Convolutional Correlation Analysis for Enhancing the Performance of SSVEP-Based Brain-Computer Interface

Currently, most of the high-performance models for frequency recognition of steady-state visual evoked potentials (SSVEPs) are linear. However, SSVEPs collected from different channels can have non-linear relationship among each other. Linearly combining electroencephalogram (EEG) from multiple channels is not the most accurate solution in SSVEPs classification. To further improve the performance of SSVEP-based braincomputer interface (BCI), we propose a convolutional neural network-based non-linear model, i.e. convolutional correlation analysis (Conv-CA). Different from pure deep learning models, Conv-CA use convolutional neural networks (CNNs) at the top of a self-defined correlation layer. The CNNs function on how to transform multiple channel EEGs into a single EEG signal. The correlation layer calculates the correlation coefficients between the transformed single EEG signal and reference signals. The CNNs provide non-linear operations to combine EEGs in different channels and different time. And the correlation layer constrains the fitting space of the deep learning model. A comparison study between the proposed Conv-CA method and the task-related component analysis (TRCA) based methods is conducted. Both methods are validated on a 40-class SSVEP benchmark dataset recorded from 35 subjects. The study verifies that the Conv-CA method significantly outperforms the TRCA-based methods. Moreover, Conv-CA has good explainability since its inputs of the correlation layer can be analyzed for visualizing what the model learnt from the data. Conv-CA is a non-linear extension of spatial filters. Its CNN structures can be further explored and tuned for reaching a better performance. The structure of combining neural networks and unsupervised features has the potential to be applied to the classification of other signals.