment of the invention, the device 1 is able to implement the stimulation step during the operating period without communicating with the remote server 100, that is to say to operate autonomously during the operating period. In this way, it is possible in particular to reduce the exposure of the person P to electromagnetic radiation.

For example, the device 1 may comprise a battery 8. The battery 8 may be mounted on the support element 2 in the manner described above for the acquisition means 3, the transmission means 4 and the transmission means. 5. The battery 8 may in particular be able to supply the acquisition means 3, the transmission means 4 and the analysis means 5, the memory 6 and the communication module 7. The battery 8 is preferably able to provide energy over a period of several hours without recharging, preferably at least eight hours so as to cover a period of average sleep of a person P.

In this way, the device 1 can operate autonomously during a sleep period of the person P. In this way in particular, the device 1 is autonomous and able to implement one or more cerebral slow wave stimulation operations without communicate with an external server 100, in particular without communicating with an external server 100 over a period of several minutes, preferably several hours, preferably at least eight hours.

By "autonomous" is thus meant that the device can operate for an extended period of several minutes, preferably several hours, especially at least eight hours, without needing to be recharged with electrical energy, to communicate with elements external such as the remote server or to be structurally connected to an external device such as a fastener such as an arm or a stem. In this way the device is able to be used in the daily life of a person P without imposing particular constraints.

To enable the implementation of the acoustic stimulation step, the acquisition means 3, the transmission means 4, the analysis means 5 and the memory 6 are moreover operably connected to each other and able to exchange data. information and orders.

For this purpose, the acquisition means 3, the transmission means 4, the analysis means 5 and the memory 6 are mounted on the support element 2 so as to be close to one another so that the communication between these elements 3, 4, 5, 6 is particularly fast and at high speed.

The memory 6 may in particular be permanently mounted on the support element 2 or may be a removable module, for example a memory card such as an SD card (acronym for the term "Secure Digital").

The memory 6 is able to record data which will be detailed in the following description and may comprise at least one of the following elements: a measurement signal S acquired by the acquisition means 3, operating parameters of the device 1.

The operating parameters may in particular be first operating parameters or second operating parameters, as will be detailed below.

In particular, the device 1 can be configured such that only one set of operating parameters stored in the memory 6 is used at a given instant. For this purpose, the memory 6 may, for example, store operating parameters that are, mutually exclusive, either first operating parameters, ie second operating parameters as defined below.

The memory 6 is capable of being updated dynamically, so that the measurement signal and / or the operating parameters recorded in the memory 6 can be modified during the operation of the device 1, as will be described in more detail in FIG. following the description.

The stimulation step can thus firstly comprise a substep of acquisition of at least one measurement signal S by means of the acquisition means 3.

The measurement signal S can in particular be representative of a physiological electrical signal E of the person P.

The physiological electrical signal E may for example comprise an electroencephalogram (EEG), an electromyogram (EMG), an electrooculogram (EOG), an electrocardiogram (ECG) or any other biosignal measurable on the person P.

To this end, the acquisition means 3 comprise for example a plurality of electrodes 3 adapted to be in contact with the person P, and in particular with the skin of the person P to acquire at least one measurement signal S representative of a physiological electrical signal E of the person P.

The physiological electrical signal E advantageously comprises an electroencephalogram (EEG) of the person P.

To this end, in one embodiment of the invention, the device 1 comprises at least two electrodes 3 including at least one reference electrode 3a and at least one EEG measuring electrode 3b.

The device 1 may further comprise a ground electrode 3c.

In a particular embodiment, the device 1 comprises at least three EEG measuring electrodes 3c, so as to acquire physiological electrical signals E comprising at least three electroencephalogram measurement channels.

The EEG measuring electrodes 3c are for example disposed on the surface of the scalp of the person P.

In other embodiments, the device 1 may further comprise an electrode for measuring the EMG and, optionally, an electrode for measuring EOG.

The measurement electrodes 3 may be reusable electrodes or disposable electrodes. Advantageously, the measurement electrodes 3 are reusable electrodes so as to simplify the daily use of the device.

The measurement electrodes 3 may be, in particular, dry electrodes or electrodes covered with a contact gel. The electrodes 3 may also be textile or silicone electrodes.

The acquisition means 3 may also include measuring signal acquisition devices S not only electrical.

A measurement signal S can thus be, in general, representative of a physiological signal of the person P.

The measurement signal S can in particular be representative of a physiological signal of the non-electrical or non-completely electrical person P, for example a signal of cardiac activity, such as a heart rate, a body temperature of the person P or even the movements of the person P.

For this purpose, acquisition means 3 can include a heart rate detector, a body thermometer, an accelerometer, a breathing sensor, a bioimpedance sensor or a microphone.

The acquisition means 3 may also include measuring signal acquisition devices S representative of the environment of the person P.

The measurement signal S can thus be representative of a quality of the air surrounding the person P, for example a carbon dioxide or oxygen level, or a temperature or a level of ambient noise.

Finally, the acquisition means 3 may comprise user input devices allowing the person P to enter information such as a subjective night quality index or a subjective number of times that the person P estimates to have been awakened by the device 1.

The measurement signal S can then be representative of a piece of information of the person P.

In one embodiment of the invention, the substep of acquisition of the measurement signal S also comprises a pretreatment of the measurement signal S.

The preprocessing of the measurement signal S can comprise, for example at least one of the following pretreatments:

a frequency filter, for example a frequency and / or wavelet filtering of the measurement signal S in a range of temporal frequencies of interest, for example a frequency range in a range from 0.3 Hz to 100 Hz,

a frequency and / or wavelet filtering of parasitic frequencies of the measurement signal S, for example able to filter at least at least one parasitic frequency of the measurement signal S, for example a parasitic frequency belonging to a frequency range from 0.3 Hz to 100 Hz,

an elimination of predefined artifacts of the measurement signal S.

To this end, one or more interfering frequencies can be predefined and stored in the memory 6 of the device 1. Similarly, one or more artifacts can be predefined and stored in the memory 6 of the device 1, for example in the form of predefined patterns of the measurement signal S.

Said interference frequency (s) and / or artifacts can form operating parameters of the device 1.

Said parasitic frequency (s) and / or the artifacts may vary over time, so that the pretreatment of the signal S is variable over time.

Said parasitic frequency (s) and / or the artifacts may in particular vary as a function of an absolute time or a relative time.

By an "absolute time" is meant a time independent of the operation of the device, for example an hour, a day of the week, a month, a moment in a calendar of the person P (holiday period, holiday, biological rhythm of the person P).

By a "relative time" is meant a time elapsed since an event detected by the device, for example a time elapsed since a previous determination of aptitude for stimulation, a time elapsed since a previous stimulation or a time elapsed since a previous identification of an awakening or awakening of the person P.

Pretreatment of the measurement signal S can also include pretreatments such as:

an amplification, for example an amplification of the measurement signal S by a factor ranging from 10 ^A 3 to 10 ^A 6, and / or

a sampling of the measurement signal S by means of an analog-digital converter able, for example, to sample the measurement signal S with a sampling rate of a few hundred Hertz, for example 256 Hz or 512 Hz.

Such pretreatment of the measurement signal S may for example be implemented by an analog module or a digital module of the acquisition means 3. In particular, the acquisition means 3 may comprise active electrodes capable of producing one pretreatments detailed above.

The analysis means 5 receive the measurement signals S acquisition means 3, possibly pre-processed as detailed above.

From the measurement signals S, the analysis means 5 are able to estimate whether the person P is in a state of aptitude for stimulation during a substep of analysis of the measurement signal.

In a first embodiment, by "a state of aptitude for stimulation" is meant that when the stimulation is preferentially to be performed during a sleep period, the analysis means 5 are able to estimate whether the a person P is in a state of sleep deep enough to be able to be given auditory stimulation without the risk of being awakened or that the auditory stimulation triggers a wake-up call.

In a variant or in addition, it may also be understood by "a state of aptitude for stimulation" that the analysis means 5 are able to estimate whether the person P is in a sleep state in which auditory stimulation may have a desired effect. Thus, the analysis means 5 may be able to estimate whether the person P is in a deep sleep state so that an auditory stimulation may have the effect of lengthening the duration of said deep sleep.

The analysis means 5 are thus for example able to determine an index of aptitude for stimulation from the measurement signals S.

Such a "stimulation aptitude index" may for example be a binary index taking a value "suitable for stimulation" and a value "not suitable for stimulation". In variants, the stimulation ability index may take intermediate values, indicating for example a percentage of fitness to state stimulation between the extremal values detailed above.

For this, the analysis means 5 can analyze a signal of cardiac activity, a body temperature or even movements of the person P.

The analysis means 5 can also analyze at least one measurement signal S representative of a physiological electrical signal E of the person P.

Thus, an analysis of the measurements of the cerebral, ocular and muscular activity makes it possible to evaluate the depth of the sleep of the person P.

The analysis means 5 may, for example, implement one or more predefined pattern recognition algorithms on the measurement signal S so as to identify slow oscillations, K complexes, spindles, an alpha rhythm, or even awakenings in the measurement signal S.

In a first exemplary embodiment, a spectrum frequency of the measurement signal S can be determined. The predefined shapes are then determined from a variation of energy of the frequency spectrum in predefined frequency bands such as for example a frequency band of the alpha waves (8 12 Hz), beta (> 12 Hz), delta ( <4Hz) or theta (4 7 Hz).

Frequency spectrum energy in one or more of said frequency bands can be calculated, for example using a short-term fast fourier transform.

In another exemplary embodiment, possibly combinable with the first exemplary embodiment indicated, the predefined shapes can be determined directly in the temporal form of the measurement signal S, in particular by searching for one or more predefined patterns in the measurement signal S .

Thus, for example, slow oscillations and complexes can be detected by searching for consecutive zeros spaced less than about one second apart and seeking a maximum peak to peak.

When said maximum peak to peak exceeds a certain threshold, a slow wave or a complexK can then be recorded.

The analysis means 5 can also estimate whether the person P is in a state of aptitude for stimulation from a measurement signal representative of the movement of the eyes, for example an electrooculogram.

For this purpose, the analysis means 5 can for example calculate a sliding average of a variation of the movement of the eyes.

The analysis means 5 can further estimate whether the person P is in a state of aptitude for stimulation from a measurement signal representative of a level muscle activity.

To implement the substep of analysis, the analysis means 5 can then compare each of said quantities calculated from the measurement signal with a predefined threshold to estimate whether the person P is in a fitness state. stimulation, for example sufficiently sleepy to receive stimulation.

The result of this comparison may provide a stimulation ability index as defined above.

Thus, for example, the analysis means 5 can compare an energy of a spectrum of the measurement signal S with a predefined energy threshold of a spectrum of the measurement signal S.

The analysis means 5 may also compare a frequency of a predefined pattern identified in the measurement signal with a predefined time frequency threshold of said pattern in the measurement signal.

The means of analysis 5 can further compare a level of muscle activity with a predefined threshold level of muscle activity.

In this way, a plurality of thresholds can be predefined and stored in the memory 6 of the device 1 and form operating parameters of the device 1.

Said thresholds may vary over time, so that the determination of the aptitude index is variable over time.

The thresholds may in particular vary according to an absolute time or a relative time as detailed above.

In another embodiment of the invention, possibly combinable with the embodiment detailed above, the index of aptitude for stimulation can be determined at least partly by implementation, by the analysis means 5, an automatic classification algorithm for measuring data determined from the measurement signal S.

Said measurement data may be the measurement signal S itself or data calculated from the measurement signal S as detailed above, ie for example an energy of a spectrum of the signal of measure S, a frequency of a predefined pattern identified in the measurement signal or a level of muscle activity.

Said automatic classification algorithm is for example defined during a preliminary automatic learning step. Such a preliminary automatic learning step is known from the literature. It may comprise a transfer learning operation ("transfer learning") for changing the input database, for example to apply to an input database, possibly smaller, an algorithm driven on another possibly larger database (to give a non-limitative example: to apply to persons aged 20 to 25 years old the results obtained on people aged between 40 and 45). By "automatic classification algorithm" we mean an algorithm adapted to automatically classify the measurement data, that is to say to associate a class with them from qualitative or quantitative rules characterizing the measurement data.

Said class associated with the measurement data may be selected from a class database, or may be an interpolated value from a class database.

A "class" may thus be for example an identifier, for example an alphanumeric identifier, or still a numerical value, including an integer or real value.

The index of aptitude for stimulation can then be determined from the class obtained.

The obtained class can directly provide a value of the aptitude index for stimulation or can provide intermediate data, in particular intermediate data relating to the measurement signal S such as an identification of a predefined pattern in the measurement signal. S, for example an identification of a K-complex pattern or a "spindle". The intermediate data are then used to determine an index of aptitude for stimulation, for example by treatment and comparison with thresholds as detailed above.

Such an algorithm can for example implement a neural network, a support vector machine (or wide margin separator), a decision tree, a random forest of decision trees, a genetic algorithm or a factor analysis. linear regression, Fisher discriminant analysis, logistic regression, or other known methods of classification.

Such an algorithm may include a plurality of parameters that define the qualitative or quantitative rules from which the automatic classification algorithm automatically classifies the measurement data.

Such parameters are, for example, the weights of certain neurons or of all the neurons for an algorithm implementing a network of neurons.

At least one parameter of the automatic classification algorithm and / or a class database may be predefined and stored in the memory 6 of the device 1 and form operating parameters of the device 1.

As indicated above, said parameter of the automatic classification algorithm and / or class database may vary over time, so that the determination of the aptitude index is variable over time .

Said parameter of the automatic classification algorithm and / or class database can in particular vary according to an absolute time or a relative time as detailed above.

The parameters of the automatic classification algorithm can for example be predefined during a supervised automatic learning step, or more or less automatically determined, for example by the implementation of a semi-automatic learning step. supervised, partially supervised, unsupervised or reinforced. As indicated above, the automatic learning step may comprise a transfer learning operation.

The class database may also be predefined during such a learning step.

Such an automatic learning step can be implemented from a measurement data learning sample.

Finally, the stimulation step may comprise a substep of transmitting an acoustic signal A.

For this purpose, the transmission means 4 are designed to emit an acoustic signal A, audible by the person, and synchronized with a predefined temporal wave pattern Ml of the person if it is estimated that the person is in a state of aptitude for stimulation.

For this purpose, the transmission means 4 comprise by for example at least one acoustic transducer 10 and a control electronics 11.

The control electronics 11 is particularly adapted, in real time flexible, to receive the acquisition means 3 the measurement signal S and to control the transmission by the acoustic transducer 10 of an acoustic signal A synchronized with a temporal pattern predefined T of a cerebral slow wave of the person P.

By "soft real time" is meant an implementation of the stimulation operation such as temporal constraints on this operation, in particular on the duration or repetition frequency of this operation. , are respected on average over a predefined total implementation period, for example a few hours. In particular, the implementation of said operation may at times exceed said temporal constraints as long as the average operation of the device 1 and the average implementation of the method respects them over the total predefined implementation time. In particular, time limits may be predefined beyond which the implementation of the stimulation operation must be stopped or paused.

To enable such a flexible implementation in real time, a maximum distance between the acquisition means 3, the transmission means 4, the analysis means 5 and the memory 6 may be less than about one meter and preferably less than a few tens of centimeters. In this way, a sufficiently fast communication between the elements of the device 1 can be guaranteed.

The acquisition means 3, the transmission means 4, the analysis means 5 and the memory 6 can by For example, they can be housed in the cavities of the support element 2, clipped onto the support element 2 or else fixed to the support element 2, for example by gluing, screwing or any other suitable fastening means. In one embodiment of the invention, the acquisition means 3, the transmission means 4, the analysis means 5 and the memory 6 can be mounted on the support member 2 removably.

Without an advantageous embodiment of the invention, the control electronics 11 is functionally connected to the acquisition means 3 and to the acoustic transducer 10 via wired links 10. In this way, the exposure of the control electronics 11 is reduced. the person P to electromagnetic radiation.

The acoustic transducer or transducers 10 are able to emit an acoustic signal A stimulating at least one inner ear of the person P.

In a first embodiment illustrated in particular in Figures 1 and 2, an acoustic transducer 10 is an osteophonic device stimulating the inner ear of the person P by bone conduction.

This osteophonic device 10 may for example be able to be placed close to the ear, for example above as shown in Figure 1, in particular on a skin area covering a cranial bone.

In a second embodiment, the acoustic transducer 10 is a speaker stimulating the inner ear of the person P through an ear canal leading to said inner ear.

This speaker may be disposed outside the ear of the person P or in the ear canal.

The acoustic signal A is a modulated signal belonging at least partially to a frequency range audible by a person P, for example the range from 20Hz to 30 kHz.

The control electronics 11 receives the measurement signals S acquisition means 3, possibly pretreated as detailed above.

If the measurement signals S received by the control electronics 11 are not pretreated, the control electronics 11 can in particular implement one and / or the other of the pretreatments detailed above.

The control electronics 11 is then able to implement a brain wave stimulation operation of the person P, an operation which will now be described in more detail.

Brain waves can in particular be slow brain waves.

By "cerebral slow wave" is meant in particular a cerebral electrical wave of the person P having a frequency of less than 5 Hz and greater than 0.3 Hz. By "cerebral slow wave", it is possible to hear a cerebral electrical wave of the person P having a peak to peak amplitude of, for example, between 10 and 200 microvolts. In addition to the very low frequency waves below 1 Hz, cerebral slow waves are also understood to mean, in particular, delta waves of higher frequencies (usually between 1.6 and 4 Hz). A cerebral slow wave can still be understood to mean any type of wave having the characteristics of frequency and amplitude mentioned above. Thus, for example, phase 2 sleep waves called "K-complexes" can be considered as slow brain waves for the invention.

In general, the implementation of the invention may for example take place during a phase of sleep of the person P (as identified for example in the AASM standards, acronym for "American Academy of Sleep Medicine"), for example a phase of deep sleep of the person P (commonly called stage 3 or stage 4) or during other phases of sleep, for example during light sleep of the person (usually called stage 2).

The invention can also be implemented during an awakening, falling asleep or waking phase of the person P. The brain waves can then differ from the slow brain waves.

To implement the brain wave stimulation operation, the control electronics 11 is, for example, able, from the measurement signal S, to first determine a temporal shape F of a cerebral slow wave C such that illustrated in Figure 6.

In a first embodiment, the temporal shape F is a series of points sampled amplitudes of the measurement signal values S, optionally pretreated as mentioned above, said series of measuring points being optionally interpolated or re ^¬ sampled.

In a second embodiment, the temporal form F is a series of amplitude values generated by a phase locked loop, or phase locked loop, (commonly referred to as PLL, acronym for the English term "Phase locked loop"). ").

The phase-locked loop is such that the instantaneous phase of the temporal form F at the output of said loop is slaved to the instantaneous phase of the measurement signal S.

The phase locked loop can be implemented by analog means or digital means. It is thus clear that the temporal form F is a representation of the brain wave C which can be obtained directly or can be obtained by a phase-locked loop which makes it possible to obtain a cleaner signal. In particular, the instantaneous phase of the temporal form F and the cerebral wave C are synchronized temporally. In the present description, therefore, the term "brain wave C" is used to mean the values taken by the temporal form F.

From this temporal form F, the control electronics 11 is able to determine at least a timing instant I of synchronization between a predefined temporal pattern Ml of cerebral slow wave C and a predefined temporal pattern M2 of the acoustic signal A.

Then, the control electronics 11 is able to control the acoustic transducer 10 so that the predefined temporal pattern M2 of the acoustic signal A is emitted at the timing instant I of synchronization.

The predefined temporal pattern Ml of cerebral slow wave C is therefore a pattern of amplitude and / or phase values of the temporal form F which represents the cerebral slow wave C. In particular, the predefined temporal pattern M1 may be a succession of phase values of the temporal form F and may therefore be in particular independent of the absolute value of the amplitude of the time form F.

The predefined temporal pattern M1 may also be a succession of relative values of the amplitude of the temporal form F. Said relative values are, for example, relating to a maximum amplitude of the predefined or stored temporal form F.

In one embodiment of the invention, the predefined temporal pattern M1 can thus for example correspond to a local temporal maximum of the cerebral slow wave C, a local temporal minimum of the cerebral slow wave C or else a predefined succession of at least a local temporal maximum and at least a local temporal minimum of the slow wave cerebral C.

The predefined temporal pattern M1 may also correspond to a portion of such a maximum, minimum or of such a succession, for example a rising edge, a falling edge or a plateau.

In the same manner, the predefined temporal pattern M2 of the acoustic signal may be a pattern of amplitude and / or phase values of the acoustic signal A.

In a first embodiment, the acoustic signal is for example an intermittent signal as illustrated in FIG. 6. This intermittent signal is for example emitted for a duration less than a period of a slow cerebral wave. The duration of the intermittent signal is for example less than a few seconds, preferably less than one second.

In an example given for purely indicative and non-limiting, the acoustic signal A is for example a type 1 / f pink noise pulse with a time duration of 50 to 100 milliseconds with a rise and fall time of a few milliseconds. Still in a nonlimiting manner and to fix the ideas, in this example the predefined temporal pattern Ml of cerebral slow wave C may for example correspond to a rising edge of a local maximum of the cerebral slow wave C. The temporal pattern predefined M2 of the acoustic signal A can then be for example a rising edge of the pink noise pulse. In this example, the timing instant I of synchronization between the predefined temporal pattern Ml of cerebral slow wave C and the predefined temporal pattern M2 of acoustic signal A can for example be defined so that the rising edge of the pink noise pulse A and the rising edge of the local maximum of the cerebral slow wave C is synchronized, that is to say concomitant.

In another embodiment, the acoustic signal A may be a continuous signal. The duration of the acoustic signal A may then in particular be greater than a period of the cerebral slow wave C. By "continuous signal" is meant in particular a signal of great duration in front of a period of the cerebral slow wave C.

In this embodiment, the acoustic signal A may be modulated temporally in amplitude, frequency or phase and the predefined temporal pattern M2 of the acoustic signal A may then be such a temporal modulation.

Alternatively, the continuous acoustic signal A may not be modulated temporally, for example in a manner that will now be described.

The device 1 may comprise at least two acoustic transducers 10, in particular a first acoustic transducer 10a and a second acoustic transducer 10b as illustrated in FIG. 3. The first acoustic transducer 10a is able to emit an acoustic signal A1 stimulating a right inner ear of the person P. The second acoustic transducer 10b is able to emit an acoustic signal A2 stimulating a left inner ear of the person P.

In particular, the first and second acoustic transducers 10a, 10b can be controlled in such a way that the acoustic signals A1 and A2 are binaural acoustic signals A. For this purpose, the acoustic signals A1 and A2 may for example be continuous signals. different frequencies. Such acoustic signals A1, A2 are known to generate intermittent pulses in the brain of the person P, in particular called binaural beats.

Still in a nonlimiting manner and to fix the ideas, in this example, the predefined temporal pattern M1 of cerebral slow wave C may, for example, again correspond to a rising edge of a local maximum of the cerebral slow wave C The predefined temporal patterns M2 of the acoustic signals A1, A2 can moreover be ranges of the acoustic signals A1, A2 corresponding temporally to said intermittent pulses generated in the brain of the person P. In this example, the time instant I of synchronization between the predefined temporal pattern M1 of cerebral slow wave C and the predefined temporal patterns M2 of the acoustic signals A1, A2 may for example be defined so that an intermittent pulse generated in the brain of the person P is synchronized temporally with the rising edge of the local maximum of the cerebral slow wave C.

FIG. 6 illustrates an example of predefined temporal patterns M1 and M2.

One and / or the other among a sound level, a duration, a spectrum and a temporal pattern M2 of the acoustic signal A can be predefined and stored in the memory 6 of the device 1.

Said one and / or other of a sound level, a duration, a spectrum and a temporal pattern M2 of the acoustic signal A can form operating parameters of the device 1.

As indicated above, said one and / or other of a sound level, a duration, a spectrum and a temporal pattern M2 of the acoustic signal A may vary over time, so that the emission of the acoustic signal A be variable over time.

Said one and / or other among a sound level, a duration, a spectrum and a temporal pattern M2 of the acoustic signal A may in particular vary according to an absolute time or a relative time as detailed above.

The acoustic signal A can thus be transmitted according to said operating parameters.

According to the embodiments and according to the selected time pattern Ml, different embodiments can be envisaged for determining the time instant I of synchronization.

Likewise, one and / or the other of a brain wave phase of the person and a predefined temporal wave pattern Ml of the person P can be predefined and stored in the memory 6 of the device 1.

Said one and / or another of a brain wave phase of the person and a predefined temporal wave pattern Ml of the person P can form operating parameters of the device 1.

Here again, said one and / or other of a brain wave phase of the person and a predefined temporal pattern of the brain wave Ml of the person P may vary over time, so that the synchronized transmission of the acoustic signal A is adjusted over time.

Said one and / or another of a brain wave phase of the person and a predefined temporal pattern of brain wave Ml of the person P can in particular vary according to an absolute time or a relative time such as detailed above.

The acoustic signal A can thus be emitted from to be synchronized according to said operating parameters.

Moreover, in order to determine the time instant I, the control electronics 11 can for example compare the amplitude values of the measurement signal S, possibly filtered and / or normalized, with an amplitude threshold.

In the example given above, which is purely nonlimiting, the predefined temporal pattern Ml of cerebral slow wave C corresponds to a rising edge of a local maximum of the cerebral slow wave C. A temporal instant I then corresponds to a time instant of exceeding the amplitude threshold, or a predefined duration immediately following such an overrun time. The control electronics 11 can thus control the acoustic transducer 4 so that the predefined temporal pattern M2 of the acoustic signal A is synchronized temporally with said time instant I.

It is well understood that the speed of communication between the acquisition means 3, the acoustic transducer 10 and the control electronics 11 makes it possible in particular to ensure reliable synchronization and optimal implementation of the stimulation operation.

In an embodiment in which the time form F is a series of amplitude values generated by a phase locked loop, it is possible to determine said time instant I from said phase locked loop, by threshold detection or by predicting future values of time form F.

In this embodiment, the temporal form F may in particular be less noisy than the measurement signal S and allow a facilitated determination of the time instant I of synchronization. In this way, it is thus easier to use the phase values of the temporal form F to identify the time instant I.

The device 1 may further comprise data transmission means 7 to the remote server 100. The data transmission means 7 may be mounted on the support element 2 in the manner described above for the acquisition means 3, the transmission means 4 and the analysis means 5. The data transmission means 7 can be controlled by an electronic device 1, for example the control electronics 11.

The data transmission means 7 may advantageously comprise a wireless communication module, for example a module implementing a protocol such as Bluetooth and / or Wi-Fi.

In this way, when the person P is in a sleep period, it is not hampered by cables, especially if it is necessary to perform data transmissions during the sleep period.

As illustrated in FIG. 3, the remote server 100 may also comprise data transmission means 110.

The data transmission means 7 of the device 1 and the data transmission means 110 of the remote server 100 are able to communicate with each other, directly (point-to-point communication) or via an extended network, for example the internet network.

More specifically, the data transmission means 7 of the device 1 and the data transmission means 110 of the remote server 100 are able to exchange data.

Thus, the data transmission means 7 of the device 1 may in particular be able to transfer the measurement signals S acquired by the acquisition means 3 the data transmission means 110 of the remote server 100. Such a transfer may in particular be implemented after a sleep period of the person P.

Similarly, the data transmission means 110 of the remote server 100 may in particular be able to transfer second operating parameters to the data transmission means 7 of the device 1.

In one embodiment of the invention, the remote server 100 may be able to communicate with a plurality of devices 1 respectively capable of being worn by a plurality of persons P.

In this embodiment, each device 1 of the plurality of devices 1 can transmit to the remote server 100 operating data comprising at least one measurement signal S acquired by the acquisition means 3 of said device 1.

The remote server 100 can thus receive a plurality of operating data respectively associated with the plurality of devices 1.

The operating data received by the remote server 100 of the device (s) 1 are associated with first operating parameters.

By "first operating parameters" is meant operating parameters used by a device to implement a stimulation step having made it possible to acquire the measurement signal S included in the operating data transmitted to the remote server 100. Operating parameters are, for example, operating parameters recorded in a device 1 during the manufacture of said device, or during a previous implementation of a personalization method according to the invention.

The remote server 100 may be able to communicate with a device 1 or with the plurality of devices 1 via an extended network 12, for example an internet network. The device or devices 1 can be directly connected to the wide area network 12, by their data transmission means 7, or be connected to said wide area network 12 via a mediation device, for example a base, a computer or a smartphone.

The remote server 100 also comprises processing means 120, able to perform a processing of the operating data to determine second operating parameters.

"Second operating parameters" means operating parameters determined by the processing means from the operating data. The second operating parameters may in particular be identical to the first operating parameters or different from the first operating parameters.

The processing means 120 may for example comprise one or more processors as well as one or more appropriate memories.

The processing means 120 are adapted and intended to implement a step of processing the operating data received from the device 1 during which second operating parameters are determined from an analysis of the operating data.

In an embodiment in which the remote server 100 receives a plurality of operating data associated with a plurality of devices 1, the processing step may include, in particular, analyzing said plurality of operating data so as to determine seconds. operating parameters to be transmitted and stored in the memory of at least one one of the plurality of devices 1.

In a first embodiment, the step of processing the operating data may comprise analyzing the measurement signal S by the processing means 120 to identify at least one parasitic frequency of the measurement signal S.

For this, the processing means 120 may in particular compute a harmonic spectrum of the measurement signal S and compare the amplitudes of one or more frequencies of said spectrum with average energy values, spectral amplitude values or energy thresholds or of maximum spectral amplitude, so as to detect too large spectral amplitudes.

Said energy values, average spectral amplitude or energy thresholds or maximum spectral amplitude can in particular be determined from a plurality of device 1, for example from a plurality of associated operating data. to a plurality of devices 1 as detailed above.

When at least one parasitic frequency has been identified in the measurement signal S, it is then possible to determine second operating parameters as a function of said parasitic frequency.

The second operating parameters are then transmitted and stored in the memory 6 of the device 1 from the remote server 100 during a transmission and storage step.

In a second embodiment, which can in particular be combined with the first embodiment described above, the step of processing the operating data can comprise the search in the measurement signal S of at least one temporal or frequency pattern indicator of an awakening or awakening of the person P, said temporal pattern temporally following the emission of an acoustic signal A.

By "said temporal pattern following the temporal emission of an acoustic signal", it is meant that the desired temporal pattern has been acquired by the acquisition means 3 after the emission of an acoustic signal A, within a certain time range. which can immediately follow the emission of the acoustic signal A or be delayed to take into account the biological reaction time of the person P to the acoustic signal A.

For this, the processing means 120 may implement a Fourier transform frequency analysis of at least a portion of the measurement signal S following the emission of an acoustic signal A, followed if necessary by the implementation. of a sleep depth detection algorithm. In this way, it is possible to detect whether the emission of the acoustic signal has generated an awakening or a start of awakening of the person P.

From this analysis, the processing means 120 can determine a wakeup indicator under the effect of the stimulation.

If said wake-up indicator indicates that the emission of the acoustic signal has generated an awakening or awakening of the person P, the second operating parameters can be determined so as to prevent a future occurrence of this situation.

For this purpose, the processing means 120 may, for example, determine second operating parameters more particularly including the operating parameters used by the analysis means 5 detailed above.

The second operating parameters can thus comprise one and / or the other among at least one threshold predefined power of a spectrum of the measurement signal S, at least one predefined time-frequency threshold of a predefined pattern identified in the measurement signal in the measurement signal, or at least one predefined level threshold of Muscle activity as detailed above.

Preferably, said thresholds of the second operating parameters are below the thresholds of the first operating parameters of the device 1 so as to prevent an awakening or awakening of the person P during subsequent stimulation.

In a third embodiment, which can in particular be combined with the first and second embodiments described above, the step of processing the operating data can comprise the comparison of a portion of the measurement signal acquired after the transmitting an acoustic signal A with a reference portion of the measurement signal, so as to determine an impact indicator of the stimulation.

Here also, by "a portion of the measurement signal acquired after the emission of an acoustic signal A" is meant that said portion of the measurement signal has been acquired by the acquisition means 3 after the transmission of a acoustic signal A, within a certain time range that can immediately follow the emission of the acoustic signal A or be delayed to take into account the biological reaction time of the person P to the acoustic signal A.

Said time range may for example extend over a period of a few seconds after the acoustic signal A. "A reference portion of the measurement signal" means a portion of the measurement signal preceding the emission of any acoustic signal A, or a portion of the measurement signal sufficiently spaced apart from the transmission of any acoustic signal A so that the person P is no longer considered to be influenced by the emission of an acoustic signal A. Such a reference portion may be an average made between several portions of the measurement signal, in particular between several portions of the measurement signal preceding each the emission of an acoustic signal A.

The processing means 120 may, for example, determine a difference between the averages of the portion of the measurement signal acquired after the emission of an acoustic signal A and the reference portion of the measurement signal, and an indicator of the impact of the measurement signal. stimulation can be determined from said difference.

Moreover, as detailed above, the processing means 120 may implement a Fourier transform frequency analysis of the portion of the measurement signal S following the emission of an acoustic signal A, so as to detect if emission of the acoustic signal has generated an awakening or awakening of the person P and determine a wakeup indicator under the effect of stimulation.

A composite effect indicator of the stimulation can then be determined from the stimulation impact indicator and the wake up indicator under the effect of the stimulation.

The second operating parameters can then be determined from the composite effect indicator of the stimulation. For this purpose, the processing means 120 may, for example, determine second operating parameters more particularly including the operating parameters used by the transmission means 4 detailed above.

The second operating parameters may thus comprise one and / or the other among at least one sound level, a duration, a spectrum or a temporal pattern M2 of the acoustic signal A, or at least one of a phase of brain wave of the person and a predefined temporal pattern of brain wave Ml as detailed above.

In one embodiment of the invention, the second operating parameters are determined by implementation, by the processing means 120, of an automatic classification algorithm on the operating data.

Said automatic classification algorithm is for example defined during a preliminary automatic learning step. As indicated above, the automatic learning step may comprise a transfer learning operation.

By "automatic classification algorithm" is meant an algorithm adapted to automatically classify the operating data transmitted by the device 1, that is to say to associate a class from qualitative or quantitative rules characterizing the operating data. By "automatic classification algorithm" is also broadly meant regression algorithms capable of associating a class which is a real value to the operating data transmitted by the device 1.

Said class associated with the operating data may be selected from a class database, or may be an interpolated value from a class database.

A "class" can thus be for example an identifier, for example an alphanumeric identifier, or a numerical value, in particular an integer or real value. In the case where the class is a real value, we speak of a regression algorithm.

Said algorithm can be implemented directly on the operating data itself or the operating data can be pre-processed before implementation of the operating algorithm, by filtering or otherwise.

Such parameters are, for example, the weights of certain neurons, of all the neurons, or even connections between the neurons, for an algorithm implementing a neural network. The parameters of the automatic classification algorithm can for example be predefined during a supervised automatic learning step, or more or less automatically determined, for example by the implementation of a semi-automatic learning step. supervised, partially supervised, unsupervised or reinforced. As indicated above, the automatic learning step may comprise a transfer learning operation.

The class database may also be predefined during such a learning step.

The second operating parameters can then be determined from the class obtained.

Said class can directly provide a second operating parameter value or can be used to determine one or more values of second operating parameters, for example by comparison with a database of values of second operating parameters and / or interpolation between values of second operating parameters predefined in such a database.

Thus, the obtained class can make it possible to select a user behavior under the effect of the stimulation among several user behaviors under the effect of the stimulation recorded in a database and associated in said database with seconds. predefined operating parameters.

For this purpose, the processing means 120 may, for example, determine second operating parameters comprising more particularly the parameters of operation used by the transmission means 4 detailed above.

The second operating parameters can thus comprise one and / or the other among at least one sound level, a duration, a spectrum or a temporal pattern M2 of the acoustic signal A, and / or at least one of a brain wave phase of the person and a predefined temporal pattern of brain wave Ml, and / or at least one parameter of an automatic classification algorithm and / or a class database of an automatic classification algorithm set implemented by means of analysis 5 of a device 1, as detailed above.

In one embodiment of the invention in which the second operating parameters comprise at least one parameter of an automatic classification algorithm and / or a class database of an automatic classification algorithm implemented by means 5 of a device 1, the remote server 100 can implement an algorithm as detailed above.

The remote server 100 can firstly implement an associated automatic classification algorithm, in particular similar, to said automatic classification algorithm implemented by analysis means 5 of the device 1.

However, the automatic classification algorithm implemented by the remote server 100 can be applied to much larger input data than the measurement data on which the associated automatic classification algorithm implemented by the device is applied. 1.

Indeed, the associated automatic classification algorithm implemented by the device 1 operates in real time, that is to say that it has access at any given time only recorded measurement data previous instant. On the other hand, the automatic classification algorithm implemented by the remote server 100 operates off-real-time ("offline") and the input data on which said algorithm is applied can thus comprise, for each instant, data measurement recorded by a device 1 before and after said instant.

Moreover, the remote server 100 can receive measurement signals S acquired by a device during several periods of operation of the device, for example during several sleep periods of the person P. The input data on which is applied the algorithm implemented by the remote server 100 may thus comprise, for each moment, measurement data recorded during different periods of operation of the device 1.

It will therefore be understood that the algorithm implemented by the remote server 100, although similar to the classification algorithm implemented by the analysis means 5 of the device 1, may make it possible to obtain more precise results.

The algorithm implemented by the remote server 100 can thus be used to label the measurement data received from the device 1, that is to say to determine the expected output values of the classification algorithm implemented on the device. device 1 for the different input measurement data.

Once the measurement data have been labeled, the remote server 100 can determine an updated classification algorithm for the device 1.

For this, the remote server 100 can implement implement an automatic learning operation of the automatic classification algorithm of the device 1, in particular reinforcement learning, from the labeled measurement data. The automatic learning operation can be implemented from an initial state constituted by the automatic classification algorithm currently implemented on the device 1.

The second operating parameters can then be determined from the updated classification algorithm.

Once the second operating parameters transmitted and stored in the memory 6 of the device 1 from the remote server 100 during the transmission and storage step, the device 1 can then implement a second step of acoustic wave stimulation. in which at least one of the acquisition sub-steps a), analysis b) and emission c) is performed as a function of the second operating parameters.

By "second stimulation step" is meant here a stimulation step implemented after transmission from the remote server second operating parameters. It will be understood that, when a plurality of stimulation steps has been implemented before the processing step, on the remote server, operating data, said "second stimulation step" may not be the second setting. implementation of a stimulation step but an implementation subsequent.

Claims

A method of acoustically stimulating personalized brainwaves of a person (P) comprising:

o a first step of acoustic stimulation of the brain waves of a person (P) implemented by a device (1) for acoustic stimulation of brain waves adapted to be worn by the person (P), the device (1) for stimulation comprising a memory storing first operating parameters, the initial stimulation step comprising the sub-steps:

a) acquisition of at least one measurement signal (S) representative of a physiological signal of the person,

b) analyzing the measurement signal to estimate whether the person is in a state of fitness for stimulation, and c) if it is judged that the person is in a state of fitness for stimulation, emission an acoustic signal (A), audible by the person, and synchronized with a predetermined temporal pattern of the brain wave (Ml) of the person,

wherein at least one of the acquisition sub-steps a), analysis b) and transmission c) is performed as a function of the first operating parameters;

o a step of transmitting operating data to a remote server from the device, said operating data comprising at least said measurement signal (S);

a step of transmitting and storing second operating parameters from the server remote in the device memory;

a second step of acoustic stimulation of the brain waves implemented by the device (1) in which the memory of the device (1) stores the second operating parameters, and in which at least one of the acquisition sub-steps a), analysis b) and emission c) is performed according to the second operating parameters.

Method according to claim 1, wherein the operating parameters comprise at least one parasitic frequency of the measurement signal (S),

and wherein the acquisition substep a) comprises frequency filtering said parasitic frequency into the measurement signal (S).

3. Method according to any one of claims 1 and 2, wherein the operating parameters comprise at least one energy threshold of a spectrum of the measurement signal (S),

and wherein the analysis sub-step b) comprises comparing an energy of a spectrum of the measurement signal (S) with said threshold.

The method according to any one of claims 1 to 3, wherein the operating parameters comprise at least one time frequency threshold of a predefined pattern of the measurement signal,

and wherein the analysis sub-step b) comprises identifying said predefined pattern in the measurement signal and comparing a frequency of said pattern in the measurement signal with said threshold.

The method according to any one of claims 1 to 4, wherein the operating parameters comprise at least a muscle activity level threshold,

and wherein the analysis sub-step b) comprises determining a level of muscle activity from the measurement signal (S) and comparing said level of muscle activity with said threshold.

6. Method according to any one of claims 1 to 5, wherein the substep of analysis b) is implemented at least in part by an algorithm for automatic classification of measurement data determined from the measurement signal. (S),

and wherein the operating parameters comprise at least one parameter of said automatic classification algorithm and / or at least one class database of said automatic classification algorithm.

The method according to any one of claims 1 to 6, wherein the operating parameters comprise at least one parameter selected from a list comprising a sound level, a duration, a spectrum and a temporal pattern of an acoustic signal,

and wherein the acoustic signal (A) transmitted during the transmitting sub-step c) is transmitted according to said parameter.

The method of any one of claims 1 to 7, wherein the operating parameters comprise at least one parameter selected from a list comprising a brain wave phase of the person and a predefined temporal pattern of brain wave (Ml) of the person,

and wherein the acoustic signal (A) transmitted during the transmitting sub-step c) is transmitted so as to be synchronized according to said parameter.

The method according to any one of claims 1 to 8, wherein the processing step comprises analyzing the measurement signal on the remote server to identify at least one parasitic frequency of the measurement signal (S),

and wherein the second operating parameters are determined as a function of said parasitic frequency.

The method according to any one of claims 1 to 9, wherein the processing step includes searching the measurement signal for at least one predefined pattern indicative of an alarm clock or awakening start. the person and following the emission of an acoustic signal (A), so as to determine an awakening indicator under the effect of stimulation,

and wherein the second operating parameters are determined according to said wake up indicator.

The method according to any of claims 1 to 10, wherein the processing step comprises comparing a portion of the measurement signal acquired after the emission of an acoustic signal (A) with a reference portion. of the measurement signal, so as to determine an indicator of the impact of the stimulation,

and in which the second parameters of are determined according to the impact indicator.

12. The method of claim 10, wherein the determination of the second operating parameters according to the impact indicator of the stimulation comprises the implementation of an automatic classification algorithm, said automatic classification algorithm being preferably defined during a preliminary automatic learning step.

The method of any of claims 1 to 12, wherein the first step of acoustically stimulating brain waves of a person (P) is performed a plurality of times by a plurality of respective devices (1), respectively adapted to be worn by a plurality of persons (P) respectively,

wherein the step of transmitting operating data to the remote server is implemented a plurality of times from said plurality of respective devices, respectively for respectively transmitting a plurality of respective operating data, respectively comprising at least one measurement signal (S) of each of said respective devices,

and wherein the processing step comprises analyzing said plurality of operating data so as to determine second operating parameters to be transmitted and stored in the memory of at least one of the plurality of devices (D ·

14. Process according to any one of claims 1 to 13, wherein the first stimulation step is repeated a plurality of times by a device (1) during a period of operation of said device,

and wherein the step of transmitting operating data to a remote server from the device is performed after said period of operation, the operation data including at least one measurement signal (S) for each of the reiterations of the first stimulation stage.

15. The method of claim 14, wherein the operating period of the device extends over a period of several hours, preferably at least eight hours.

The method according to any one of claims 1 to 15, wherein, transmitting an acoustic signal (A) synchronized with a predetermined temporal pattern of brain wave (Ml) comprises:

the determination, from the measurement signal (S), of a temporal waveform of the brain (F),

determining, from said cerebral waveform time form (F), at least one timing instant of synchronization (I) between the predefined brainwave time pattern (M1) and a predefined temporal pattern of the acoustic signal ( M2), and

controlling an acoustic transducer (4) of the device so that the predefined temporal pattern of the acoustic signal (M2) is transmitted at said timing instant of synchronization (I).

17. Process according to any one of Claims 1 to 16, wherein the brain wave is a cerebral slow wave having a frequency of less than 5 Hz and greater than 0.3 Hz.

The method of any one of claims 1 to 17, wherein the acoustic signal (A) is an intermittent signal and wherein a duration of the acoustic signal is less than a period of a brain wave, preferably less than a few seconds, preferably less than one second.

The method of any one of claims 1 to 17, wherein the acoustic signal (A) is a continuous signal and wherein a duration of the acoustic signal is greater than a period of a brain wave.

The method according to any one of claims 1 to 19, wherein the predefined temporal pattern of brain wave (Ml) corresponds to a local temporal maximum of a brain wave, a local temporal minimum of a brain, a forehead. amount or a falling edge of a local maximum or minimum of a brain wave, a predefined succession of at least one local temporal maximum and at least a local temporal minimum of a brain wave or a rising or falling edge such a succession.

21. Personalized acoustic stimulation system for the brain waves of a person (P), the system comprising a device for acoustic stimulation of the brain waves of a person (P) and a remote server, in which the device (1) comprises: a memory (6) able to store operating parameters comprising at least one of first operating parameters and second operating parameters,

acquisition means (3) of at least one measurement signal (S) representative of a physiological signal of the person (P),

means for analyzing (5) the measurement signal to estimate whether the person is in a state of aptitude for stimulation, and

transmission means (4) designed to emit an acoustic signal (A), audible by the person, and synchronized with a predetermined temporal wave pattern (Ml) of the person if it is judged that the person is in a state of aptitude for stimulation,

in which at least one of the acquisition means (3), the analysis means (5) and the transmission means (4) are operable according to the operating parameters stored in the memory (6). )

transmitting operating data from the device to the remote server, said operating data comprising at least said measurement signal (S), and