Improvement of the Classification Accuracy of Steady-State Visual Evoked Potential-Based Brain-Computer Interfaces by Combining L1-MCCA with SVM

Yuhang Gao,Weixian Li,Jianhu Chen,Qing He,Yujin Zhang,Juanning Si,Sijin Wu,Hao Liu

doi:10.3390/app112311453

Abstract

Canonical correlation analysis (CCA) has been used for the steady-state visual evoked potential (SSVEP) based brain-computer interface (BCI) for a long time. However, the reference signal of CCA is relatively simple and lacks subject-specific information. Moreover, over-fitting may occur when a short time window (TW) length was used in CCA. In this article, an optimized L1-regularized multiway canonical correlation analysis (L1-MCCA) is combined with a support vector machine (SVM) to overcome the aforementioned shortcomings in CCA. The correlation coefficients obtained by L1-MCCA were transferred into a particle-swarm-optimization (PSO)-optimized support vector machine (SVM) classifier to improve the classification accuracy. The performance of the proposed method was evaluated and compared with the traditional CCA and power spectral density (PSD) methods. The results showed that the accuracy of the L1-MCCA-PSO-SVM was 96.36% and 98.18% respectively when the TW lengths were 2 s and 6 s. This accuracy is higher than that of the traditional CCA and PSD methods.

Highlights

Brain-computer interface (BCI) aims to create new communication pathways without depending on the brain’s normal output pathways of peripheral nerves and muscles [1]
It has been reported that the L1-regularized multiway canonical correlation analysis (L1-multiway canonical correlation analysis (MCCA)) method can improve the classification accuracy of state visual evoked potential (SSVEP)-based brain-computer interface (BCI) by optimizing the reference signal
The results showed that compared with Canonical correlation analysis (CCA), L1-MCCA improves the correlation coefficient of the target frequency and reduces the correlation coefficient of other non-target frequencies, indicating the effectiveness of L1-MCCA in SSVEP classification

Summary

Introduction

Brain-computer interface (BCI) aims to create new communication pathways without depending on the brain’s normal output pathways of peripheral nerves and muscles [1]. Electroencephalography (EEG)-based BCI is the most popular method owing to its unique advantages, such as non-invasiveness, cost-effectiveness, portability, and high temporal resolution. The most commonly utilized BCI paradigms based on EEG mainly include steady-state visual evoked potential (SSVEP) [11,12,13], P300 [14,15], motor imagery [16,17], etc. SSVEP based BCI has the advantages of simple preparation, high classification accuracy, high signal-to-noise ratio, short response time, and fewer training requirements [18]. The neural responses acquired by EEG have peak frequencies that are the fundamental and harmonic frequencies of the flicker frequency. The user’s target can be identified by matching the characteristics of the acquired EEG signals to the command-related particular flicker frequency

Methods

Findings

Discussion

Conclusion