Frequency Recognition of Short-Time SSVEP Signal Using CORRCA-Based Spatio-Spectral Feature Fusion Framework

IEEE Access

Shabbir Mahmood¹  Iffat Farhana²  Md. Khademul Islam Molla³  Md. Rabiul Islam⁴  Jungpil Shin⁵ 

[1] Department of Computer Science and Engineering, Green University of Bangladesh, Dhaka, Bangladesh;Department of Computer Science and Engineering, University of Information Technology and Sciences (UITS), Dhaka, Bangladesh;Department of Computer Science and Engineering, University of Rajshahi, Rajshahi, Bangladesh;Department of Medicine, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA;School of Computer Science and Engineering, The University of Aizu, Aizuwakamatsu, Fukushima, Japan;

关键词: Brain-computer interface (BCI);    correlated component analysis (CORRCA);    electroencephalogram (EEG);    feature fusion framework;    filterbank analysis;    steady-state visual evoked potential (SSVEP);   

DOI  :  10.1109/ACCESS.2021.3136774

来源: DOAJ

【 摘 要 】

Brain-computer interface (BCI) refers to the recognition of brain activity leading to generate corresponding commands to interact with external devices. Due to its safety and high time resolution, electroencephalogram (EEG) based BCIs have become popular. Steady-state visual evoked potential (SSVEP) is an EEG particularly attractive due to high signal to noise ratio (SNR) and robustness. A spatio-spectral feature fusion approach is introduced to recognize the frequency of short-time SSVEP using correlated component analysis (CORRCA). Two reference signals are generated by averaging each half of the training trials. The signal of each channel is passed through a filter bank designed to decompose into a predefined set of subbands. The spatial correlation coefficients are calculated between each subband of the test trial and the reference signals using CORRCA. The two sets of coefficients derived from two reference signals are merged and sorted in descending order. Thus obtained coefficients are weighted using a nonlinear function to define their contribution in frequency recognition. The weighted coefficients are fused to obtain a single coefficient for the target stimulus frequency of individual subband. The derived coefficients for each subband are weighted with another nonlinear function and fused to single coefficient for the target stimulus. A similar process is applied for each stimulus frequency and then the frequency corresponding to the highest coefficient is recognized as the target stimulus. The performance of the proposed method outperforms other existing algorithms to recognize the stimulus frequencies of SSVEP.

【 授权许可】

Unknown