Shen Lili, Liu Zhijian, Li Yueping
School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China.
School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China.
J Neurosci Methods. 2020 May 1;337:108650. doi: 10.1016/j.jneumeth.2020.108650. Epub 2020 Mar 3.
Stereopsis plays a vital role in many aspects of human daily life. Random-dot stereogram (RDS) is often used to detect stereoacuity and perform research on visual cognition. Electroencephalogram (EEG) is one of the commonly adopted visual cognition techniques due to its noninvasive collection.
In this study, a methodology named WPT-BED based on wavelet packet transform (WPT) and bispectral eigenvalues of differential signals (BED) is proposed, which can classify the three-pattern EEG signals evoked by dynamic RDS (DRDS). Specifically, the signals are decomposed into different frequency bands by WPT. The appropriate sub-bands are selected for reconstruction. Finally, the optimized bispectrum features are extracted for classification to achieve higher accuracy.
The classification performance of the proposed method in different periods of signal processing are investigated. The method WPT-BED has the highest classification accuracy 84.38%, and the average classification accuracy is 73.98%. The active channels with higher accuracy are focused on the visual pathway in the human cerebral cortex.
Comparison with other methods for EEG signals classification is performed to identify the effectiveness of the proposed methodology.
The proposed methodology can effectively distinguish the EEG signals evoked by DRDS. It demonstrates the feasibility of DRDS recognition based on EEG.
立体视觉在人类日常生活的许多方面都起着至关重要的作用。随机点立体图（RDS）常用于检测立体视敏度并进行视觉认知研究。脑电图（EEG）因其非侵入性采集而成为常用的视觉认知技术之一。
在本研究中，提出了一种基于小波包变换（WPT）和差分信号双谱特征值（BED）的名为WPT - BED的方法，该方法可对动态随机点立体图（DRDS）诱发的三种模式的脑电信号进行分类。具体而言，通过小波包变换将信号分解为不同频段。选择合适的子频段进行重构。最后，提取优化后的双谱特征进行分类以实现更高的准确率。
研究了所提方法在不同信号处理阶段的分类性能。WPT - BED方法具有最高分类准确率84.38%，平均分类准确率为73.98%。准确率较高的活跃通道集中在人类大脑皮层的视觉通路上。
与其他脑电信号分类方法进行比较，以确定所提方法的有效性。
所提方法能够有效区分动态随机点立体图诱发的脑电信号。它证明了基于脑电图进行动态随机点立体图识别的可行性。
