Analysis and application of functional connectivity in synchronic hybrid mental tasks for brain-computer interface

Wang, Li; Yan, Zhenxiong; Liu, Yanjun; Hu, Linchun

doi:10.1016/j.measurement.2021.110116

Cited by 2 publications

(2 citation statements)

References 47 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…When subjects perform psychological tasks, the power of EEG signal from the relevant cerebral cortex changes at a specific frequency 28 . To quantify this power change, we calculated the power spectrum density of each subband.…”

Section: Methodsmentioning

confidence: 99%

“…When subjects perform psychological tasks, the power of EEG signal from the relevant cerebral cortex changes at a specific frequency. 28 To quantify this power change, we calculated the power spectrum density of each subband. The power spectrum density shows the distribution of the spectral power of the signal in the unit frequency band accompanied with frequency.…”

Section: Power Spectrum Density-based Subband Selectionmentioning

confidence: 99%

See 1 more Smart Citation

Optimal channel and frequency band‐based feature selection for motor imagery electroencephalogram classification

Meng

Dong

Gao

et al. 2022

Int J Imaging Syst Tech

View full text Add to dashboard Cite

Common spatial pattern (CSP) is a widely adopted method for electroencephalogram (EEG) feature extraction in brain-computer interface (BCI) based on motor imagery. Bandpass-filtering EEG into several subbands related to brain activity tasks is an effective approach to improve the performance of CSP based algorithm. However, this approach tends to suffer the over-fitting problem because of the increase in feature dimension. Therefore, we proposed an optimal channel and frequency band-based CSP feature selection method in this paper. Firstly, the correlation coefficient was calculated to select the optimal channels, and these channels were bandpass-filtered into multiple overlapping subbands. The subbands with higher power spectrum density were chosen for CSP feature extraction. Next, the pair-wise relevance was utilized to remove subband features with less difference. And then the screened subband features were combined with features extracted from the broadband signal. The Fisher ratio was exploited to carry out further feature selection. Finally, a support vector machine (SVM) was trained to classify the selected CSP features. An experimental study was implemented on BCI competition III dataset IVa and BCI competition IV dataset 1. The average classification accuracy reached 89.33% and 84.08%, which indicated the rationality and effectiveness of the proposed method.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Power Spectrum Density-based Subband Selectionmentioning

confidence: 99%