A separated feature learning based DBN structure for classification of SSMVEP signals

Signal processing is one of the key points in brain computer interface (BCI) application. The common methods in BCI signal classification include canonical correlation analysis (CCA), support vector machine (SVM) and so on. However, because BCI signals are very complex and valid signals often come with confounded background noise, many current classification methods would lose meaningful information embedded in human EEGs. Otherwise, due to the huge inter-subject variability with respect to characteristics and patterns of BCI signals, there often exists large difference of classification accuracy among different subjects. Since BCI signals have high dimensionality and multi-channel properties, this paper proposes a novel structure of deep belief neural (DBN) network stacked by restricted boltsman machine (RBM) to extract efficient features from steady-state motion visual evoked potential signals and implement further classification. Here DBN extracts local feature from BCI data of each channel separately and fuses the local features, and then input the fused features to the output classifier which is consist of softmax units. Results proved that the proposed algorithm could achieve higher accuracy and lower inter-subject variability in short response time when compared to conventional CCA method.