A two-step idle-state detection method for SSVEP BCI

Du, Jiale; Ke, Yufeng; Li, Xiuting; Li, Wentao; Kong, Linghan; Wang, Ningci; Xu, Minpeng; An, Xingwei; Ming, Dong

doi:10.1109/embc.2019.8857024

Cited by 3 publications

(2 citation statements)

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“…Classification accuracy Acc in equation ( 16) most directly reflects the usability of the system. However, Evaluation results based on Precision and Recall are more accurate in equation (17,18). This study uses F1 as an evaluation metric to address the presence of inaccuracies in recall evaluation.…”

Section: ) Performance Evaluationmentioning

confidence: 99%

“…The general practice is to use the CCA coefficients or PSD coefficients obtained during the computation of the above two methods as the basis for classification. In recent years, the main working state classification algorithms in the direction of SSVEP are the CCA threshold method (CCAthre) [15], the CCA correlation coefficient ratio method (CCAcorr) [16], and the PSD threshold method (PSDthre) [17]. However, the criteria of such algorithms use only one hreshold basis, which can result in the system being unable to respond in time when the experimental signals are disturbed.…”

Section: Introductionmentioning

confidence: 99%

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A Genetic Algorithm-Based Multi-Parameter Method for Asynchronous Working State Classification Under SSVEP

Wang,

Geng,

Duan

et al. 2024

IEEE Access

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Working state classification, which is a critical part of an asynchronous control system based on a brain-computer interface (BCI). The traditional algorithms for the working state classification are based on the threshold of power spectral density (PSD) or the canonical correlation analysis (CCA) parameters, which usually suffer insufficient robustness in case of using one threshold. To improve the robustness of working state classification, a multi-parameter asynchronous working state classification method combined with a genetic algorithm (GA-PSD-CCA) is proposed based on SSVEP. GA-PSD-CCA obtains the CCA parameter and the PSD parameter from the processed electroencephalogram (EEG) signals.Then GA-PSD-CCA iteratively searches the optimization of the PSD and CCA parameters and finds the threshold value by the genetic algorithm for the working state classification to recognize the working state. An experiment is designed to compare the application effect of GA-PSD-CCA with the traditional algorithm based on the EEG signals of ten subjects. The results show that the average accuracy with GA-PSD-CCA achieved 89.35% ± 8.39% and has an improvement of more than 7% compared with the traditional algorithm which has the highest accuracy. GA-PSD-CCA has a large improvement in asynchronous working state classification accuracy and achieves high target recognition accuracy. The BCI systems with the GA-PSD-CCA will have lower false recognition of commands and higher reliability and smoothness of state recognition processes.INDEX TERMS Asynchronous control, brain-computer interface (BCI), steady-state visual evoked potentials (SSVEP), working state classification.

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Section: ) Performance Evaluationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%