Filter Bank Convolutional Neural Network for SSVEP Classification

Harmonics in electroencephalogram (EEG) caused by visual stimulation are the main basis of classification of steady-state visual evoked potential (SSVEP). However, the correlation of various harmonics, which could improve the classification performance especially when evoked EEG components are much weaker than spontaneous EEG components, has not been take into consideration in the design of classifier in previous studies. In this study, we proposed a filter bank convolutional neural network (FBCNN) method to optimize SSVEP classification. Three filters with passbands covering each harmonic of SSVEP signals are used to extract and separate the corresponding components, and the information from them are transformed into frequency domain. Subsequently, we introduce a novel convolutional neural network (CNN) architecture with three parallel CNN channels to extract and learn the harmonic features in passbands, and conclusions on the correlation among harmonics can finally be made by pair-add-up operations and dimension reductions to weigh the feature vectors. The proposed FBCNN is evaluated on two public datasets (Dataset1: 12-class, 10 subjects; Dataset2: 40-class, 35 subjects) to compare with other methods. The experimental results illustrate that FBCNN method improves the performance of CNN-based SSVEP classification methods and has a great potential to be applied in SSVEP-based BCI.