Cui Yujie, Xie Songyun, Xie Xinzhou, Duan Xu, Gao Chuanlin
NPU-TUB Joint Laboratory for Neural informatics, School of Electronics and Information, Northwestern Polytechnical University, Xi'an 710129, P. R. China.
Sheng Wu Yi Xue Gong Cheng Xue Za Zhi. 2022 Feb 25;39(1):39-46. doi: 10.7507/1001-5515.202104049.
Rapid serial visual presentation-brain computer interface (RSVP-BCI) is the most popular technology in the early discover task based on human brain. This algorithm can obtain the rapid perception of the environment by human brain. Decoding brain state based on single-trial of multichannel electroencephalogram (EEG) recording remains a challenge due to the low signal-to-noise ratio (SNR) and nonstationary. To solve the problem of low classification accuracy of single-trial in RSVP-BCI, this paper presents a new feature extraction algorithm which uses principal component analysis (PCA) and common spatial pattern (CSP) algorithm separately in spatial domain and time domain, creating a spatial-temporal hybrid CSP-PCA (STHCP) algorithm. By maximizing the discrimination distance between target and non-target, the feature dimensionality was reduced effectively. The area under the curve (AUC) of STHCP algorithm is higher than that of the three benchmark algorithms (SWFP, CSP and PCA) by 17.9%, 22.2% and 29.2%, respectively. STHCP algorithm provides a new method for target detection.
快速序列视觉呈现-脑机接口（RSVP-BCI）是早期基于人脑的发现任务中最流行的技术。该算法能够使人脑快速感知环境。基于多通道脑电图（EEG）单次试验记录来解码脑状态，由于信噪比（SNR）低且信号不稳定，仍然是一项挑战。为了解决RSVP-BCI中单次试验分类准确率低的问题，本文提出了一种新的特征提取算法，该算法在空间域和时间域分别使用主成分分析（PCA）和共同空间模式（CSP）算法，创建了一种时空混合CSP-PCA（STHCP）算法。通过最大化目标与非目标之间的判别距离，有效地降低了特征维度。STHCP算法的曲线下面积（AUC）分别比三种基准算法（SWFP、CSP和PCA）高17.9%、22.2%和29.2%。STHCP算法为目标检测提供了一种新方法。
