EP1988965A1 - Vorrichtung und verfahren zur echtzeit-ansteuerung eineas effektors - Google Patents
Vorrichtung und verfahren zur echtzeit-ansteuerung eineas effektorsInfo
- Publication number
- EP1988965A1 EP1988965A1 EP07704536A EP07704536A EP1988965A1 EP 1988965 A1 EP1988965 A1 EP 1988965A1 EP 07704536 A EP07704536 A EP 07704536A EP 07704536 A EP07704536 A EP 07704536A EP 1988965 A1 EP1988965 A1 EP 1988965A1
- Authority
- EP
- European Patent Office
- Prior art keywords
- training
- signals
- signal
- effector
- input signals
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Withdrawn
Links
Classifications
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61F—FILTERS IMPLANTABLE INTO BLOOD VESSELS; PROSTHESES; DEVICES PROVIDING PATENCY TO, OR PREVENTING COLLAPSING OF, TUBULAR STRUCTURES OF THE BODY, e.g. STENTS; ORTHOPAEDIC, NURSING OR CONTRACEPTIVE DEVICES; FOMENTATION; TREATMENT OR PROTECTION OF EYES OR EARS; BANDAGES, DRESSINGS OR ABSORBENT PADS; FIRST-AID KITS
- A61F2/00—Filters implantable into blood vessels; Prostheses, i.e. artificial substitutes or replacements for parts of the body; Appliances for connecting them with the body; Devices providing patency to, or preventing collapsing of, tubular structures of the body, e.g. stents
- A61F2/50—Prostheses not implantable in the body
- A61F2/68—Operating or control means
- A61F2/70—Operating or control means electrical
- A61F2/72—Bioelectric control, e.g. myoelectric
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61N—ELECTROTHERAPY; MAGNETOTHERAPY; RADIATION THERAPY; ULTRASOUND THERAPY
- A61N1/00—Electrotherapy; Circuits therefor
- A61N1/18—Applying electric currents by contact electrodes
- A61N1/32—Applying electric currents by contact electrodes alternating or intermittent currents
- A61N1/36—Applying electric currents by contact electrodes alternating or intermittent currents for stimulation
- A61N1/36003—Applying electric currents by contact electrodes alternating or intermittent currents for stimulation of motor muscles, e.g. for walking assistance
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61N—ELECTROTHERAPY; MAGNETOTHERAPY; RADIATION THERAPY; ULTRASOUND THERAPY
- A61N1/00—Electrotherapy; Circuits therefor
- A61N1/18—Applying electric currents by contact electrodes
- A61N1/32—Applying electric currents by contact electrodes alternating or intermittent currents
- A61N1/36—Applying electric currents by contact electrodes alternating or intermittent currents for stimulation
- A61N1/36014—External stimulators, e.g. with patch electrodes
- A61N1/36025—External stimulators, e.g. with patch electrodes for treating a mental or cerebral condition
Definitions
- the invention relates to a device and a method for real-time control of an effector.
- a particularly serious case is the so-called locked-in patients who have been deprived of any voluntary movement due to complete paralysis of skeletal muscle (such as amyotrophic lateral sclerosis (ALS) or muscle soreness) or a stroke in the brainstem area.
- skeletal muscle such as amyotrophic lateral sclerosis (ALS) or muscle soreness
- ALS amyotrophic lateral sclerosis
- muscle soreness a stroke in the brainstem area.
- ALS amyotrophic lateral sclerosis
- Paralysis or disability prevents these patients from performing intended movements. Movements here and in the following should be understood to mean not only a movement in the sense of running, but any movement of muscles such as an arm movement, but also facial expressions or speech. Therefore, an attempt is made to restore or improve the ability to move by deliberately controlling a prosthesis by means of its own brain signals.
- the core of such a neuroprosthesis is a so-called Brain Machine Interface (BMI). Their function is to translate the neural activity of the
- a conventional BMI system is based on electrodes that are placed upside down and record an electroencephalogram (EEG). Then the patient is taught to intensify or mitigate certain aspects of recorded brain activity by volitional effort.
- EEG electroencephalogram
- SPCs are slow voltage changes over the entire cortex at intervals of one-half to ten seconds. Negative SCPs are typically associated with movement or other causes of cortical activation, while positive SCPs are associated with decreased cortical activity. A patient can learn to consciously raise and lower the SCPs in a training that typically spans several weeks and months. This information, which can also be discriminated from the outside via the EEG, can then be used for a simple cursor control after completion of the training phase The disadvantages are a long and strenuous training with subsequently only a very low transmission bandwidth: in the duration of an SPC in the In the order of several seconds, just one bit can be transmitted, and this type of communication is tiring and tedious for the patient.
- P300 Rare or particularly significant stimuli interspersed with common stimuli, which may be auditory, visual, or somatosensory, induce an activity peak (typically in the EEG above the parietal cortex) with a time delay of about 300 ms versus the stimulus. If you show the patient a selection of letters, for example, and let them flash one after the other, the P300 is strongest for the desired letter. The process can be speeded up a bit by showing letters in rows and columns, with the letter resulting from the strongest column and the strongest row. Disadvantage of this method is that you can only select from predetermined possibilities, and that a whole series of presentations with high attention to the selection of only one letter is used.
- C) Mu and Beta Rhythm The 8-12 Hz Mu and 13-30 Hz beta activity is found more frequently over the somatosensory and motor cortex. Movement or exercise preparation is usually associated with a reduction of mu and beta rhythms, while after a movement or relaxation usually a rhythm enhancement occurs. Once again, after several weeks of training, subjects were shown to be able to control the strength of these rhythms, most recently independently for Mu and Beta, allowing two-dimensional cursor control. This is an improvement over the SPCs achieved, but this is only gradual and does not eliminate the disadvantages. In addition, movements can affect the mu rhythms and thus reduce the accuracy of the control. All these methods have in common that a voluntary achievement of the corresponding brain activities in training as application requires a high concentration and is very tiring for the patient.
- the patient often has in addition to his paralysis with serious other health problems to fight. From a healing in the sense of a casual dealing with other things using a prosthesis, which replaces the lost mobility without effort, so there can be no question.
- the detection of brain activity requires a relatively large amount of time and can only transmit little information (typically between 1-20 bits per minute). This creates a BMI with a very low information transmission bandwidth. Complex and even simple movements can not be controlled in this way. Everyday activities like writing a simple letter cost hours, if not days of extreme concentration.
- the inventive solution is initially based on the knowledge that conventional BMI systems use any or arbitrarily trained signal that has nothing to do with the original biological signal for the function to be performed. As a result, training often takes a long time and control of complex natural movements is prevented or prevented is at least very difficult. Therefore, the invention is based on the principle of exploiting the patient's natural patterns of activity, as he has already learned before losing his own ability to move.
- "natural neural information processing processes” are to be understood as meaning those which directly represent the central nervous processing of movement-relevant information, as is also the case in healthy people, and which therefore do not require any training.
- Delimitations are "artificial neuronal processes” that need to be learned specifically for the control of the effector, or that may correspond to a natural brain activity, but this activity was originally unrelated to the movement to be learned before training.
- An advantage of the invention is to enable the translation of the natural signals of a movement into a corresponding movement of the prosthesis by means of a direct, easily trainable access to the desired control possibilities in their entire natural complexity and diversity. Training is directly related to the control of movement in the usual way, and is therefore motivating, purposeful, not quickly tiring and generally easier, as the activity patterns learned from childhood can be used. In the application, the patient is therefore not remembered with every information to be transmitted to the artificial and laboriously learned.
- the data processing system has a receiving unit for the input signals and a drive unit for supplying the drive signals to the effector.
- the data processing system can then communicate with the components for information input and output via these additional units.
- the Ableitelektrode for the determination of the electromagnetic input signals from neuronal population activity is formed, in particular an LFP (local field potential) -, ECoG (electrocorticography) - or EEG (electroencephalography) electrode.
- LFP local field potential
- ECoG electrospray imaging
- EEG electroencephalography
- the Ableitelektrode is non-invasive, the Ableitelektrode is placed in operation outside on the head surface, under the skin, under the skull on the brain tissue or inserted within a sulcus without injuring brain tissue.
- invasive procedures can reach single neurons (SUA) or neuronal groups (LFP). This, however, is paid for by an injury, the consequences of which are often unforeseeable. Because of this and because of the risks of infection and surgery, the use of invasive electrodes is limited.
- the discharge electrode determines during operation movement-related input signals from brain areas responsible for movements, in particular information processes during the presentation, planning, execution or control of a movement in the motor cortex.
- other input signals can usefully control an effector, such as visual signals.
- motion-related input signals particularly those of the motor-controlled brain, provide the most suitable input for motion control.
- one pass of a standard movement forms one
- Training step and during each training step, the input signals are recorded as training data.
- a standard movement (presented, presented or executed) can thus form the basis of a movement database for which the neural activity is stored. This can be used not only to directly improve the determination of the predictive model, but also much later for recalibration, such as when new, ie at the time of training still unknown, prediction developed or existing methods are further developed.
- a training step is a demonstration, introduction, and / or execution of the standard movement. These three steps, as explained in more detail below, allow an accurate and targeted training. It also exploits the plasticity of the brain, because not only the evaluation unit improves its prediction accuracy, but also the patient improves his idea of the intended movement by means of feedback from the executed movement of his prosthesis. As a result, intentional and actual movement rapidly converge on both sides.
- the condition of the patient is categorized and stored during each training step, and this condition influences the weighting of the respective training data in the determination of the predictive model, in particular health, attention or fatigue.
- the inventors were able to show that the relevant activity is dependent on such state parameters. Therefore, if taken into account by the predictive model, it may become more accurate and correctly classify systemic deviations depending on the condition of the patient.
- external circumstances are categorized and stored during each training step, and this condition affects the weighting of the training respective training data in the determination of the predictive model, in particular the position of the patient - lying, sitting, standing, the lighting conditions or the environment.
- These external circumstances also affect the relevant activity, so that the predictive model becomes more accurate if it takes it into account.
- a portion of the training data remains disregarded or erased in response to a non-corrective signal, and this non-corrective signal is, in particular, a predetermined standard movement. If the patient sees that his intention has been misapplied, he must have a chance to correct the appropriate training data. Particularly easy he succeeds in this, if it is possible on a - certainly learned - standard movement, because he is then not dependent on a distracting and thus disturbing other input method during training, which are in extreme cases because of his paralysis not available like.
- the predictive model uses the standard motions as interpolation points in the composition of new motions. It will rarely be possible to train all possible movements. By means of an interpolation, the entire range of possible movements can be exploited with as few standard movements as possible to be trained.
- the standard motion is converted into a representation of a frequency and phase modulation of the time-frequency resolved bands of the input signals.
- the effector is a prosthesis, a patient's own body part or a data processing system.
- the drive signals have a destination, which are converted by the effector into an action or movement including intermediate destinations not contained in the drive signals. The patient then does not have to drive all intermediate stations during an intended movement, which would also make the correct prediction difficult. Simple physical laws often determine the more accurate motion, and these details can be determined by the evaluation unit or electronics in the effector, independent of the prediction.
- an amplifier between the Ableitelektrode and the receiving unit is provided, which filters the input signals and / or amplified.
- the neural signals which derive the electrodes often require treatment before they can be started with their evaluation. This in turn increases the prediction accuracy.
- FIG. 1 is a schematic overview of the invention
- Figure 2 is a plan view of the carrier surface with a plurality of individual electrodes of a preferred embodiment of the discharge electrode.
- FIG. 3 is a schematic representation of the signal transmission interface for an embodiment of the invention.
- FIG. 4 shows neuronal activity signals for a movement in three different states of the subject;
- FIG. 4 shows neuronal activity signals for a movement in three different states of the subject;
- Fig. 5 decoding probabilities for three different variants to take into account the state of the subject in the prediction model
- FIG. 6 shows an exemplary schematic representation for the conversion of neuronal signals / data into control signals for an effector with the aid of a predictive model
- FIG. 7 shows an illustration of an arm prosthesis as an example of an effector, which can be actuated by an embodiment of the invention
- a lead-out electrode 1 for deriving the neuronal activity of the cerebral cortex (cerebral cortex) 2 of a human brain is inserted under the skull of a patient or, in alternative embodiments, placed under the scalp or on the head surface.
- the lead-out electrode 1 measures the neuronal activity and forwards it via an Si 1 Sg 1 nal tenal tenal tenal tenal tenal tenal tenase 3 as electromagnetic input signals to an amplifier 4, which is preferably designed as a multi-channel amplifier.
- the amplifier amplifies and filters the electromagnetic input signals of the lead-out electrode 1 with high temporal resolution and forwards the thus preprocessed signals in real time to an evaluation chip, a computer or the like system 5 Signal processing continues. There, the signals are classified in real time using a predictive model and generated corresponding control signals.
- the action intention from the activity signals is determined here for certain, trained arbitrary movements of the patient, which the patient can no longer perform himself.
- an effector 6 is activated.
- This can be a classic prosthesis, ie a mechanical replica of a body part, but also its own body parts. It is also conceivable a virtual command such as the to a computer cursor or a menu selection.
- the effector 6 may return effector condition signals to the system 5 to allow feedback of the effector control.
- the discharge electrode 1 is formed in the practical application as a multi-electrode.
- the electrical voltage is recorded.
- Invasive procedures include single unit activity (SUA), where the lead-in electrode 1 is brought into or even enters the vicinity of a single neuron, as well as the local field potential (LFP) determination, where the lead-off electrostatic field - de 1 measures the electric potential field from its environment that is determined by the neighboring neurons.
- SAA single unit activity
- LFP local field potential
- the invasive methods allow to reach deeper layers of the brain and to obtain more accurate measurement data (spatially resolved). However, this is achieved by partially destroying the brain tissue with often unpredictable consequences for the patient.
- the lead-out electrode 1 is a thin foil electrode, as shown in FIG. 2, which is implanted subdurally or epidurally over one or more selected areas of the cerebrum 2. It is alternatively conceivable to accommodate the discharge electrode 1 outside the skull. When used over multiple areas, a corresponding plurality of diverting electrodes 1 designed as described herein are used.
- the lead-out electrode 1 is inserted in a groove (sulcus) of the brain. This also allows areas of the brain to be reached that are not on the outer surface without injuring brain tissue. This arrangement is explained in detail in the introductory said sister application.
- the deflection electrode 1 has a carrier Ia made of flexible or elastic material.
- a material offer themselves polyimide or silicone because of their compatibility or biocompatibility, easy processability and insensitivity. Equally, however, any other material is suitable which has the required flexibility and biocompatibility, ie which does not affect the brain tissue even with long-term use.
- the material should not be conductive. It should allow in a simple way to give the wearer its individual shape, so for example easy to cut to size.
- the wearer must be elastic and thin enough, usually with a thickness. In order not to injure tissue, the wearer has rounded edges.
- the carrier Ia On the carrier are a number of electrodes Ic, which are each connected individually to a cable Ib, can be passed through the signals to the outside.
- the carrier Ia is shown in a rough approximation rectangular. In the application, it will often be advantageous to adapt it to the area to be derived.
- the electrodes Ic are arranged in matrix form as contact points.
- Conductor tracks Ie inside the carrier Ib connect each electrode Ic individually and without overlapping their respective interconnects Ie with the cable Ib for the signal exchange.
- the production possibilities of such printed conductors Ie and possibilities of their arrangement are known to the person skilled in the art.
- Electrodes can be made of different materials, in particular gold, platinum, a metallic alloy or also of conductive plastics and semiconductor materials.
- the support 1a may take on a size of less than one to more than ten centimeters.
- the electrode contacts are designed with a typical density of 1 to about 1000 electrode contacts per cm 2 .
- a higher density of electrode contacts improves the signal resolution, but of course increases the expense not only of the production of the electrodes Ic, but also of the gain and the computational complexity of the drive. Note also the increased energy requirements for wireless transmission of many channels of a large number of electrodes. The energy supply can definitely become the limiting factor.
- the arrangement may vary as needed from the illustrated here with staggered rows.
- this carrier does not injure the brain tissue, in contrast to penetrating electrodes.
- the carrier Ia with the electrodes Ic can also be very small ("gru"). In this case, the operation with which the carrier Ia is used for the patient, with little effort and very little impairment of the patient is possible.
- This operation requires specific pre-surgical diagnostics and surgical planning.
- One of the most important aspects is to determine the exact target area for implantation, which can not be determined a priori due to the strong inter-individual neuroanatomical variability of the human brain. Only in exceptional cases would it be desirable to insert it at a location that was not previously determined individually. Although one knows general mappings of the brain and therefore knows, where certain functional areas are roughly to be found. In the more specific example of the motor and somatosensory cortex, even the anatomy of humans is replicated locally and individual body parts are assigned to spatially distinct areas of the cortex. For the individual patient, however, this prior knowledge is mostly not accurate enough.
- fMRI functional magnetic resonance imaging
- FIG. 3 shows a preferred embodiment of the signal interface 3.
- data transmission by cable can take place, as has hitherto been used by default in neurosurgical diagnostics.
- a permanent cable connection through the body surface however, carries an increased risk of infection and is also less attractive from a cosmetic and practical point of view.
- the signal transmission between the electrode and the amplifier takes place by inductive energy transmission without a transcutaneous cable connection.
- the wireless signal transmission system 3 is divided into two parts each above and below the skin surface 3a. From the outer transmitter / receiver unit outside the body, that is to say here above the skin surface 3a, only a coil 3b is shown as representative. In one embodiment, this external transceiver unit can only transmit data to the amplifier 4 or the computer system 5 wirelessly or by direct cable connection. Also conceivable is an alternative embodiment in which amplifier 4 and / or computer system 5 is partially or completely contained in a chip which is accommodated on the skull surface or another suitable location on the body. Which embodiment is preferred in each case or is feasible at all depends on the complexity of the application. At present, at least one compact transmitting / receiving unit to an external amplifier 4 or a computer system 5 over almost arbitrary distances (mobile radio, Bluetooth, WLAN) is technically possible without further ado.
- One of the mentioned transmission paths can also be used for the data exchange with the effector 6.
- a further two-part signal transmission interface similar to that described here can be used in the respective body part. Since the external transceiver unit is easily accessible, it can also be adapted or replaced according to the advancing technology, without the need for a renewed surgical intervention.
- a multi-functional chip 3c is used as the inner transmitting / receiving unit as a counterpart to the outer transceiver unit.
- This multi-functional chip 3c has a receiver unit 3c 1, a transmitter unit 3c2 and optionally a battery unit 3c3. Via the cable Ib, the signals from the electrodes Ic of the carrier Ia of the transmitter unit 3c2 and the receiving unit 3c 1 are supplied.
- the coil 3b of the outer transceiver transmits power and any control signals for the Ableitelektrode 1 inductively via radio frequency signals to the receiving unit 3c 1.
- control signals can be on and off commands or about a query on the power state of the battery 3c3.
- the interface is basically also suitable for the transmission of stimulation signals to the deflection electrode 1.
- the multifunction chip 3c determines the modulated control or the said stimulation signals in a manner known from telecommunications engineering.
- the energy for the required arithmetic operations of control units in the multi-functional chip 3c are obtained from the high-frequency signals.
- battery 3c3 or an accumulator can be inductively charged via the high-frequency signals, so that the energy supply is decoupled in time from the transmission at the interface.
- it is necessary to distinguish between charging and stimulation signals for example by time windows or by separate frequency bands.
- signals from the measuring electrodes Ic are transmitted via the cable Ib to the transmitter unit 3c2 and there, preferably in the 402-405 MHz signal band of the MICS (Medical Implantable Service Band), on the coil 3b or one for the Reception designed counterpart of only representatively shown coil 3b transmitted.
- MICS Medical Implantable Service Band
- the transmission interface has been described so that the transmission power of the transmitting unit 3c2 extends only to the coil 3b of the external transmitting / receiving unit.
- the transmitting unit 3c2 could also send directly to the amplifier 4, which may not even be seated on the skull surface.
- the power supply of the multi-functional chip 3c either by long-lasting batteries (currently technically unsatisfactory) or a Aufla- possibility to ensure approximately in the manner described by induction or by exploitation of the body's own energy sources.
- the input signals of the deflection electrode 1 are now accessible to the amplifier 4 outside the skull.
- the amplifier 4 operates on the input signals in a known manner.
- high-pass, low-pass or band-pass filters may be used (for example Savitzky-Golay, Buttersworth or Chebychev filters).
- a high temporal resolution for real-time transmission is advantageous, ideally the sampling rate is more than 200 Hz, but lower values are not excluded.
- the thus preprocessed input signals are then forwarded to the system 5 for evaluation.
- the function of the system 5 is to calculate control signals for the effector 6 from the input signals.
- two phases can be distinguished, namely a training and an application phase.
- the application can be interrupted again and again by training to improve or to learn further controls.
- the training is used to determine a predictive model, with the help of which the system 5 then detects during the application, the movement intention of the patient and calculates corresponding control signals.
- the aim of the training is to enter into the system 5 the required data for the classification of standard movements in order to determine the predictive model.
- a standard movement is selected for the individual training step, which the patient then gets presented in one of three scenarios, introduces himself or attempts to control the effector 6. It has been proven that this produces similar input signals as a natural, self-made controller.
- the patient does not have to produce a state of neuronal activity dictated by the evaluation procedure, which under natural conditions does not serve to control the function to be controlled, as conventionally described in the introduction, under tiring, highly concentrated concentration. Instead, he naturally resorts to his patterns of activity, which he had long since learned throughout his life. Subjectively, the patient does nothing else during training or during the application, as if he wanted to address the healthy muscles just as before the illness.
- the system 5 stores the incoming brain signals and can thus later allocate brain signals to specific movements.
- a standard exercise to be trained is determined by the patient, either by selection from a computer menu or by communication with a helper.
- Examples of standard movements with the aforementioned arm prosthetic hand can to be: open hand - close hand, move arm in different directions and lead back, or close the hand with different strength.
- the subject is repeatedly shown the chosen standard natural motion.
- the derived brain signals in a time interval from about one second before until the completion of the standard motion yields a record for the first training data set of the matrix.
- Repeated recording of the selected and the remaining standard movements to be learned results in the further data sets which together form the complete first training data set.
- a computer program repeatedly dictates to the subject sequentially the standard movements, and the brain signals derived therefrom yield the second training data set.
- a computer program tells the subject to try the standard moves. The derived during this time brain signals result in the third training data set.
- the training is now optimized by providing feedback on the accuracy of the predictive model used to correctly correlate the brain signals derived during each experiment to the displayed standard motion. So the test person notices directly how well he has already learned a movement, and his brain has the ability to adapt to the effector 6. In other words, not only the system 5 improves through the training, but also the patient due to the neuronal plasticity. It is important that this does not happen due to strenuous activity patterns to be generated artificially. The subjective feeling of the patient is much more in line with the natural process of learning a new motor skill, such as swimming or cycling. Because of the efficiency of feedback learning, the third training set's data will be weighted progressively more heavily than the other two training sets.
- the last part of the training is the repetition of the third part, but under different subjective conditions (eg prepared and unprepared, awake and sleepy) and objective conditions (eg lighting conditions, posture like sitting, standing, lying).
- subjective conditions eg prepared and unprepared, awake and sleepy
- objective conditions eg lighting conditions, posture like sitting, standing, lying.
- the result is an enhanced third training set that further improves training performance and prediction accuracy, ensuring a more robust association of brain signals under different circumstances.
- the subjective conditions are also called states, the objective conditions as circumstances.
- Fig. 4 shows the measured activity of a particular neuron over time for one and the same movement in three different states 1, 2, 3 of the experimental animal.
- the neural activities or signal patterns differ considerably, in particular also during the execution of the movement.
- the measured signal patterns are therefore classified and stored with respect to the trained motions and states, e.g. in a database.
- the measured neural activities of the patient are compared with the stored signal patterns to determine the movement that the patient is intending to perform with the effector.
- the conditions - external and internal - are recorded.
- FIG. 5 shows how advantageous it is to train the system 5 under many different conditions and to have available signal patterns with reference to the conditions and the movements presented. There are, for two living beings, "subject 1" and “subject 2", the probabilities of correct decoding of intended movements depending on the number of neurons used (signal pattern) for different trained predictive models specified.
- Black bars indicate the decoding probability if the application takes place under known, ie trained conditions; Mid-gray bars apply in the event that the states are not known, and all three trained states are combined, as it were, "into a large trained condition", while light gray bars denote the case of conditions other than training.
- the neuronal activity in the brain is determined under the condition or condition X measured. If training data (ie training signal patterns) are present in the system for the condition X or the state X, they are used for determining the movement prediction or the movement activation signals for the effector 6. If, on the other hand, there are no training data for condition X in the system, existing training data of similar conditions are used to determine the movement prediction. If condition X is unknown, for example because it is not captured, all training data that exists in the system will be used to determine the motion prediction.
- training data ie training signal patterns
- the patient can independently start a new training program for new standard movements or for ones already learned under a new circumstance.
- the patient can train a 'correct / incorrect signal' that will enable a workout restart. This gives the patient control to tell the system 5 that the prediction accuracy does not meet the requirements and that a re-training is required. Of course, this restart could also be commanded independently of standard movements, for example via a computer menu.
- the effector 6 is the patient's only communication capability, he should have this robust capability through the specially-trained "non-correct" default motion to communicate to the system 5.
- Alternative non-correct signals are original / natural brain error signals that may occur, for example if an assignment is wrong.
- FIG. 6 shows an exemplary schematic representation of the conversion of input signals into effector control signals with the aid of the training data.
- Three voltage curves of three electrodes Ic are shown by way of example on the left side. These voltage signals are first amplified and filtered as input signals in the amplifier 4.
- the filter functionality can also be localized in system 5. be siert.
- As an exemplary filter method - others are mentioned above in connection with the amplifier 4 - the voltage signals are filtered in a bandpass, then averaged over small time windows and divided into short time windows.
- the activity is then evaluated by means of mathematical methods.
- the predictive model is therefore determined on the one hand by selection of the mathematical method, on the other hand by calibration by means of the training data.
- the prediction of the intended movement by the system 5 is enabled ("intention prediction").
- Typical mathematical methods are: (1) preprocessing of the signals, for example a) filtering (eg low-pass or bandpass), b) time-frequency analysis (e.g., Fourier transform or multi-tapering) and / or c) binning and averaging in the time domain; (2) decoding of the preprocessed signals, for example discriminant analysis (linear, quadratic or regularized) or support vector machine (linear or radial basis function).
- discriminant analysis linear, quadratic or regularized
- support vector machine linear or radial basis function
- the predictive model is not limited to recognizing the limited number of standard movements. Further movements can be detected by interpolation and extrapolation of the trained and recorded neuronal correlates during standard movements.
- the effector 6 translates the effector control signals provided by the system 5 into movements. It should be repeated again that movements in understand a comprehensive sense, so may include a voice control or the mimic.
- the three above-mentioned groups come into consideration: a mechanical device such as a robot, robotic arm or prosthesis, a separate body part or an electrical device controlled by a virtual command of a computer, such as a computer, a mobile device, a household appliance or like.
- an effector input line 6a 1 Via an effector input line 6a 1, the effector control signals are transmitted from the system 5 to the effector 6.
- the prosthesis has a rotation system 6b 1 for rotating the hand. Control of a motor of the rotation system 6b 1 rotates the prosthesis in accordance with the effector control signals.
- the prosthesis has a gripping system 6b2 with motor and control that performs the effector control signals corresponding opening and closing movements of a finger part of the hand. It should be noted that it is not expedient to attempt to determine all control details from the neural data. Instead, system 5 could also only predict the type of movement intended and then autonomously determine the required individual steps.
- a feedback pressure sensors 6c are attached to the finger part. Their detected effector state signals are returned to the system 5 via an effector output line 6a2. These data could be returned to the brain as stimulation data in an extension of the invention. ben.
- the system 5 should also be able to query the actual state of the prosthesis, since the internal representation in the system 5 does not necessarily coincide with this. This is not necessarily due to inaccuracies of the system 5, but could also be caused by an external disturbance, such. As a bumping of the prosthesis, caused. By means of the queried actual state, the system then knows again the starting position for a correct motion specification.
- a hand prosthesis is not limited to the opening and closing, but with technically advanced prostheses within the scope of the invention, the implementation of more complex movements is possible.
- own body parts are activated via functional electrostimulation as effector 6, if only the neuronal connection between the brain and the body part is interrupted. Either intact nerve cells of the body part or directly the muscle fibers are stimulated. Any feedback may also be provided either via still intact endogenous pressure, strain, etc. Receptors or by means of supporting sensors, as described above for the case of the prosthesis control. Likewise, even in the case of incomplete paralysis, where there is still a (weak) residual mobility, it is conceivable to support these residual movements by motor-driven mechanical devices.
- the third group of "virtual" effectors 6 is particularly large.
- a computer cursor or a menu selection, but also the switching on of the light, the sending of an emergency call, etc. are controlled.
- a virtual prosthesis In this case, a body part is displayed three-dimensionally on a screen and controlled by neuronal activity of the patient or subject. This considerably facilitates the adaptation and selection of a suitable prosthesis.
- FIG. 8 summarizes the method according to the invention for the movement control of an effector 6 associated with a living being.
- the method comprises the following steps: a step 810 of receiving input signals representative of neural activities of the brain of the animal; A step 850 of obtaining motion drive signals for the effector 6 based on the input signals, wherein signal patterns are detected from the input signals, step 820, and from the signal patterns, the motion drive signals are calculated based on a predictive model, in steps 830 and 840; in which, according to a predetermined predictive model, a comparison process of the detected signal patterns is performed with stored training signal patterns representative of neural generated by the brain of the animal
- Comparison process are the most similar to the detected signal patterns, step 840.
- FIG. 9 illustrates the method of the invention for constructing a database of data from a predictive model for use in motion control of an effector 6 associated with an animal.
- the method comprises the following steps:
Landscapes
- Health & Medical Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Public Health (AREA)
- Animal Behavior & Ethology (AREA)
- Engineering & Computer Science (AREA)
- General Health & Medical Sciences (AREA)
- Veterinary Medicine (AREA)
- Biomedical Technology (AREA)
- Heart & Thoracic Surgery (AREA)
- Transplantation (AREA)
- Vascular Medicine (AREA)
- Cardiology (AREA)
- Oral & Maxillofacial Surgery (AREA)
- Physical Education & Sports Medicine (AREA)
- Nuclear Medicine, Radiotherapy & Molecular Imaging (AREA)
- Radiology & Medical Imaging (AREA)
- Prostheses (AREA)
- Electrotherapy Devices (AREA)
Abstract
Description
Claims
Applications Claiming Priority (2)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
DE102006008495.0A DE102006008495B4 (de) | 2006-02-23 | 2006-02-23 | Vorrichtung und Verfahren zur Echtzeit-Ansteuerung eines Effektors |
PCT/EP2007/051360 WO2007096269A1 (de) | 2006-02-23 | 2007-02-12 | Vorrichtung und verfahren zur echtzeit-ansteuerung eineas effektors |
Publications (1)
Publication Number | Publication Date |
---|---|
EP1988965A1 true EP1988965A1 (de) | 2008-11-12 |
Family
ID=37945841
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
EP07704536A Withdrawn EP1988965A1 (de) | 2006-02-23 | 2007-02-12 | Vorrichtung und verfahren zur echtzeit-ansteuerung eineas effektors |
Country Status (4)
Country | Link |
---|---|
EP (1) | EP1988965A1 (de) |
JP (1) | JP2009531077A (de) |
DE (1) | DE102006008495B4 (de) |
WO (1) | WO2007096269A1 (de) |
Families Citing this family (11)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
DE102007009356A1 (de) * | 2007-02-23 | 2008-08-28 | Otto Bock Healthcare Products Gmbh | Prothese |
DE102007028861A1 (de) * | 2007-06-22 | 2009-01-02 | Albert-Ludwigs-Universität Freiburg | Verfahren zur rechnergestützten Vorhersage von intendierten Bewegungen |
EP2045690A1 (de) * | 2007-10-04 | 2009-04-08 | Koninklijke Philips Electronics N.V. | Verbesserungen im Zusammenhang mit Gehirncomputerschnittstellen |
US9248280B2 (en) * | 2007-11-02 | 2016-02-02 | Boston Scientific Neuromodulation Corporation | Closed-loop feedback for steering stimulation energy within tissue |
JP2010257343A (ja) * | 2009-04-27 | 2010-11-11 | Niigata Univ | 意思伝達支援装置 |
ATE500865T1 (de) | 2009-04-28 | 2011-03-15 | Sorin Crm Sas | Induktives schaltnetzteil mit digitaler steuerung für aktive implantierbare medizinische vorrichtung |
JP5467267B2 (ja) * | 2010-03-05 | 2014-04-09 | 国立大学法人大阪大学 | 機器制御装置、機器システム、機器制御方法、機器制御プログラム、および記録媒体 |
DE102010043029A1 (de) * | 2010-10-27 | 2012-05-03 | Albert-Ludwigs-Universität Freiburg | Auswahlschaltung für eine Elektrodenanordnung sowie Verfahren zum Betrieb und zum Herstellen einer Elektrodenanordnung |
US9539118B2 (en) * | 2013-03-15 | 2017-01-10 | Neurolutions, Inc. | Brain-controlled body movement assistance devices and methods |
DE102016100886B4 (de) | 2016-01-20 | 2017-12-21 | Trutz Podschun | System zur Regeneration wenigstens einer durchtrennten Nervenleitung |
EP4041138A1 (de) | 2019-10-11 | 2022-08-17 | Neurolutions, Inc. | Orthesensysteme und rehabilitation beeinträchtigter körperteile |
Family Cites Families (5)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US6609017B1 (en) * | 1998-08-07 | 2003-08-19 | California Institute Of Technology | Processed neural signals and methods for generating and using them |
US6171239B1 (en) * | 1998-08-17 | 2001-01-09 | Emory University | Systems, methods, and devices for controlling external devices by signals derived directly from the nervous system |
US7209788B2 (en) | 2001-10-29 | 2007-04-24 | Duke University | Closed loop brain machine interface |
US7751877B2 (en) * | 2003-11-25 | 2010-07-06 | Braingate Co., Llc | Neural interface system with embedded id |
US7120486B2 (en) * | 2003-12-12 | 2006-10-10 | Washington University | Brain computer interface |
-
2006
- 2006-02-23 DE DE102006008495.0A patent/DE102006008495B4/de active Active
-
2007
- 2007-02-12 WO PCT/EP2007/051360 patent/WO2007096269A1/de active Application Filing
- 2007-02-12 JP JP2008555746A patent/JP2009531077A/ja active Pending
- 2007-02-12 EP EP07704536A patent/EP1988965A1/de not_active Withdrawn
Non-Patent Citations (1)
Title |
---|
See references of WO2007096269A1 * |
Also Published As
Publication number | Publication date |
---|---|
WO2007096269A1 (de) | 2007-08-30 |
DE102006008495B4 (de) | 2018-01-25 |
DE102006008495A1 (de) | 2007-09-06 |
JP2009531077A (ja) | 2009-09-03 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
DE102006008495B4 (de) | Vorrichtung und Verfahren zur Echtzeit-Ansteuerung eines Effektors | |
EP1988828B1 (de) | Sonde zur datenübertragung zwischen einem gehirn und einer datenverarbeitungsvorrichtung | |
EP2389859B1 (de) | BCI-Vorrichtung zur Rehabilitation von Schlaganfallpatienten | |
Nicolelis | Actions from thoughts | |
Fifer et al. | Simultaneous neural control of simple reaching and grasping with the modular prosthetic limb using intracranial EEG | |
DE102019202666B4 (de) | Neuronales Kommunikationssystem | |
EP2797667B1 (de) | Vorrichtung zur eichung einer nicht-invasiven desynchronisierenden neurostimulation | |
EP0969896B1 (de) | Lernfähiger sensomotorischer encoder für neuroprothesen | |
WO2014053244A1 (de) | Vorrichtung und verfahren zur untersuchung einer phasenverteilung zur ermittlung einer krankhaften interaktion zwischen verschiedenen hirnarealen | |
DE102019209096B4 (de) | Neuronales signalsystem zur verhaltensmodifikation | |
EP3215007B1 (de) | Vorrichtung zur kalibrierung einer nicht-invasiven mechanisch taktilen und/oder thermischen neurostimulation | |
DE102016100886B4 (de) | System zur Regeneration wenigstens einer durchtrennten Nervenleitung | |
DE102009025313A1 (de) | Außenohrmuskulaturerfassungsmittel | |
DE102020210676B4 (de) | Closed-loop computer-gehirn-schnittstellenvorrichtung | |
DE102019214752B4 (de) | Neuronales signalsystem, verfahren und computerprogramm zumsignalisieren eines gerätezustands | |
DE10294019B4 (de) | Neurostimulator sowie Datenübertragungsverfahren | |
WO2020052713A2 (de) | Verfahren und einrichtung zur herzüberwachung | |
DE102015119741A1 (de) | Vorrichtung zur individuell rückgekoppelten Regulation von Muskel- und/oder Sehnen-Oszillationen eines menschlichen und/oder tierischen Nutzers und Verfahren zur Aufzeichnung, Analyse und Übertragung solcher Oszillationen | |
Ali | EMG signals detection technique in voluntary muscle movement | |
Lebedev et al. | Bidirectional neural interfaces | |
Walter et al. | BCCI-a bidirectional cortical communication interface | |
Fernandez-Hidalgo | Design and simulation of a brainwave controlled neuroprosthesis | |
WO2019229588A1 (de) | Trainingsvorrichtung zum trainierenden stimulieren von nervenzellenenden und eine entsprechende prothese | |
Gonzalez | A Study into the Peripheral Nervous System to Control a Prosthetic Hand | |
Krüger | Investigation of electrodes as bidirectional human machine interface for neuro-technical control of prostheses |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
PUAI | Public reference made under article 153(3) epc to a published international application that has entered the european phase |
Free format text: ORIGINAL CODE: 0009012 |
|
17P | Request for examination filed |
Effective date: 20080923 |
|
AK | Designated contracting states |
Kind code of ref document: A1 Designated state(s): AT BE BG CH CY CZ DE DK EE ES FI FR GB GR HU IE IS IT LI LT LU LV MC NL PL PT RO SE SI SK TR |
|
RIN1 | Information on inventor provided before grant (corrected) |
Inventor name: RICKERT, JOERN Inventor name: SCHULZE-BONHAGE, ANDREAS Inventor name: MEHRING, CARSTEN Inventor name: AERTSEN, AD Inventor name: BALL, TONIO |
|
17Q | First examination report despatched |
Effective date: 20090630 |
|
DAX | Request for extension of the european patent (deleted) | ||
RAP1 | Party data changed (applicant data changed or rights of an application transferred) |
Owner name: CORTEC GMBH |
|
STAA | Information on the status of an ep patent application or granted ep patent |
Free format text: STATUS: THE APPLICATION IS DEEMED TO BE WITHDRAWN |
|
18D | Application deemed to be withdrawn |
Effective date: 20131015 |