Steady-State Visual Evoked Potential Classification Using Complex Valued Convolutional Neural Networks.

Akira Ikeda,Yoshikazu Washizawa

doi:10.3390/s21165309

Abstract

The steady-state visual evoked potential (SSVEP), which is a kind of event-related potential in electroencephalograms (EEGs), has been applied to brain–computer interfaces (BCIs). SSVEP-based BCIs currently perform the best in terms of information transfer rate (ITR) among various BCI implementation methods. Canonical component analysis (CCA) or spectrum estimation, such as the Fourier transform, and their extensions have been used to extract features of SSVEPs. However, these signal extraction methods have a limitation in the available stimulation frequency; thus, the number of commands is limited. In this paper, we propose a complex valued convolutional neural network (CVCNN) to overcome the limitation of SSVEP-based BCIs. The experimental results demonstrate that the proposed method overcomes the limitation of the stimulation frequency, and it outperforms conventional SSVEP feature extraction methods.

Highlights

Classification Using Complex ValuedThe brain–computer interface (BCI) or brain–machine interface (BMI) is a direct communication pathway for controlling external devices by discriminating brain signals [1,2].brain–computer interfaces (BCIs) have been widely researched and applied for many practical systems [3,4,5,6]
We propose a complex valued convolutional neural network (CVCNN) for state visual evoked potential (SSVEP) classification
The classification performance of the SSVEP-based BCI was evaluated in terms of the classification accuracy and information transfer rate (ITR) by using the cross validation for each subject

Summary

Introduction

The brain–computer interface (BCI) or brain–machine interface (BMI) is a direct communication pathway for controlling external devices by discriminating brain signals [1,2]. BCIs have been widely researched and applied for many practical systems [3,4,5,6]. There are various methods for acquiring brain signals for BCIs such as electroencephalography (EEG), magnetoencephalography (MEG), and functional magnetic resonance imaging (fMRI). There are various ways to issue commands for BCIs. For example, event-related potentials (ERPs) such as P300 [8], sensorimotor rhythms (SMRs) such as μ-rhythm [9,10], the steady-state visual evoked potential (SSVEP), and auditory or tactile evoked responses [11,12]

Methods

Results

Conclusion