# US 2023/0054751 A1 · Method and apparatus for classifying electroencephalogram signal, method and apparatus for training classification model, and electronic device and computer-readable storage medium > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2023/0054751 A1 - Document ID: US20230054751A1 - Status: Published application - Kind: A1 - Application number: 17/969,177 - Filed: 2022-10-19 - Published: 2023-02-23 - Earliest claimed: 2020-08-26 - Assignees: Tencent Technology (Shenzhen) Company Limited - Applicants: Tencent Technology (Shenzhen) Company Limited - Inventors: Luyan Liu; Xiaolin Hong; Kai Ma; Yefeng Zheng - Web page: https://patentgrep.com/patent/US20230054751A1 ## CPC - A61B 5/7267: {involving training the classification device} - B25J 9/0006: {Exoskeletons, i.e. resembling a human figure} - A61B 5/369: Electroencephalography [EEG] - A61B 5/374: Detecting the frequency distribution of signals, e.g. detecting delta, theta, alpha, beta or gamma waves - A61B 5/7275: {Determining trends in physiological measurement data; Predicting development of a medical condition based on physiological measurements, e.g. determining a risk factor} - A63F 13/212: using sensors worn by the player, e.g. for measuring heart beat or leg activity - A63F 13/35: Details of game servers - A63F 13/67: adaptively or by learning from player actions, e.g. skill level adjustment or by storing successful combat sequences for re-use - B25J 13/00: Controls for manipulators (program controls B25J9/16) - G06F 3/015: {Input arrangements based on nervous system activity detection, e.g. brain waves [EEG] detection, electromyograms [EMG] detection, electrodermal response detection} - G16H 50/20: for computer-aided diagnosis, e.g. based on medical expert systems - G16H 50/70: for mining of medical data, e.g. analysing previous cases of other patients - A61B 2505/09: Rehabilitation or training - A61G 5/04: motor-driven (A61G5/06 takes precedence; motor-operated rests A61G5/12; for vehicles with handlebars, equipped with three or more wheels B62K5/003, B62K5/023) - A61G 5/10: Parts, details or accessories - A61G 2203/18: by patient's head, eyes, facial muscles or voice - A63F 2300/8082: Virtual reality - A61B: DIAGNOSIS; SURGERY; IDENTIFICATION (usefulness limited to only animals A61D) - B25J: MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES ({manipulators specially adapted for use in surgery A61B34/70; manipulators used in cleaning hollow articles B08B9/04}; manipulators associated with rolling mills B21B39/20; manipulators associated with forging machines B21J13/10; {manipulators associated with picking-up and placing mechanisms B23P19/007}; means for holding wheels or parts thereof B60B30/00; {vehicles with ground-engaging propulsion means, e.g. walking members B62D57/02, B62D57/032; devices for picking-up and depositing articles or materials between conveyors B65G47/90, B65G47/91; manipulators with gripping or holding means for transferring packages B65H67/065}; cranes B66C; {manipulators used in the protection or supervision of pipe-line installations F17D5/00; walking equipment adapted for nuclear steam-generators F22B37/006}; manipulators specially adapted for, or associated with, nuclear reactors G21C; {apparatus used for handling wafers during manufacture or treatment of semiconductor H10P72/50}) - A63F: CARD, BOARD, OR ROULETTE GAMES; INDOOR GAMES USING SMALL MOVING PLAYING BODIES; VIDEO GAMES; GAMES NOT OTHERWISE PROVIDED FOR - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) - G16H: HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA - A61G: TRANSPORT, PERSONAL CONVEYANCES OR ACCOMMODATION SPECIALLY ADAPTED FOR PATIENTS OR DISABLED PERSONS (appliances for aiding patients or disabled persons to walk A61H3/00); OPERATING TABLES OR CHAIRS; CHAIRS FOR DENTISTRY; FUNERAL DEVICES (embalming corpses A01N1/00) ## Abstract A method and an apparatus for classifying an electroencephalogram signal, a device and a computer-readable storage medium. The method includes: obtaining an electroencephalogram signal; performing feature extraction on the electroencephalogram signal to obtain a signal feature corresponding to the electroencephalogram signal; obtaining a difference distribution ratio, the difference distribution ratio being used for representing impacts of difference distributions of different types on distributions of the signal feature and a source domain feature in a feature domain, the source domain feature being a feature corresponding to a source domain electroencephalogram signal; aligning the signal feature with the source domain feature according to the difference distribution ratio to obtain an aligned signal feature; and classifying the aligned signal feature to obtain a motor imagery type corresponding to the electroencephalogram signal. ## Claims ### Claim 1 A method for classifying an electroencephalogram signal, performed by a computer device, the method comprising: obtaining an electroencephalogram signal; performing feature extraction on the electroencephalogram signal to obtain a signal feature corresponding to the electroencephalogram signal; obtaining a difference distribution ratio, the difference distribution ratio being used for representing impacts of difference distributions of different types on distributions of the signal feature and a source domain feature in a feature domain, the source domain feature being a feature corresponding to a source domain electroencephalogram signal; aligning the signal feature with the source domain feature according to the difference distribution ratio to obtain an aligned signal feature; and classifying the aligned signal feature to obtain a motor imagery type corresponding to the electroencephalogram signal. ### Claim 2 (depends on 1) The method according to claim 1, wherein the difference distribution ratio comprises a first distribution ratio corresponding to a marginal distribution difference and a second distribution ratio corresponding to a conditional distribution difference; and the aligning comprises: obtaining the marginal distribution difference and the conditional distribution difference between the source domain feature and the signal feature in the feature domain; scaling down the marginal distribution difference according to the first distribution ratio, and scaling down the conditional distribution difference according to the second distribution ratio; obtaining a signal feature with a scaled-down distribution difference according to a scaled-down marginal distribution difference and a scaled-down conditional distribution difference; and determining the signal feature with the scaled-down distribution difference as the aligned signal feature. ### Claim 3 (depends on 2) The method according to claim 2, wherein the obtaining the marginal distribution difference and the conditional distribution difference between the source domain feature and the signal feature in the feature domain comprises: obtaining first classification accuracy of a margin discriminator and second classification accuracy of a condition discriminator, the margin discriminator being configured to determine a domain signal to which the electroencephalogram signal belongs, the condition discriminator being configured to determine a domain signal to which electroencephalogram signals of different types belong, and the domain signal comprising at least one of the source domain electroencephalogram signal or an inputted electroencephalogram signal; obtaining a first distribution distance between the signal feature and the source domain feature according to the first classification accuracy, and obtaining a second distribution distance between the signal feature and the source domain feature according to the second classification accuracy; and determining the first distribution distance as the marginal distribution difference, and determining the second distribution distance as the conditional distribution difference. ### Claim 4 (depends on 1) The method according to claim 1, wherein the difference distribution ratio comprises the first distribution ratio corresponding to the marginal distribution difference and the second distribution ratio corresponding to the conditional distribution difference; and the obtaining a difference distribution ratio comprises: obtaining the first distribution distance and the second distribution distance between the signal feature and the source domain feature, the first distribution distance being used for representing the marginal distribution difference between the signal feature and the source domain feature, and the second distribution distance being used for representing the conditional distribution difference between the signal feature and the source domain feature; obtaining a type quantity of motor imagery types; and obtaining the first distribution ratio corresponding to the marginal distribution difference and the second distribution ratio corresponding to the conditional distribution difference according to the first distribution distance, the second distribution distance, and the type quantity. ### Claim 5 (depends on 1) The method according to claim 1, wherein the classifying comprises: invoking a classifier to process the aligned signal feature, to obtain a prediction probability of the motor imagery type corresponding to the electroencephalogram signal; invoking the condition discriminator to process the aligned signal feature, to obtain prediction probabilities that the electroencephalogram signals of different types belong to the domain signal, the domain signal comprising at least one of the source domain electroencephalogram signal or the inputted electroencephalogram signal; invoking the margin discriminator to process the aligned signal feature, to obtain a prediction probability that the electroencephalogram signal belongs to the domain signal; and obtaining the motor imagery type corresponding to the electroencephalogram signal according to the prediction probability of the motor imagery type corresponding to the electroencephalogram signal, the prediction probabilities that the electroencephalogram signals of different types belong to the domain signal, and the prediction probability that the electroencephalogram signal belongs to the domain signal. ### Claim 6 (depends on 1) The method according to claim 1, wherein the performing comprises: invoking a temporal convolutional layer to perform feature extraction on the electroencephalogram signal, to obtain a first signal feature corresponding to the electroencephalogram signal; invoking a spatial convolutional layer to perform feature extraction on the first signal feature, to obtain a second signal feature corresponding to the electroencephalogram signal; invoking a batch normalization layer to perform feature extraction on the second signal feature to obtain a third signal feature corresponding to the electroencephalogram signal; invoking a square activation layer to perform feature extraction on the third signal feature, to obtain a fourth signal feature corresponding to the electroencephalogram signal; invoking an average pooling layer to perform feature extraction on the fourth signal feature, to obtain a fifth signal feature corresponding to the electroencephalogram signal; and invoking a dropout layer to perform feature extraction on the fifth signal feature to obtain a sixth signal feature corresponding to the electroencephalogram signal, and determining the sixth signal feature as the signal feature corresponding to the electroencephalogram signal. ### Claim 7 (depends on 2) The method according to claim 2, wherein the marginal distribution difference and the conditional distribution difference are scaled down simultaneously. ### Claim 8 An apparatus for classifying an electroencephalogram signal comprising: at least one memory configured to store program code; and at least one processor configured to read the program code and operate as instructed by the program code, the program code comprising: first obtaining code configured to cause the at least one processor to obtain an electroencephalogram signal; first feature extraction code configured to cause the at least one processor to perform feature extraction on the electroencephalogram signal, to obtain a signal feature corresponding to the electroencephalogram signal; the first obtaining code being configured to cause the at least one processor to obtain a difference distribution ratio, the difference distribution ratio being used for representing impacts of difference distributions of different types on distributions of the signal feature and a source domain feature in a feature domain, the source domain feature being a feature corresponding to a source domain electroencephalogram signal; first processing code configured to cause the at least one processor to align the signal feature with the source domain feature according to the difference distribution ratio, to obtain an aligned signal feature; and classification code configured to cause the at least one processor to classify the aligned signal feature to obtain a motor imagery type corresponding to the electroencephalogram signal. ### Claim 9 (depends on 8) The apparatus according to claim 8, wherein the difference distribution ratio comprises a first distribution ratio corresponding to a marginal distribution difference and a second distribution ratio corresponding to a conditional distribution difference; and the first processing code is further configured to cause the at least one processor to: obtain the marginal distribution difference and the conditional distribution difference between the source domain feature and the signal feature in the feature domain; scale down the marginal distribution difference according to the first distribution ratio, and scale down the conditional distribution difference according to the second distribution ratio; obtain a signal feature with a scaled-down distribution difference according to a scaled-down marginal distribution difference and a scaled-down conditional distribution difference; and determine the signal feature with the scaled-down distribution difference as the aligned signal feature. ### Claim 10 (depends on 9) The apparatus according to claim 9, wherein the first processing code is further configured to cause the at least one processor to: obtain first classification accuracy of a margin discriminator and second classification accuracy of a condition discriminator, the margin discriminator being configured to determine a domain signal to which the electroencephalogram signal belongs, the condition discriminator being configured to determine a domain signal to which electroencephalogram signals of different types belong, and the domain signal comprising at least one of the source domain electroencephalogram signal or an inputted electroencephalogram signal; obtain a first distribution distance between the signal feature and the source domain feature according to the first classification accuracy, and obtain a second distribution distance between the signal feature and the source domain feature according to the second classification accuracy; and determine the first distribution distance as the marginal distribution difference, and determine the second distribution distance as the conditional distribution difference. ### Claim 11 (depends on 8) The apparatus according to claim 8, wherein the difference distribution ratio comprises the first distribution ratio corresponding to the marginal distribution difference and the second distribution ratio corresponding to the conditional distribution difference; and the first obtaining code is further configured to cause the at least one processor to: obtain the first distribution distance and the second distribution distance between the signal feature and the source domain feature, the first distribution distance being used for representing the marginal distribution difference between the signal feature and the source domain feature, and the second distribution distance being used for representing the conditional distribution difference between the signal feature and the source domain feature; obtain a type quantity of motor imagery types; and obtain the first distribution ratio corresponding to the marginal distribution difference and the second distribution ratio corresponding to the conditional distribution difference according to the first distribution distance, the second distribution distance, and the type quantity. ### Claim 12 (depends on 8) The apparatus according to claim 8, wherein the clarification code is further configured to cause the at least one processor to: invoke a classifier to process the aligned signal feature to obtain a prediction probability of the motor imagery type corresponding to the electroencephalogram signal; invoke the condition discriminator to process the aligned signal feature to obtain prediction probabilities that the electroencephalogram signals of different types belong to the domain signal, the domain signal comprising at least one of the source domain electroencephalogram signal or the inputted electroencephalogram signal; invoke the margin discriminator to process the aligned signal feature to obtain a prediction probability that the electroencephalogram signal belongs to the domain signal; and obtain the motor imagery type corresponding to the electroencephalogram signal according to the prediction probability of the motor imagery type corresponding to the electroencephalogram signal, the prediction probabilities that the electroencephalogram signals of different types belong to the domain signal, and the prediction probability that the electroencephalogram signal belongs to the domain signal. ### Claim 13 (depends on 8) The apparatus according to claim 8, wherein the first feature extraction code is further configured to cause the at least one processor to: invoke a temporal convolutional layer to perform feature extraction on the electroencephalogram signal to obtain a first signal feature corresponding to the electroencephalogram signal; invoke a spatial convolutional layer to perform feature extraction on the first signal feature to obtain a second signal feature corresponding to the electroencephalogram signal; invoke a batch normalization layer to perform feature extraction on the second signal feature to obtain a third signal feature corresponding to the electroencephalogram signal; invoke a square activation layer to perform feature extraction on the third signal feature to obtain a fourth signal feature corresponding to the electroencephalogram signal; invoke an average pooling layer to perform feature extraction on the fourth signal feature to obtain a fifth signal feature corresponding to the electroencephalogram signal; and invoke a dropout layer to perform feature extraction on the fifth signal feature to obtain a sixth signal feature corresponding to the electroencephalogram signal, and determine the sixth signal feature as the signal feature corresponding to the electroencephalogram signal. ### Claim 14 (depends on 9) The apparatus according to claim 9, wherein the marginal distribution difference and the conditional distribution difference are scaled down simultaneously. ### Claim 15 A non-transitory computer-readable storage medium, storing computer program code that when executed by at least one processor causes the at least one processor to: obtain an electroencephalogram signal; perform feature extraction on the electroencephalogram signal to obtain a signal feature corresponding to the electroencephalogram signal; obtain a difference distribution ratio, the difference distribution ratio being used for representing impacts of difference distributions of different types on distributions of the signal feature and a source domain feature in a feature domain, the source domain feature being a feature corresponding to a source domain electroencephalogram signal; align the signal feature with the source domain feature according to the difference distribution ratio to obtain an aligned signal feature; and classify the aligned signal feature to obtain a motor imagery type corresponding to the electroencephalogram signal. ### Claim 16 (depends on 15) The non-transitory computer-readable storage medium according to claim 15, wherein the difference distribution ratio comprises a first distribution ratio corresponding to a marginal distribution difference and a second distribution ratio corresponding to a conditional distribution difference; and the aligning comprises: obtaining the marginal distribution difference and the conditional distribution difference between the source domain feature and the signal feature in the feature domain; scaling down the marginal distribution difference according to the first distribution ratio, and scaling down the conditional distribution difference according to the second distribution ratio; obtaining a signal feature with a scaled-down distribution difference according to a scaled-down marginal distribution difference and a scaled-down conditional distribution difference; and determining the signal feature with the scaled-down distribution difference as the aligned signal feature. ### Claim 17 (depends on 16) The non-transitory computer-readable storage medium according to claim 16, wherein the obtain the marginal distribution difference and the conditional distribution difference between the source domain feature and the signal feature in the feature domain comprises: obtaining first classification accuracy of a margin discriminator and second classification accuracy of a condition discriminator, the margin discriminator being configured to determine a domain signal to which the electroencephalogram signal belongs, the condition discriminator being configured to determine a domain signal to which electroencephalogram signals of different types belong, and the domain signal comprising at least one of the source domain electroencephalogram signal or an inputted electroencephalogram signal; obtaining a first distribution distance between the signal feature and the source domain feature according to the first classification accuracy, and obtaining a second distribution distance between the signal feature and the source domain feature according to the second classification accuracy; and determining the first distribution distance as the marginal distribution difference, and determining the second distribution distance as the conditional distribution difference. ### Claim 18 (depends on 15) The non-transitory computer-readable storage medium according to claim 15, wherein the difference distribution ratio comprises the first distribution ratio corresponding to the marginal distribution difference and the second distribution ratio corresponding to the conditional distribution difference; and the obtain a difference distribution ratio comprises: obtaining the first distribution distance and the second distribution distance between the signal feature and the source domain feature, the first distribution distance being used for representing the marginal distribution difference between the signal feature and the source domain feature, and the second distribution distance being used for representing the conditional distribution difference between the signal feature and the source domain feature; obtaining a type quantity of motor imagery types; and obtaining the first distribution ratio corresponding to the marginal distribution difference and the second distribution ratio corresponding to the conditional distribution difference according to the first distribution distance, the second distribution distance, and the type quantity. ### Claim 19 (depends on 15) The non-transitory computer-readable storage medium according to claim 15, wherein the classifying comprises: invoking a classifier to process the aligned signal feature, to obtain a prediction probability of the motor imagery type corresponding to the electroencephalogram signal; invoking the condition discriminator to process the aligned signal feature, to obtain prediction probabilities that the electroencephalogram signals of different types belong to the domain signal, the domain signal comprising at least one of the source domain electroencephalogram signal or the inputted electroencephalogram signal; invoking the margin discriminator to process the aligned signal feature, to obtain a prediction probability that the electroencephalogram signal belongs to the domain signal; and obtaining the motor imagery type corresponding to the electroencephalogram signal according to the prediction probability of the motor imagery type corresponding to the electroencephalogram signal, the prediction probabilities that the electroencephalogram signals of different types belong to the domain signal, and the prediction probability that the electroencephalogram signal belongs to the domain signal. ### Claim 20 (depends on 15) The non-transitory computer-readable storage medium according to claim 15, wherein the performing comprises: invoking a temporal convolutional layer to perform feature extraction on the electroencephalogram signal, to obtain a first signal feature corresponding to the electroencephalogram signal; invoking a spatial convolutional layer to perform feature extraction on the first signal feature, to obtain a second signal feature corresponding to the electroencephalogram signal; invoking a batch normalization layer to perform feature extraction on the second signal feature, to obtain a third signal feature corresponding to the electroencephalogram signal; invoking a square activation layer to perform feature extraction on the third signal feature, to obtain a fourth signal feature corresponding to the electroencephalogram signal; invoking an average pooling layer to perform feature extraction on the fourth signal feature, to obtain a fifth signal feature corresponding to the electroencephalogram signal; and invoking a dropout layer to perform feature extraction on the fifth signal feature, to obtain a sixth signal feature corresponding to the electroencephalogram signal, and determining the sixth signal feature as the signal feature corresponding to the electroencephalogram signal.