Li Bowen, Zhang Shangen, Hu Yijun, Lin Yanfei, Gao Xiaorong
School of Medicine, Tsinghua University, Beijing 100084, People's Republic of China.
School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, People's Republic of China.
J Neural Eng. 2023 Feb 22;20(1). doi: 10.1088/1741-2552/acb96f.
Brain-computer interface (BCI) system has developed rapidly in the past decade. And rapid serial visual presentation (RSVP) is an important BCI paradigm to detect the targets in high-speed image streams. For decoding electroencephalography (EEG) in RSVP task, the ensemble-model methods have better performance than the single-model ones.This study proposed a method based on ensemble learning to extract discriminant information of EEG. An extreme gradient boosting framework was utilized to sequentially generate the sub models, including one global spatial-temporal filter and a group of local ones. EEG was reshaped into a three-dimensional form by remapping the electrode dimension into a 2D array to learn the spatial-temporal features from real local space.A benchmark RSVP EEG dataset was utilized to evaluate the performance of the proposed method, where EEG data of 63 subjects were analyzed. Compared with several state-of-the-art methods, the spatial-temporal patterns of proposed method were more consistent with P300, and the proposed method can provide significantly better classification performance.The ensemble model in this study was end-to-end optimized, which can avoid error accumulation. The sub models optimized by gradient boosting theory can extract discriminant information complementarily and non-redundantly.
脑机接口（BCI）系统在过去十年中发展迅速。快速序列视觉呈现（RSVP）是一种在高速图像流中检测目标的重要BCI范式。对于在RSVP任务中解码脑电图（EEG），集成模型方法比单模型方法具有更好的性能。本研究提出了一种基于集成学习的方法来提取EEG的判别信息。利用极端梯度提升框架顺序生成子模型，包括一个全局时空滤波器和一组局部时空滤波器。通过将电极维度重新映射为二维数组，将EEG重塑为三维形式，以便从真实局部空间中学习时空特征。使用一个基准RSVP EEG数据集来评估所提方法的性能，其中分析了63名受试者的EEG数据。与几种最先进的方法相比，所提方法的时空模式与P300更一致，并且所提方法能够提供显著更好的分类性能。本研究中的集成模型进行了端到端优化，可避免误差累积。通过梯度提升理论优化的子模型能够互补且无冗余地提取判别信息。
