METHOD, DEVICE AND SYSTEM FOR PREDICTING AN EFFECT OF ACOUSTIC STIMULATION OF THE BRAIN WAVES OF AN INDIVIDUAL
A method, implemented by computer, for predicting an effect of an acoustic stimulation of the brain waves of an individual, the method including: acquisition of at least one measurement signal representative of a physiological signal of the individual, by a device for acoustic stimulation of brain waves that is suitable for being worn by the individual; analysis of the measurement signal by an artificial intelligence trained to predict the effect of an acoustic stimulation; and determination of whether an acoustic stimulation is to be performed by the device.
1 . A method, implemented by computer means, for predicting an effect of an acoustic stimulation of the brain waves of an individual, the method comprising:
acquisition of at least one measurement signal representative of a physiological signal of the individual, by a device for acoustic stimulation of brain waves that is suitable for being worn by the individual,
analysis of said measurement signal by an artificial intelligence trained to predict the effect of an acoustic stimulation, and
determination of whether an acoustic stimulation is to be performed by the device.
2 . The method according to claim 1 , wherein the artificial intelligence is a neural network, the method including a prior learning step comprising:
a plurality of successive acoustic stimulations of the brain waves of said individual,
an acquisition of measurement signals representative of a physiological signal of said individual at least before and after each of the acoustic stimulations of said plurality of acoustic stimulations,
for each of the acoustic stimulations, determination of a change in the measurement signals acquired after an acoustic stimulation in comparison to the measurement signal acquired before this acoustic stimulation, and association of an effect with said change,
training of said neural network until a threshold of convergence is reached, and
storage of said neural network.
3 . The method according to claim 2 , wherein the learning step further comprises the indication of physiological data of said individual, the neural network being further trained to predict the effect of an acoustic stimulation on the basis of said physiological data of said individual.
4 . The method according to claim 1 , wherein the artificial intelligence is a neural network, the method including a prior learning step comprising:
at least one acoustic stimulation of the brain waves of a plurality of individuals,
an acquisition of measurement signals representative of a physiological signal of said plurality of individuals at least before and after said acoustic stimulation,
determination of a change in the measurement signals acquired after said acoustic stimulation in comparison to the measurement signals acquired before said acoustic stimulation, and association of an effect with said change,
training of said neural network until a threshold of convergence is reached, and
storage of said neural network.
5 . The method according to claim 4 , wherein the learning step further comprises the indication of physiological data of said plurality of individuals, the neural network being further trained to predict the effect of an acoustic stimulation on the basis of said physiological data of said plurality of individuals.
6 . The method according to claim 2 , wherein measurement signals representative of a physiological signal of said individual are acquired continuously, the neural network also being trained continuously and in real time to predict the effect of an acoustic stimulation on the basis of said measurement signals of said individual.
7 . The method according to claim 1 , wherein an acoustic stimulation comprises the emission of an acoustic signal, the method further comprising a changing of at least one parameter of the acoustic signal to be emitted, on the basis of a result of the prediction by the artificial intelligence of the effect of an acoustic stimulation.
8 . A device for predicting an effect of an acoustic stimulation of the brain waves of an individual, comprising:
an acquisition module configured to acquire at least one measurement signal representative of a physiological signal of the individual,
a processor communicating with the acquisition module and configured to analyze said at least one measurement signal representative of a physiological signal of the individual, by an artificial intelligence trained to predict the effect of an acoustic stimulation.
9 . The device according to claim 8 , further comprising:
an emission module configured to emit an acoustic signal audible to the individual, and communicating with said processor, said acoustic signal being emitted or not emitted depending on a result of the prediction by the artificial intelligence of the effect of said acoustic stimulation.
10 . The device according to claim 8 , wherein the artificial intelligence comprises a neural network trained to predict the effect of an acoustic stimulation and wherein the device for acoustic stimulation further comprises a memory storing said neural network.
11 . A system for predicting an effect of an acoustic stimulation of the brain waves of an individual, comprising:
a device for predicting an effect of an acoustic stimulation of the brain waves of an individual, comprising:
an acquisition module configured to acquire at least one measurement signal representative of a physiological signal of the individual,
a processor communicating with the acquisition module and configured to analyze said at least one measurement signal representative of a physiological signal of the individual, by an artificial intelligence trained to predict the effect of an acoustic stimulation,
a server that is remote from said device.
12 . The system according to claim 11 , wherein the server is configured to store a database comprising a plurality of measurement signals representative of a physiological signal of at least one individual, said plurality of measurement signals having been acquired by said device.
13 . The system according to claim 11 , comprising a plurality of devices, the plurality of devices being in communication with the server.
14 . The method according to claim 2 , wherein the artificial intelligence is a neural network, the method including a prior learning step comprising:
at least one acoustic stimulation of the brain waves of a plurality of individuals,
an acquisition of measurement signals representative of a physiological signal of said plurality of individuals at least before and after said acoustic stimulation,
determination of a change in the measurement signals acquired after said acoustic stimulation in comparison to the measurement signals acquired before said acoustic stimulation, and association of an effect with said change,
training of said neural network until a threshold of convergence is reached, and
storage of said neural network.
15 . The method according to claim 3 , wherein the artificial intelligence is a neural network, the method including a prior learning step comprising:
at least one acoustic stimulation of the brain waves of a plurality of individuals,
an acquisition of measurement signals representative of a physiological signal of said plurality of individuals at least before and after said acoustic stimulation,
determination of a change in the measurement signals acquired after said acoustic stimulation in comparison to the measurement signals acquired before said acoustic stimulation, and association of an effect with said change,
training of said neural network until a threshold of convergence is reached, and
storage of said neural network.
16 . The method according to claim 3 , wherein measurement signals representative of a physiological signal of said individual are acquired continuously, the neural network also being trained continuously and in real time to predict the effect of an acoustic stimulation on the basis of said measurement signals of said individual.
17 . The method according to claim 4 , wherein measurement signals representative of a physiological signal of said individual are acquired continuously, the neural network also being trained continuously and in real time to predict the effect of an acoustic stimulation on the basis of said measurement signals of said individual.
18 . The method according to claim 5 , wherein measurement signals representative of a physiological signal of said individual are acquired continuously, the neural network also being trained continuously and in real time to predict the effect of an acoustic stimulation on the basis of said measurement signals of said individual.
19 . The method according to claim 2 , wherein an acoustic stimulation comprises the emission of an acoustic signal, the method further comprising a changing of at least one parameter of the acoustic signal to be emitted, on the basis of a result of the prediction by the artificial intelligence of the effect of an acoustic stimulation.
20 . The method according to claim 3 , wherein an acoustic stimulation comprises the emission of an acoustic signal, the method further comprising a changing of at least one parameter of the acoustic signal to be emitted, on the basis of a result of the prediction by the artificial intelligence of the effect of an acoustic stimulation.