CN114288520A - Sleep assisting method, device, equipment and storage medium based on brain waves - Google Patents
Sleep assisting method, device, equipment and storage medium based on brain waves Download PDFInfo
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Abstract
The invention discloses a sleep assisting method, device, equipment and storage medium based on brain waves. Acquiring electroencephalogram data of a user in a sleep state, extracting rhythm information of the electroencephalogram data from the electroencephalogram data, converting the electroencephalogram data into audio matched with the rhythm of the electroencephalogram data based on the rhythm information of the electroencephalogram data, and playing the audio to the user. The audio is converted based on the brain wave data acquired by the user in the normal sleep state and has the rhythm same as the rhythm of the brain wave in the normal sleep state, so that resonance is more easily formed with the brain wave of the user under the induction of the audio to guide the user to enter the sleep state, thereby providing a personalized sleep assisting scheme for the user and improving the sleep quality.
Description
Technical Field
The embodiment of the invention relates to the technical field of sleep assistance, in particular to a sleep assistance method, device, equipment and storage medium based on brain waves.
Background
Sleep has the functions of maintaining the survival of individuals, promoting growth and development, forming memory and the like, and is an important process in physiological activities.
With the accelerated pace of life, negative emotions such as work, emotional stress and the like cause many people to suffer from insomnia. According to statistics, the incidence rate of insomnia of Chinese adults is as high as 38.2%, and more than 3 hundred million Chinese people have sleep disorder. Therefore, the sleep state of the human body is improved through technical means, and the method has important social value.
Disclosure of Invention
The invention provides a sleep assisting method, device, equipment and storage medium based on brain waves, which are used for improving the sleep quality of a user.
In a first aspect, an embodiment of the present invention provides a sleep assisting method based on brain waves, including:
acquiring electroencephalogram data of a user in a sleep state;
extracting rhythm information of the electroencephalogram data from the electroencephalogram data;
converting the brain wave data into audio matched with the rhythm of the brain wave data based on the rhythm information of the brain wave data;
playing the audio to the user.
In a second aspect, an embodiment of the present invention further provides a sleep assisting apparatus based on brain waves, including:
the data acquisition module is used for acquiring electroencephalogram data of a user in a sleep state;
the rhythm information extraction module is used for extracting rhythm information of the electroencephalogram data from the electroencephalogram data;
the audio conversion module is used for converting the brain wave data into audio matched with the rhythm of the brain wave data based on the rhythm information of the brain wave data;
and the audio playing module is used for playing the audio to the user.
In a third aspect, an embodiment of the present invention further provides a computer device, including:
one or more processors;
storage means for storing one or more programs;
when the one or more programs are executed by the one or more processors, the one or more processors implement the brain wave-based assisted sleep method as provided by the first aspect of the present invention.
In a fourth aspect, embodiments of the present invention also provide a computer-readable storage medium, on which a computer program is stored, which when executed by a processor, implements the brain wave-based assisted sleep method as provided in the first aspect of the present invention.
The sleep assisting method based on the brain waves provided by the embodiment of the invention comprises the following steps: acquiring electroencephalogram data of a user in a sleep state, extracting rhythm information of the electroencephalogram data from the electroencephalogram data, converting the electroencephalogram data into audio matched with the rhythm of the electroencephalogram data based on the rhythm information of the electroencephalogram data, and playing the audio to the user. The audio is converted based on the brain wave data acquired by the user in the normal sleep state and has the rhythm same as the rhythm of the brain wave in the normal sleep state, so that resonance is more easily formed with the brain wave of the user under the induction of the audio to guide the user to enter the sleep state, thereby providing a personalized sleep assisting scheme for the user and improving the sleep quality.
Drawings
Fig. 1 is a flowchart illustrating a method for assisting sleep based on brain waves according to an embodiment of the present invention;
fig. 2A is a flowchart of a method for assisting sleep based on brain waves according to a second embodiment of the present invention;
FIG. 2B is a table showing the relationship between the amplitude intervals and the pattern shapes according to the embodiment of the present invention;
fig. 3 is a schematic structural diagram of a sleep assisting apparatus based on brain waves according to a third embodiment of the present invention;
fig. 4 is a schematic structural diagram of a computer device according to a fourth embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the invention and are not limiting of the invention. It should be further noted that, for the convenience of description, only some of the structures related to the present invention are shown in the drawings, not all of the structures.
Example one
Fig. 1 is a flowchart of a method for assisting sleep based on brain waves according to an embodiment of the present invention, where the method is implemented by an apparatus for assisting sleep based on brain waves according to an embodiment of the present invention, and the apparatus may be implemented by software and/or hardware, and is generally configured in a computer device, as shown in fig. 1, and the method specifically includes the following steps:
s101, acquiring electroencephalogram data of a user in a sleep state.
Electroencephalography (EEG) is a method of recording brain activity using electrophysiological markers, in which post-synaptic potentials generated in synchronization with a large number of neurons are summed up during brain activity. It records the electrical wave changes during brain activity, which is a general reflection of the electrophysiological activity of brain neurons on the surface of the cerebral cortex or scalp. According to the acquisition mode of electroencephalogram data, the electroencephalogram data can be divided into self-generating electroencephalogram signals and induced electroencephalogram signals, wherein the self-generating electroencephalogram signals refer to continuous and rhythmic potential changes generated by neurons under the condition of no external stimulation. The frequency range of the spontaneous electroencephalogram signals is generally 0-49Hz, and the spontaneous electroencephalogram signals can be divided into the following 5 frequency bands according to different frequencies:
delta (Delta) wave with frequency range of 0.5-4Hz is obvious in forehead, and mostly appears in deep sleep, deep anesthesia and unconsciousness of infants or adults, and the Delta wave activity can not be monitored by the adults in the waking period.
Theta (Theta) waves have a frequency range of 4-8Hz, and are not common in the brain waves of normal adults, and generally appear in large quantities when people have low mood or depression.
Alpha (Alpha) wave, with a frequency range of 8-13Hz, is the most common wave band in adult electroencephalogram signals, and is considered as the basic rhythm of normal brain wave, reflecting the important rhythm wave of each index of brain.
Beta (Beta) waves, with frequencies ranging from 13 to 30Hz, appear in excited, stressed or excited states in humans, and have been shown to be more prevalent in women than in men, and have been shown to be related to the level of attention in the awake state of humans.
Gamma waves, with frequencies in the range of 30-49Hz, have been shown to appear more when a person is concentrating, and thus, Gamma waves are associated with attention mechanisms and conscious activity.
The brain wave data is a data sequence acquired from brain wave signals at a certain acquisition frequency. In the embodiment of the invention, the spontaneous electroencephalogram signals of the user in the normal sleep state can be collected in advance to serve as electroencephalogram data, and the electroencephalogram signals in the sleep state mainly comprise Delta (Delta) waves.
And S102, extracting rhythm information of the brain wave data from the brain wave data.
In a sleeping state, waveforms in brain waves appear periodically, and the periodically appearing activities are rhythms. In the embodiment of the invention, the rhythm information of the brain wave data is extracted from the brain wave data by analyzing the brain wave data. For example, in the embodiment of the present invention, the waves that occur periodically are referred to as electroencephalogram events, and the rhythm information of the brain wave data may include periods, frequencies, amplitudes, and the like of the electroencephalogram events, which is not limited herein.
And S103, converting the electroencephalogram data into audio matched with the rhythm of the electroencephalogram data based on the rhythm information of the electroencephalogram data.
A fractal system has Scale invariance, i.e. scaleless (Scale-free), also known as Scale properties (ScalingProperties). A physical quantity, if of a scaled nature, can be described by its oscillation amplitude using a power function, with x (t) at time t and a scaling factor α, expressed as:
X(t)∝t-α
this Law is commonly referred to as the Power Law, namely Power Law. Power Law is seen everywhere in nature, and the existence of the Law can be found in phenomena of tidal billowing, seismic wave conduction and the like. For human beings themselves, various phenomena in the physiological aspects as well as the psychological aspects of the individual, as well as in social activities, follow this law.
The brain wave signal and the music signal are both non-standard in nature, so that the brain wave data collected by the user in the sleeping state can be converted into audio matched with the rhythm of the brain wave data.
And S104, playing audio to the user.
After the brain wave data is converted into audio, the audio is played to the user. Because the audio is converted based on the brain wave data collected by the user in the normal sleep state and has the same rhythm as the brain wave in the normal sleep state, the user can more easily enter the sleep state under the induction of the audio, thereby providing a personalized sleep assisting scheme for the user.
The sleep assisting method based on the brain waves provided by the embodiment of the invention comprises the following steps: acquiring electroencephalogram data of a user in a sleep state, extracting rhythm information of the electroencephalogram data from the electroencephalogram data, converting the electroencephalogram data into audio matched with the rhythm of the electroencephalogram data based on the rhythm information of the electroencephalogram data, and playing the audio to the user. The audio is converted based on the brain wave data acquired by the user in the normal sleep state and has the rhythm same as the rhythm of the brain wave in the normal sleep state, so that resonance is more easily formed with the brain wave of the user under the induction of the audio to guide the user to enter the sleep state, thereby providing a personalized sleep assisting scheme for the user and improving the sleep quality.
Example two
Fig. 2A is a flowchart of a method for assisting sleep based on brain waves according to a second embodiment of the present invention, which is further detailed based on the first embodiment, and describes in detail the specific processes of the steps in the method for assisting sleep based on brain waves, as shown in fig. 2A, the method includes:
s201, acquiring electroencephalogram data of a user in a sleep state.
In the embodiment of the invention, the spontaneous electroencephalogram signals of the user in the normal sleep state can be collected in advance to be used as electroencephalogram data.
S202, determining each electroencephalogram event from the electroencephalogram data, wherein one electroencephalogram event is a data set of electroencephalogram data passing through points of a central axis of the electroencephalogram data twice continuously.
When each wave in the electroencephalogram data is shifted up and down, the waves are shifted according to the central point of the wave, and the central points of the continuous electroencephalograms are connected to form an approximate straight line, wherein the straight line is called as a central axis and is also called as a baseline in the field of electroencephalograms.
In the embodiment of the invention, a data set between points of the electroencephalogram data which pass through the central axis twice in succession is called an electroencephalogram event. Illustratively, the brain wave data passes through the central axis at point a, and then passes through the central axis again at point B, and the data set between A, B in the brain wave data is one brain wave event. Illustratively, a point passing through the central axis (i.e., a zero-crossing point) may be detected from the brain wave signal by a zero-crossing point detection algorithm.
In the embodiment of the invention, the brain wave data is obtained by sampling brain wave signals, and the sampling rate is assumed to be N Hz, that is, the sampling times per second are N times, and N sampling points are obtained per second. That is, in the embodiment of the present invention, electroencephalogram data is a point set composed of sampling points. Each data point represents the acquisition time (abscissa) of the point and the potential (ordinate) of the brain electrical activity at that acquisition time. For example, for a set of points, brain wave data may be traversed to find a target pair of sample points comprising two adjacent sample points, one of the sample points in the target pair having a value greater than or equal to 0 and the other sample point having a value less than 0. I.e. the zero crossing is one of the target sample point pair or the zero crossing is between the target sample point pair.
Illustratively, in order to save computing resources and improve computing efficiency, in the embodiment of the present invention, brain wave data is traversed, a sample point whose potential is greater than or equal to 0 is marked as 1, and a sample point whose potential is less than 0 is marked as 0, so as to obtain a set of tag arrays whose numerical values are 0 or 1. Then, two sampling points corresponding to two adjacent target marks are found from the mark array as a target sampling point pair, wherein one of the two adjacent target marks is marked as 1, and the other one is marked as 0.
And then, taking one of the target sampling points in the target sampling point pair as the starting point of the electroencephalogram event, taking one of the next target sampling point in the target sampling point pair as the end point of the electroencephalogram event, and obtaining a point set, namely the electroencephalogram event. Thus, all the electroencephalogram events in the electroencephalogram data can be determined by repeating the above process.
S203, determining the total number of notes in the audio based on the total number of the electroencephalogram events, wherein each electroencephalogram event corresponds to one note.
After all brain electrical events in the brain electrical data are determined, the total number of notes in the audio is determined based on the total number of brain electrical events. Illustratively, in the embodiment of the present invention, one electroencephalogram event corresponds to one note according to the time sequence of the electroencephalogram event.
S204, extracting waveform parameters of the brain electrical events from the brain electrical events, wherein the rhythm information of the brain electrical wave data comprises the total number of the brain electrical events and the waveform parameters of the brain electrical events.
The waveform parameters of the electroencephalogram event may include a period, an amplitude, a frequency, a sampling rate, an average energy, and the like, and the embodiment of the present invention is not limited herein.
In a specific embodiment of the present invention, the waveform parameters of the brain electrical event include a sampling rate of the brain wave data, an amplitude of the brain electrical event, and an average energy of the brain electrical event. The process of determining the waveform parameters of a brain electrical event is as follows:
1. and determining the sampling rate of the brain wave data based on the number of sampling points acquired per second in the brain wave data.
The number of sampling points acquired in each second in the electroencephalogram data is the sampling rate of the electroencephalogram data.
2. Traversing all sampling points in the electroencephalogram event, and subtracting the minimum amplitude from the maximum amplitude (namely potential) to obtain the amplitude of the electroencephalogram event.
Traversing all sampling points in the electroencephalogram event to obtain a sampling point with the maximum amplitude value (called as the maximum sampling point) and a sampling point with the minimum amplitude value (called as the minimum sampling point) in the electroencephalogram event, and subtracting the amplitude value of the minimum sampling point from the amplitude value of the maximum sampling point to obtain the amplitude value of the electroencephalogram event. Thus, the amplitude of each brain electrical event can be obtained by repeating the above process.
3. And calculating the mean value of the square sum of the amplitudes of all the sampling points in the electroencephalogram event to obtain the average energy of the electroencephalogram event.
The electroencephalogram event is a set comprising a plurality of sampling points, and the average energy of the set is calculated, namely the average value of the square sum of the amplitudes of all the sampling points in the electroencephalogram event is calculated. Thus, the average energy of each brain electrical event can be obtained by repeating the above process.
S205, determining note parameters of notes corresponding to the electroencephalogram events based on the waveform parameters of the electroencephalogram events.
As described above, in the embodiment of the present invention, one electroencephalogram event corresponds to one note according to the time sequence of the electroencephalogram event. In order to convert the brain wave data into an audio frequency matched with the rhythm of the brain wave data, it is necessary to establish a mapping relationship between a waveform parameter of the brain wave event and a note parameter of a note corresponding to the brain wave event, thereby determining the note parameter of the note corresponding to the brain wave event.
Illustratively, in particular embodiments of the present invention, the note parameters of the note include duration, pitch, and intensity. Illustratively, the process of determining the note parameters of a note is as follows:
1. and counting the number of sampling points in the electroencephalogram event.
In the embodiment of the invention, the number of sampling points in the electroencephalogram event is counted aiming at each electroencephalogram event. Thus, the number of sampling points in each electroencephalogram event is obtained.
2. And dividing the number of the sampling points in the electroencephalogram event by the sampling rate of the electroencephalogram data to obtain the duration of the musical notes corresponding to the electroencephalogram event.
Illustratively, for each electroencephalogram event, the number of sampling points in the electroencephalogram event is divided by the sampling rate of the electroencephalogram data to obtain the duration of the note corresponding to the electroencephalogram event. Thus, the duration of the note corresponding to each electroencephalogram event is obtained.
3. Mapping the amplitude of the electroencephalogram event according to a preset first mapping relation to obtain the pitch of the corresponding note, wherein the first mapping relation is as follows:
where, pit (i) is the pitch of the ith note, amp (i) the amplitude of the ith electroencephalogram event, and f (i) is the frequency of the ith note, round () means rounding up the value of the ith note, where α is 1.50 and β is 0.48, and where f (i) is the reciprocal of the duration.
Thus, the pitch of the note corresponding to each electroencephalogram event is obtained.
3. Mapping the average energy of the electroencephalogram event according to a preset second mapping relation to obtain the sound intensity of the corresponding note, wherein the second mapping relation is as follows:
T＝16·lg(apd(i))+64
wherein, vol (i) is the intensity of the ith note, and apd (i) is the average energy of the ith electroencephalogram event.
Thus, the sound intensity of the note corresponding to each electroencephalogram event is obtained.
And S206, generating audio matched with the rhythm of the brain wave data based on the note parameters of the notes.
In the embodiment of the present invention, after the note parameters of the notes are obtained, the audio matched with the rhythm of the brain wave data is generated according to the timing sequence of the notes and the note parameters.
Illustratively, each note is stored in the MIDI format according to the time sequence of the electroencephalogram event, resulting in a MIDI file. MIDI is an abbreviation for Musical Instrument Digital Interface, meaning the Musical Instrument Digital Interface, which is a standard protocol for exchanging Musical information between music synthesizers, electronic Musical instruments, and computers. A MIDI file is a data file, similar to a DOC file, that is MIDI data and commands. These data and commands are transmitted between different instruments and can be controlled with each other. The format of the MIDI file is designed to run in the order of time codes. The time code enables the sequencer to faithfully reproduce a desired musical melody.
When the audio needs to be generated, the audio generation program is called to read and analyze the MIDI file, and the audio matched with the rhythm of the electroencephalogram data is generated. For example, some open source methods for generating music by using MIDI may be called, for example, by using a miditoobox tool of matlab, writing MIDI information into a matrix according to an input rule required by the miditoobox, calling a related function, and generating a brain wave audio in the MIDI format.
And S207, converting the brain wave data into a video based on the rhythm information of the brain wave data, wherein the video comprises a plurality of patterns matched with the rhythm of the brain wave data.
In an embodiment of the present invention, brain wave data is converted into a video based on rhythm information of the brain wave data, the video including a plurality of different patterns matching a rhythm of the brain wave data. Illustratively, in an embodiment of the present invention, brain wave data is converted into a pattern including a rhythm transformation according to the brain wave data, forming a video, according to the rhythm transformation of the brain wave data. For example, in the embodiment of the present invention, the shape, color, size, and the like of the pattern are not limited. Illustratively, the video conversion process is as follows:
1. and segmenting the brain wave data into a plurality of segments with preset duration.
In the embodiment of the invention, the acquired electroencephalogram data are uniformly segmented into a plurality of segments with preset duration. Illustratively, the brain wave data is taken once every 100 milliseconds, each time considering a signal 100 milliseconds before this time, i.e., a segment length of 100 milliseconds. Each segment corresponds to a pattern in the video.
2. Segment parameters of the segments are determined.
In an embodiment of the present invention, for each segment, segment parameters of the segment are calculated. Illustratively, the segment parameters include the number, average amplitude and mean energy of the brain electrical events in the segment, the average amplitude is the mean of the amplitudes of all the brain electrical events in the segment, and the mean energy is the mean of the average energies of all the brain electrical events in the segment.
3. And determining the pattern parameters of the pattern corresponding to the segment based on the segment parameters of the segment.
In the embodiment of the present invention, in order to convert the segment into the pattern matching the rhythm of the segment, it is necessary to establish a mapping relationship between the segment parameters of the segment and the pattern parameters of the pattern corresponding to the segment, thereby determining the pattern parameters of the pattern corresponding to the segment. Illustratively, in embodiments of the present invention, the pattern parameters include pattern shape, pixel area, and pattern color. Illustratively, the process of determining the pattern parameters of the pattern corresponding to the segment is as follows:
1. the maximum value of the amplitude of all brain electrical events in the brain electrical data is determined.
Specifically, the calculation process of the amplitude of the electroencephalogram event is recorded in the foregoing, and the embodiment of the present invention is not described herein again. Next, a maximum value is determined from the amplitudes of all brain electrical events.
2. The section formed by the amplitude of zero and the maximum value is equally divided into a plurality of amplitude sections, and each amplitude section corresponds to one pattern shape.
In the embodiment of the invention, after the maximum value of the amplitudes of all the electroencephalogram events is obtained, the interval formed by the amplitude being zero and the maximum value is divided into a plurality of amplitude intervals, and each amplitude interval corresponds to one pattern shape.
Illustratively, in embodiments of the invention, the maximum of the amplitudes of all brain electrical events is kmaxFrom 0 to kmaxThe formed intervals are evenly divided into 5 amplitude intervals which are respectively marked as x1, x2, x3, x4 and x5 from small to large.
3. And determining the pattern shape of the pattern corresponding to the segment based on the amplitude interval to which the average amplitude belongs.
In the embodiment of the present invention, the pattern shape of the pattern corresponding to the segment is determined based on the amplitude interval to which the average amplitude belongs, and for example, if the average amplitude of a certain segment belongs to a certain amplitude interval, the pattern shape of the pattern corresponding to the determined segment is the pattern shape corresponding to the amplitude interval. Fig. 2B is a table of correspondence between amplitude intervals and pattern shapes according to an embodiment of the present invention, for example, as shown in fig. 2B, when the average amplitude of a certain segment belongs to the amplitude interval x2, the pattern shape of the pattern corresponding to the certain segment is characterized by being regular and relatively rounded, for example, rounded rectangles.
4. And determining the maximum value of the average energy of all brain electrical events in the brain electrical wave data.
After the average energy of all the electroencephalogram events in the electroencephalogram data is obtained, the maximum value of the average energy of all the electroencephalogram events is determined. The calculation process of the average energy is described in detail in the foregoing embodiments, and the embodiments of the present invention are not described herein again.
5. And establishing a third mapping relation between the average energy and the pixel area based on the maximum value of the average energy of all the electroencephalogram events.
Illustratively, in the embodiment of the present invention, the maximum value apd of the average energy of all the brain electrical events is setmaxFrom 0 to apdmaxThe value of (d) and the area of 0 to 160000 pixels establish a uniform mapping relationship.
6. And determining the pixel area of the pattern corresponding to the segment according to the mapping relation.
Illustratively, the mean energy is substituted into the mapping relationship according to the mean energy of the brain electrical events in the segment from 0 to apdmaxThe pixel area of the pattern corresponding to the segment can be known.
7. And establishing a fourth mapping relation between the number of the electroencephalogram events and the pattern color based on the maximum value of the number of the electroencephalogram events in all the segments.
For example, in the embodiment of the present invention, the number of electroencephalogram events in each segment is determined first, and the process of determining the number of electroencephalogram events is described in detail in the foregoing embodiment, which is not described herein again. And (3) setting the maximum value of the number of the electroencephalogram events in all the segments as M, and establishing a uniform mapping relation between the numerical values from 0 to M and the color spaces from 0 to 255. Illustratively, the number of brain electrical events in each segment is normalized based on the maximum value M, i.e., the number of brain electrical events in each segment is normalized by dividing the maximum value M.
8. And substituting the number of the electroencephalogram events in the segments into the fourth mapping relation to determine the color of the pattern corresponding to the segments.
In an embodiment of the present invention, the pattern is taken as a gray scale image as an example, the normalized specific numerical value is used as a percentage, the percentage is multiplied by 255, and then the integer is taken to obtain the gray scale value of the pattern.
And S208, playing the audio and the video to the user.
After the brain wave data is converted into audio and video, the audio and video are synchronously played to a user. Because the audio and the video are converted based on the brain wave data collected by the user in the normal sleep state and have the same rhythm as the brain wave in the normal sleep state, the user can more easily enter the sleep state under the induction of the audio and the video, thereby providing a personalized sleep assisting scheme for the user. In addition, the user can be guided to generate corresponding sleep brain waves from multiple aspects through the bimodal sleep-aiding scheme of hearing and vision, so that the user is guided to enter a sleep state, and the sleep quality is improved.
The sleep assisting method based on the brain waves determines the total number of notes in audio based on the total number of the brain electrical events in brain wave data, converts the brain electrical events into rhythm-matched notes based on waveform parameters of the brain electrical events, divides the brain electrical wave data into a plurality of segments with preset duration, and determines pattern parameters of patterns corresponding to the segments based on the segment parameters of the segments. Because the audio and the video are converted based on the brain wave data collected by the user in the normal sleep state and have the same rhythm as the brain wave in the normal sleep state, the audio and the video are more easily resonated with the brain wave of the user under the induction of the audio and the video to guide the user to enter the sleep state, thereby providing a personalized sleep assisting scheme for the user and improving the sleep quality. In addition, the user can be guided to generate corresponding sleep brain waves from multiple aspects through the bimodal sleep-aiding scheme of hearing and vision, so that the user is guided to enter a sleep state, and the sleep quality is improved.
EXAMPLE III
Fig. 3 is a schematic structural diagram of a sleep assisting apparatus based on brain waves according to a third embodiment of the present invention, as shown in fig. 3, the apparatus includes:
the data acquisition module 301 is used for acquiring electroencephalogram data of a user in a sleep state;
a rhythm information extraction module 302, configured to extract rhythm information of the electroencephalogram data from the electroencephalogram data;
an audio conversion module 303, configured to convert the brain wave data into an audio matched with a rhythm of the brain wave data based on rhythm information of the brain wave data;
an audio playing module 304, configured to play the audio to the user.
In some embodiments of the present invention, rhythm information extraction module 302 includes:
the event determining submodule is used for determining each electroencephalogram event from the electroencephalogram data, and one electroencephalogram event is a data set between points of the electroencephalogram data which continuously pass through the central axis of the electroencephalogram data for two times;
and the waveform parameter extraction submodule is used for extracting the waveform parameters of the electroencephalogram events from the electroencephalogram events, and the rhythm information of the electroencephalogram data comprises the total number of the electroencephalogram events and the waveform parameters of the electroencephalogram events.
In some embodiments of the invention, the event determination sub-module comprises:
the target sampling point pair searching unit is used for traversing the brain wave data and finding a target sampling point pair comprising two adjacent sampling points, wherein the numerical value of one sampling point in the target sampling point pair is greater than or equal to 0, and the numerical value of the other sampling point in the target sampling point pair is less than 0;
and the event determining unit is used for taking one of the target sampling point pairs as the starting point of the electroencephalogram event and taking one of the next target sampling point pair as the end point of the electroencephalogram event.
In some embodiments of the present invention, the target sampling point pair searching unit comprises:
the marking subunit is used for traversing the brain wave data, marking the sampling points with the numerical values greater than or equal to 0 as 1, and marking the sampling points with the numerical values less than 0 as 0 to obtain a group of marking arrays with the numerical values of 0 or 1;
and the target sampling point pair determining subunit is used for finding two sampling points corresponding to two adjacent target marks from the mark array as a target sampling point pair, wherein one of the two adjacent target marks is marked as 1, and the other one of the two adjacent target marks is marked as 0.
In some embodiments of the present invention, the waveform parameters of the brain electrical event include a sampling rate of the brain wave data, an amplitude of the brain electrical event, and an average energy of the brain electrical event, and the waveform parameter extraction sub-module includes:
the sampling rate determining unit is used for determining the sampling rate of the brain wave data based on the number of sampling points acquired per second in the brain wave data;
the amplitude calculation unit is used for traversing all sampling points in the electroencephalogram event and subtracting the minimum amplitude from the maximum amplitude to obtain the amplitude of the electroencephalogram event;
and the average energy calculating unit is used for calculating the average value of the square sum of the amplitudes of all the sampling points in the electroencephalogram event to obtain the average energy of the electroencephalogram event.
In some embodiments of the present invention, the audio conversion module 303 comprises:
a note determination submodule, configured to determine the total number of notes in the audio based on the total number of electroencephalogram events, where each electroencephalogram event corresponds to one note;
the note parameter determining submodule is used for determining note parameters of notes corresponding to the electroencephalogram events based on the waveform parameters of the electroencephalogram events;
and the audio generation sub-module is used for generating audio matched with the rhythm of the electroencephalogram data based on the note parameters of the notes.
In some embodiments of the present invention, the note parameters of the note include duration, pitch, and intensity, and the note parameter determination submodule includes:
the sampling point quantity counting unit is used for counting the quantity of the sampling points in the electroencephalogram event;
the sound length calculating unit is used for dividing the number of the sampling points in the electroencephalogram event by the sampling rate of the electroencephalogram data to obtain the sound length of the musical notes corresponding to the electroencephalogram event;
the first mapping unit is configured to map the amplitude of the electroencephalogram event according to a preset first mapping relationship to obtain a pitch of a corresponding note, where the first mapping relationship is:
where, pit (i) is the pitch of the ith note, amp (i) the amplitude of the ith electroencephalogram event, f (i) is the frequency of the ith note, round () means rounding up the value therein, α is 1.50, β is 0.48;
the second mapping unit is configured to map the average energy of the electroencephalogram event according to a preset second mapping relationship to obtain the sound intensity of the corresponding note, where the second mapping relationship is:
T＝16·lg(apd(i))+64
wherein, vol (i) is the intensity of the ith note, and apd (i) is the average energy of the ith electroencephalogram event.
In some embodiments of the invention, the audio generation sub-module comprises:
the storage unit is used for storing each note as a digital interface MIDI format of the music equipment according to the time sequence of the electroencephalogram event to obtain a digital interface MIDI file of the music equipment;
and the calling unit is used for calling an audio generation program to read and analyze the MIDI file of the digital interface of the music equipment and generate audio matched with the rhythm of the electroencephalogram data.
In some embodiments of the present invention, the brain wave-based sleep assisting apparatus further includes:
the video conversion module is used for extracting rhythm information of the brain wave data from the brain wave data and then converting the brain wave data into a video based on the rhythm information of the brain wave data, wherein the video comprises a plurality of patterns matched with the rhythm of the brain wave data;
and the video playing module is used for playing the video to the user.
In some embodiments of the invention, the video conversion module comprises:
the segment segmentation submodule is used for segmenting the brain wave data into a plurality of segments with preset duration;
the segment parameter determining submodule is used for determining segment parameters of the segments, wherein the segment parameters comprise the number, the average amplitude and the mean energy of the electroencephalogram events in the segments, the average amplitude is the mean value of the amplitudes of all the electroencephalogram events in the segments, and the mean energy is the mean value of the mean energy of all the electroencephalogram events in the segments;
and the pattern parameter determining submodule is used for determining the pattern parameters of the patterns corresponding to the segments based on the segment parameters of the segments.
In some embodiments of the invention, the pattern parameters include pattern shape, pixel area and pattern color, and the pattern parameter determination submodule includes:
an amplitude maximum value determination unit for determining the maximum value of the amplitudes of all the brain electrical events in the brain wave data;
an amplitude interval dividing unit for equally dividing an interval in which the amplitude is zero and the amplitude is maximum into a plurality of amplitude intervals, each of the amplitude intervals corresponding to one pattern shape;
a pattern shape determining unit configured to determine a pattern shape of a pattern corresponding to the segment based on an amplitude section to which the average amplitude belongs;
the average energy maximum value determining unit is used for determining the maximum value of the average energy of all brain electrical events in the brain wave data;
the third mapping relation establishing unit is used for establishing a third mapping relation between the average energy and the pixel area based on the maximum value of the average energy of all the electroencephalogram events;
a pixel area determining unit, configured to substitute the mean energy into the third mapping relationship, and determine a pixel area of a pattern corresponding to the segment;
a fourth mapping relation establishing unit, configured to establish a fourth mapping relation between the number of the electroencephalogram events and the pattern color based on a maximum value of the number of the electroencephalogram events in all the segments;
and the pattern color determining unit is used for substituting the number of the electroencephalogram events in the segments into the fourth mapping relation by the pattern to determine the pattern color of the pattern corresponding to the segments.
The device for assisting sleep based on brain waves can execute the method for assisting sleep based on brain waves provided by any embodiment of the invention, and has the corresponding functional modules and beneficial effects for executing the method for assisting sleep based on brain waves.
Example four
A fourth embodiment of the present invention provides a computer device, and fig. 4 is a schematic structural diagram of the computer device provided in the fourth embodiment of the present invention, as shown in fig. 4, the computer device includes:
a processor 401, a memory 402, a communication module 403, an input device 404, and an output device 405; the number of the processors 401 in the mobile terminal may be one or more, and one processor 401 is taken as an example in fig. 4; the processor 401, the memory 402, the communication module 403, the input device 404 and the output device 405 in the mobile terminal may be connected by a bus or other means, and fig. 4 illustrates an example of a connection by a bus. The processor 401, memory 402, communication module 403, input device 404, and output device 405 described above may be integrated on a computer device.
The memory 402, which is a computer-readable storage medium, may be used to store software programs, computer-executable programs, and modules, such as the modules corresponding to the brain wave-based assisted sleep method in the above-described embodiments. The processor 401 executes various functional applications and data processing of the computer device by running software programs, instructions and modules stored in the memory 402, that is, implements the above-described brain wave-based sleep aid method.
The memory 402 may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application program required for at least one function; the storage data area may store data created according to use of the microcomputer, and the like. Further, the memory 402 may include high speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other non-volatile solid state storage device. In some examples, the memory 402 may further include memory located remotely from the processor 401, which may be connected to an electronic device through a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
And a communication module 403, configured to establish a connection with an external device (e.g., an intelligent terminal), and implement data interaction with the external device. The input device 404 may be used to receive input numeric or character information and generate key signal inputs related to user settings and function controls of the computer apparatus.
The computer device provided by the embodiment can execute the brain wave-based sleep assisting method provided by any of the above embodiments of the invention, and has corresponding functions and beneficial effects.
EXAMPLE five
An embodiment five of the present invention provides a storage medium containing computer-executable instructions, on which a computer program is stored, the program, when executed by a processor, implementing a brain wave-based assisted sleep method according to any of the above embodiments of the present invention, the method including:
acquiring electroencephalogram data of a user in a sleep state;
extracting rhythm information of the electroencephalogram data from the electroencephalogram data;
converting the brain wave data into audio matched with the rhythm of the brain wave data based on the rhythm information of the brain wave data;
playing the audio to the user.
It should be noted that, as for the apparatus, the device and the storage medium embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and in relevant places, reference may be made to the partial description of the method embodiments.
From the above description of the embodiments, it is obvious for those skilled in the art that the present invention can be implemented by software and necessary general hardware, and certainly, can also be implemented by hardware, but the former is a better embodiment in many cases. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which may be stored in a computer-readable storage medium, such as a floppy disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a FLASH Memory (FLASH), a hard disk or an optical disk of a computer, and includes instructions for enabling a computer device (which may be a robot, a personal computer, a server, or a network device) to execute the sleep assisting method for brain waves according to any embodiment of the present invention.
It should be noted that, in the above apparatus, each of the modules, sub-modules, units, and sub-units included in the apparatus is merely divided according to functional logic, but is not limited to the above division as long as the corresponding function can be implemented; in addition, the specific names of the functional modules are only for convenience of distinguishing from each other and are not used for limiting the protection scope of the present invention.
It should be understood that portions of the present invention may be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, the various steps or methods may be implemented in software or firmware stored in memory and executed by suitable instruction execution devices. For example, if implemented in hardware, as in another embodiment, any one or combination of the following techniques, which are known in the art, may be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application specific integrated circuit having an appropriate combinational logic gate circuit, a Programmable Gate Array (PGA), a Field Programmable Gate Array (FPGA), or the like.
In the description herein, references to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
It is to be noted that the foregoing is only illustrative of the preferred embodiments of the present invention and the technical principles employed. It will be understood by those skilled in the art that the present invention is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the invention. Therefore, although the present invention has been described in greater detail by the above embodiments, the present invention is not limited to the above embodiments, and may include other equivalent embodiments without departing from the spirit of the present invention, and the scope of the present invention is determined by the scope of the appended claims.
Claims (14)
1. A sleep assisting method based on brain waves is characterized by comprising the following steps:
acquiring electroencephalogram data of a user in a sleep state;
extracting rhythm information of the electroencephalogram data from the electroencephalogram data;
converting the brain wave data into audio matched with the rhythm of the brain wave data based on the rhythm information of the brain wave data;
playing the audio to the user.
2. The brain wave-based sleep assisting method according to claim 1, wherein extracting rhythm information of the brain wave data from the brain wave data includes:
determining each electroencephalogram event from the electroencephalogram data, wherein one electroencephalogram event is a data set between points of the electroencephalogram data which continuously pass through the central axis of the electroencephalogram data twice;
extracting waveform parameters of the brain electrical events from the brain electrical events, wherein the rhythm information of the brain electrical wave data comprises the total number of the brain electrical events and the waveform parameters of the brain electrical events.
3. The brain wave-based sleep aiding method according to claim 2, wherein determining each brain electrical event from the brain electrical wave data comprises:
traversing the brain wave data, finding out a target sampling point pair comprising two adjacent sampling points, wherein the numerical value of one sampling point in the target sampling point pair is more than or equal to 0, and the numerical value of the other sampling point in the target sampling point pair is less than 0;
and taking one of the target sampling point pairs as the starting point of the electroencephalogram event, and taking one of the next target sampling point pair as the end point of the electroencephalogram event.
4. The brain wave-based assisted sleep method according to claim 3, wherein traversing the brain wave data to find a target sampling point pair comprising two adjacent sampling points comprises:
traversing the electroencephalogram data, marking the sampling points with the numerical values greater than or equal to 0 as 1, and marking the sampling points with the numerical values less than 0 as 0 to obtain a group of marking arrays with the numerical values of 0 or 1;
and finding two sampling points corresponding to two adjacent target marks from the mark array as a target sampling point pair, wherein one of the two adjacent target marks is marked as 1, and the other one of the two adjacent target marks is marked as 0.
5. The brain wave-based sleep aiding method according to any one of claims 2 to 4, wherein the waveform parameters of the brain electrical event include a sampling rate of the brain wave data, an amplitude of the brain electrical event and an average energy of the brain electrical event, and the extracting the waveform parameters of the brain electrical event from the brain electrical event includes:
determining the sampling rate of the brain wave data based on the number of sampling points acquired per second in the brain wave data;
traversing all sampling points in the electroencephalogram event, and subtracting the minimum amplitude from the maximum amplitude to obtain the amplitude of the electroencephalogram event;
and calculating the mean value of the square sum of the amplitudes of all the sampling points in the electroencephalogram event to obtain the average energy of the electroencephalogram event.
6. The brain wave-based sleep assisting method according to claim 5, wherein converting the brain wave data into audio matched with a rhythm of the brain wave data based on rhythm information of the brain wave data includes:
determining the total number of notes in the audio based on the total number of the electroencephalogram events, wherein each electroencephalogram event corresponds to one note;
determining note parameters of notes corresponding to the electroencephalogram events based on the waveform parameters of the electroencephalogram events;
and generating audio matched with the rhythm of the brain wave data based on the note parameters of the notes.
7. The brain wave-based sleep assisting method according to claim 6, wherein the note parameters of the note include duration, pitch and intensity, and the determining the note parameters of the note corresponding to the brain electrical event based on the waveform parameters of the brain electrical event comprises:
counting the number of the sampling points in the electroencephalogram event;
dividing the number of the sampling points in the electroencephalogram event by the sampling rate of the electroencephalogram data to obtain the sound length of the musical notes corresponding to the electroencephalogram event;
mapping the amplitude of the electroencephalogram event according to a preset first mapping relation to obtain the pitch of the corresponding note, wherein the first mapping relation is as follows:
where, pit (i) is the pitch of the ith note, amp (i) the amplitude of the ith electroencephalogram event, f (i) is the frequency of the ith note, round () means rounding up the value therein, α is 1.50, β is 0.48;
mapping the average energy of the electroencephalogram event according to a preset second mapping relation to obtain the sound intensity of the corresponding note, wherein the second mapping relation is as follows:
T＝16·lg(apd(i))+64
wherein, vol (i) is the intensity of the ith note, and apd (i) is the average energy of the ith electroencephalogram event.
8. The brain wave-based sleep assisting method according to claim 6 or 7, wherein generating audio matched with the rhythm of the brain wave data based on the note parameter of each note comprises:
storing each note as a digital interface MIDI format of the music equipment according to the time sequence of the electroencephalogram event to obtain a digital interface MIDI file of the music equipment;
and calling an audio generation program to read and analyze the MIDI file of the digital interface of the music equipment and generate audio matched with the rhythm of the electroencephalogram data.
9. The brain wave-based sleep aiding method according to any one of claims 1 to 4, 6 and 7, further comprising, after extracting rhythm information of the brain wave data from the brain wave data:
converting the brain wave data into a video based on rhythm information of the brain wave data, the video including a plurality of patterns matching a rhythm of the brain wave data;
and playing the video to the user.
10. The brain wave-based sleep assisting method according to claim 9, wherein converting the brain wave data into a video based on rhythm information of the brain wave data includes:
segmenting the brain wave data into a plurality of segments with preset duration;
determining segment parameters of the segments, wherein the segment parameters comprise the number, average amplitude and mean energy of the electroencephalogram events in the segments, the average amplitude is the mean value of the amplitudes of all the electroencephalogram events in the segments, and the mean energy is the mean value of the mean energy of all the electroencephalogram events in the segments;
determining the pattern parameters of the pattern corresponding to the segment based on the segment parameters of the segment.
11. The brain wave-based assisted sleep method according to claim 10, wherein the pattern parameters include a pattern shape, a pixel area, and a pattern color, and the determining the pattern parameters of the pattern corresponding to the segment based on the segment parameters of the segment includes:
determining the maximum value of the amplitudes of all electroencephalogram events in the electroencephalogram data;
equally dividing the interval formed by the amplitude being zero and the maximum value into a plurality of amplitude intervals, wherein each amplitude interval corresponds to one pattern shape;
determining the pattern shape of the pattern corresponding to the segment based on the amplitude interval to which the average amplitude belongs;
determining the maximum value of the average energy of all brain electrical events in the brain electrical wave data;
establishing a third mapping relation between the average energy and the pixel area based on the maximum value of the average energy of all the electroencephalogram events;
substituting the average value energy into the third mapping relation to determine the pixel area of the pattern corresponding to the segment;
establishing a fourth mapping relation between the number of the electroencephalogram events and the pattern color based on the maximum value of the number of the electroencephalogram events in all the segments;
and substituting the number of the electroencephalogram events in the segments into the fourth mapping relation to determine the pattern color of the pattern corresponding to the segments.
12. A sleep-assisting device based on brain waves, comprising:
the data acquisition module is used for acquiring electroencephalogram data of a user in a sleep state;
the rhythm information extraction module is used for extracting rhythm information of the electroencephalogram data from the electroencephalogram data;
the audio conversion module is used for converting the brain wave data into audio matched with the rhythm of the brain wave data based on the rhythm information of the brain wave data;
and the audio playing module is used for playing the audio to the user.
13. A computer device, comprising:
one or more processors;
storage means for storing one or more programs;
when executed by the one or more processors, cause the one or more processors to implement the brain wave-based assisted sleep method of any one of claims 1-11.
14. A computer-readable storage medium on which a computer program is stored, the program, when being executed by a processor, implementing the brain wave-based assisted sleep method according to any one of claims 1 to 11.
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