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基于PSD特征的FBCCA脑电信号识别方法

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当前基于稳态视觉诱发电位(steady-state visual evoked potential,SSVEP)的脑机接口(brain-computer interfaces,BCIs)使用的都是单一识别算法,针对不同时间长度的识别准确率较低.提出了一种基于滤波器组的典型相关分析(filter bank ca-nonical correlation analysis,FBCCA)与功率谱密度(power spectral density,PSD)分析相结合的SSVEP识别算法,可以提高SSVEP识别的普适性与准确率.该方法使用FBCCA寻找高相似度的参考频率信号,再通过多组PSD分析来锁定最终的响应频率,完成频率识别.该方法无需经过训练就能得到较高的识别准确率.实验结果表明:在刺激时长为 1s 时,该方法能达到86.61%的准确率,比PSD分析方法提升了5.44%,比典型相关性分析方法(canonical correlation analysis,CCA)提升了10.38%的准确率,比FBCCA提升了8.86%的准确率.
FBCCA SSVEP EEG Signal Recognition Method Based on PSD
The current brain-computer interfaces(BCIs)based on steady-state visual evoked potential(SSVEP)typically employ single recognition algorithms,which often result in low accuracy for different time durations.A novel SSVEP recognition algorithm was proposed,the proposed algorithm combined filter bank canonical correlation analysis(FBCCA)and power spectral density(PSD)analysis,in order to improve the universality and accuracy of SSVEP recognition.The proposed method utilized FBCCA to identify highly similar reference frequency signals and then locked in the final response frequency through multiple set of PSD analysis,achie-ving frequency recognition without the need for training.Experimental results demonstrate that with the stimulation duration of 1 s,the proposed method achieves 86.61%accuracy,5.44%better than the PSD analysis method,10.38%better than the canonical correla-tion analysis(CCA),and an8.86%better than the FBCCA.

brain computer interface(BCI)steady state visual evoked potential(SSVEP)filter bank canonical correlation analy-sis(FBCCA)power spectral density(PSD)frequency recognition

张学军、杨京儒

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南京邮电大学电子与光学工程学院,南京 210023

南京邮电大学柔性电子(未来技术)学院,南京 210023

脑机接口(BCI) 稳态视觉诱发电位(SSVEP) 滤波器组的典型相关分析(FBCCA) 功率谱密度(PSD) 频率识别

国家自然科学基金

61977039

2024

10.12404/j.issn.1671-1815.2300414

科学技术与工程
中国技术经济学会

科学技术与工程

CSTPCD北大核心
影响因子：0.338
ISSN：1671-1815
年,卷(期)：2024.24(4)
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