A Spatio-Temporal Method for Extracting Gamma-Band Features to Enhance Classification in a Rapid Serial Visual Presentation Task

Ping Xie, Shencai Hao, Jing Zhao*, Zhenhu Liang, Xiaoli Li

Abstract

Rapid serial visual presentation (RSVP) is a type of electroencephalogram (EEG) pattern commonly used for target recognition. Besides delta- and theta-band responses already used for classification, RSVP task also evokes gamma-band responses having low amplitude and large individual difference. This paper proposes a filter bank spatio-temporal component analysis (FBSCA) method, extracting spatio-temporal features of the gamma-band responses for the first time, to enhance the RSVP classification performance. Considering the individual difference in time latency and responsive frequency, the proposed FBSCA method decomposes the gamma-band EEG data into sub-components in different time–frequency-space domains and seeks the weight coefficients to optimize the combinations of electrodes, common spatial pattern (CSP) components, time windows and frequency bands. Two state-of-the-art methods, i.e. hierarchical discriminant principal component analysis (HDPCA) and discriminative canonical pattern matching (DCPM), were used for comparison. The performance was evaluated in 10×10 cross validations using a public dataset. Study results showed that the FBSCA method outperformed the other methods regardless of number of training trials. These results suggest that the proposed FBSCA method can enhance the RSVP classification.

摘要

快速连续视觉呈现（RSVP）是一种脑电图（EEG）模式，常用于目标识别。除了用于分类的 delta 波段和 theta 波段反应外，RSVP 任务还会引起振幅较低且个体差异较大的 gamma 波段反应。本文提出了一种滤波器组时空分量分析（FBSCA）方法，首次提取了伽玛波段响应的时空特征，以提高 RSVP 的分类性能。考虑到时间延迟和响应频率的个体差异，所提出的 FBSCA 方法将伽马波段脑电图数据分解为不同时频空间域的子分量，并寻求权重系数以优化电极、共同空间模式（CSP）分量、时间窗和频段的组合。比较中使用了两种最先进的方法，即分层判别主成分分析法（HDPCA）和判别典型模式匹配法（DCPM）。在 10×10使用公共数据集进行交叉验证。研究结果表明，无论训练次数多少，FBSCA 方法都优于其他方法。这些结果表明，所提出的 FBSCA 方法可以增强 RSVP 分类能力。