Abstract: This disclosure provides an EEG decoding method based on a non-negative CP decomposition model. The method extracts time component characteristics of the EEG of the different subjects in the boundary avoidance task, optimizes a characteristic dimension by using a 2-DPCA, and takes classification by using a support vector machine, so that differences of the EEG of subjects in different states can be reflected, and the EEG classification of the single subject has a great accuracy. The time component characteristics of the EEG can be obtained by using the channel components and the frequency components based on the non-negative CP decomposition model and by means of the interaction between the EEG modes. The characteristics of the obtained EEG time components have good separability, and the dimensions of the characteristics are optimized, so that the EEG of left and right hand movements in the boundary avoidance tasks can be effectively decoded.

Abstract: In a data preparation process, raw data in a database is classified into first-type data and second-type data. In a data retrieval process, a first retrieval range is determined in the first-type data, and to-be-retrieved data is retrieved in the first retrieval range, to obtain a first retrieval result; the to-be-retrieved data is retrieved from all of the second-type data, to obtain a second retrieval result; and finally, a final retrieval result is determined from the first retrieval result and the second retrieval result.

Abstract: In a data preparation process, raw data in a database is classified into first-type data and second-type data. In a data retrieval process, a first retrieval range is determined in the first-type data, and to-be-retrieved data is retrieved in the first retrieval range, to obtain a first retrieval result; the to-be-retrieved data is retrieved from all of the second-type data, to obtain a second retrieval result; and finally, a final retrieval result is determined from the first retrieval result and the second retrieval result.

Abstract: This disclosure provides an EEG decoding method based on a non-negative CP decomposition model. The method extracts time component characteristics of the EEG of the different subjects in the boundary avoidance task, optimizes a characteristic dimension by using a 2-DPCA, and takes classification by using a support vector machine, so that differences of the EEG of subjects in different states can be reflected, and the EEG classification of the single subject has a great accuracy. The time component characteristics of the EEG can be obtained by using the channel components and the frequency components based on the non-negative CP decomposition model and by means of the interaction between the EEG modes. The characteristics of the obtained EEG time components have good separability, and the dimensions of the characteristics are optimized, so that the EEG of left and right hand movements in the boundary avoidance tasks can be effectively decoded.