Feature consistency-based model adaptation in session-to-session classification: a study using motor imagery of swallow EEG signals.

The performance degradation for session to session classification in brain computer interface is a critical problem. This paper proposes a novel method for model adaptation based on motor imagery of swallow EEG signal for dysphagia rehabilitation. A small amount of calibration testing data is utilized to select the model catering for test data. The features of the training and calibration testing data are firstly clustered and each cluster is labeled by the dominant label of the training data. The cluster with the minimum impurity is selected and the number of features consistent with the cluster label are calculated for both training and calibration testing data. Finally, the training model with the maximum number of consistent features is selected. Experiments conducted on motor imagery of swallow EEG data achieved an average accuracy of 74.29% and 72.64% with model adaptation for Laplacian derivates of power features and wavelet features, respectively. Further, an average accuracy increase of 2.9% is achieved with model adaptation using wavelet features, in comparison with that achieved without model adaptation, which is significant at 5% significance level as demonstrated in the statistical test.