Dual-stream convolutional neural network can use original time domain signal and frequency domain signal as input.
Using time-frequency signal as input can achieve higher performance than using time-domain or frequency-domain signal alone.
The linear weighted fusion method can combine the time-domain features with the frequency-domain features.
The optimal weight value α for each subject can be searched.
Background and objective
An important task of the brain-computer interface (BCI) of motor imagery is to extract effective time-domain features, frequency-domain features or time-frequency domain features from the raw electroencephalogram (EEG) signals for classification of motor imagery. However, choosing an appropriate method to combine time domain and frequency domain features to improve the performance of motor imagery recognition is still a research hotspot.
In order to fully extract and utilize the time-domain and frequency-domain features of EEG in classification tasks, this paper proposed a novel dual-stream convolutional neural network (DCNN), which can use time domain signal and frequency domain signal as the inputs, and the extracted time-domain features and frequency-domain features are fused by linear weighting for classification training. Furthermore, the weight can be learned by the DCNN automatically.
The experiments based on BCI competition II dataset III and BCI competition IV dataset 2a showed that the model proposed by this study has better performance than other conventional methods. The model used time-frequency signal as the inputs had better performance than the model only used time-domain signals or frequency-domain signals. The accuracy of classification was improved for each subject compared with the models only used one signals as the inputs.
Further analysis shown that the fusion weight of different subject is specifically, adjusting the weight coefficient automatically is helpful to improve the classification accuracy.Le texte complet de cet article est disponible en PDF.
Keywords : Brain-computer interface, Motor imagery, Time-domain, Frequency-domain, Convolutional neural network