摘要
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 canonical 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1 s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。
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,achieving 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 correlation analysis(CCA),and an 8.86%better than the FBCCA.
作者
张学军
杨京儒
ZHANG Xue-jun;YANG Jing-ru(School of Electronic and Optical Engineering,Nanjing University of Posts and Telecommunications,Nanjing 210023,China;School of Flexible Electronics(Future Technology),Nanjing University of Posts and Telecommunications,Nanjing 210023,China)
出处
《科学技术与工程》
北大核心
2024年第4期1411-1417,共7页
Science Technology and Engineering
基金
国家自然科学基金(61977039)。
