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

IEEE Transactions on Neural Systems and Rehabilitation Engineering : a Publication of the IEEE Engineering in Medicine and Biology Society
Yao LiThenkurussi Kesavadas

Abstract

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 brain-computer 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 vali...Continue Reading

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Methods Mentioned

BETA
feature extraction

Software Mentioned

Keras
TRCA
hspace
mathbb
CORCA
MsetCCA
CA
SSVEP
CNN
Conv

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