Method and apparatus for classifying electroencephalogram signal, method and apparatus for training classification model, and electronic device and computer-readable storage medium

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.

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.