Dynamic initiation and dual-tree complex wavelet feature-based classification of motor imagery of swallow EEG signals

The use of motor imagery-based brain computer interface has recently been shown to have potential for rehabilitation. This paper proposes a novel scheme to detect motor imagery of swallow from electroencephalography (EEG) signals for dysphagia rehabilitation. The proposed scheme extracts features from the coefficients of dual-tree complex wavelet transform (DT-CWT). A novel sliding window-based peak localization scheme is proposed to dynamically locate the initiation of tongue movement from Electromyography (EMG) signal. Subsequently, effective time segments are extracted from EEG signal for classification based on the detected dynamic initiation location. Comparisons are made between our proposed scheme with that of the three existing approaches. The results based on six healthy subjects show that an increase in averaged accuracy of 9.95% is achieved. Further, an increase in averaged accuracy of 8.02% is resulted comparing our proposed scheme by using and not using the dynamic initiation to extract the time segments. Classification results using EMG data confirm that our results are not due to movements artifacts. Statistical tests with 95% confidence to estimate the accuracy on the respective action at chance level show that five out of six subjects performed above chance level for our proposed dynamic initiation and wavelet feature-based approach.