Abstract This study aimed to improve the subjects' EEG arousal by establishing a “bowl-ball” model and performing visually guided boundary avoidance tasks. In the process of interacting with the “bowl-ball” model, the EEG data of 10 healthy subjects about the left and right-hand motor tasks were collected, and the optimized features of the EEG were classified to realize the decoding of the exercise intention. The EEG signal induced by the boundary avoidance task is subjected to 8-13Hz band-pass filtering to obtain data of a specific frequency band, and the frequency components of EEG were obtained through continuous wavelet transform to generate EEG tensor. We used the non-negative CP decomposition model to extract the time component features of the EEG tensor, then used the two-dimensional principal component analysis to optimize the features, used the support vector machine (SVM) to classify the features, and compared with the method of feature extraction and feature classification using common spatial pattern (CSP) and SVM. The results of all subjects showed that the general best component number of CP decomposition was 16, based on the feature extraction method of non-negative CP decomposition model, the accuracy of SVM classification was 95.5%±3.0%, and the AUC value was 0.978 2±0.012 1. The classification accuracy was better than CSP+SVM (93.7%±3.1%). The discriminant score of the classification result was tested by t-test, and the classification result had 95% confidence (P<0.05). In conclusion, the motor intention classification based on features extracted from the non-negative CP decomposition model reflected the differences in different states in boundary avoidance tasks and improve classification performance.
Fu Rongrong,Yu Bao,Sun Jiedi. Research on EEG Feature Extraction Method in Boundary Avoidance Task Based on Non-Negative CP Decomposition Model[J]. Chinese Journal of Biomedical Engineering, 2021, 40(1): 53-59.
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