Training method of brain activity state classification model, brain activity state classification method, device, and storage medium

An electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable by the processor,

• wherein the processor, when executing the computer program, performs the following:
• acquiring pulse sequences of electroencephalography signal samples corresponding to a plurality of training tasks for brain activity state classification;
• and
• inputting the pulse sequences of the electroencephalography signal samples corresponding to the plurality of training tasks into an initial brain activity state classification model separately and training the brain activity state classification model based on a target rule,
• wherein the processor performs training the brain activity state classification model based on the target rule by:
• in a forward propagation stage in the target rule, updating Hebbian information corresponding to each synapse in the brain activity state classification model according to the pulse sequences corresponding to the plurality of training tasks;
• and
• in a backward propagation stage in the target rule, determining a weight of each synapse in the brain activity state classification model according to the Hebbian information corresponding to each synapse and a backward propagation result;
• wherein the Hebbian information is determined based on a co-firing frequency of each synapse, the Hebbian information is used for representing a degree of association between the plurality of training tasks and each synapse, and the brain activity state classification model is established based on a spiking neural network.

A non-transitory computer-readable storage medium storing a computer program, wherein when executed by a processor, the computer program causes the processor to perform the following:

• acquiring pulse sequences of electroencephalography signal samples corresponding to a plurality of training tasks for brain activity state classification;
• and
• inputting the pulse sequences of the electroencephalography signal samples corresponding to the plurality of training tasks into an initial brain activity state classification model separately and training the brain activity state classification model based on a target rule,
• wherein the computer program causes the processor to perform training the brain activity state classification model based on the target rule by:
• in a forward propagation stage in the target rule, updating Hebbian information corresponding to each synapse in the brain activity state classification model according to the pulse sequences corresponding to the plurality of training tasks;
• and
• in a backward propagation stage in the target rule, determining a weight of each synapse in the brain activity state classification model according to the Hebbian information corresponding to each synapse and a backward propagation result;
• wherein the Hebbian information is determined based on a co-firing frequency of each synapse, the Hebbian information is used for representing a degree of association between the plurality of training tasks and each synapse, and the brain activity state classification model is established based on a spiking neural network.