Xiong Xiong, Wang Ying, Song Tianyuan, Huang Jinguo, Kang Guixia
School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing, China.
Department of Neuroscience, University of Sheffield, Sheffield, United Kingdom.
Front Neurosci. 2023 Dec 13;17:1303648. doi: 10.3389/fnins.2023.1303648. eCollection 2023.
As a typical self-paced brain-computer interface (BCI) system, the motor imagery (MI) BCI has been widely applied in fields such as robot control, stroke rehabilitation, and assistance for patients with stroke or spinal cord injury. Many studies have focused on the traditional spatial filters obtained through the common spatial pattern (CSP) method. However, the CSP method can only obtain fixed spatial filters for specific input signals. In addition, the CSP method only focuses on the variance difference of two types of electroencephalogram (EEG) signals, so the decoding ability of EEG signals is limited.
To make up for these deficiencies, this study introduces a novel spatial filter-solving paradigm named adaptive spatial pattern (ASP), which aims to minimize the energy intra-class matrix and maximize the inter-class matrix of MI-EEG after spatial filtering. The filter bank adaptive and common spatial pattern (FBACSP), our proposed method for MI-EEG decoding, amalgamates ASP spatial filters with CSP features across multiple frequency bands. Through a dual-stage feature selection strategy, it employs the Particle Swarm Optimization algorithm for spatial filter optimization, surpassing traditional CSP approaches in MI classification. To streamline feature sets and enhance recognition efficiency, it first prunes CSP features in each frequency band using mutual information, followed by merging these with ASP features.
Comparative experiments are conducted on two public datasets (2a and 2b) from BCI competition IV, which show the outstanding average recognition accuracy of FBACSP. The classification accuracy of the proposed method has reached 74.61 and 81.19% on datasets 2a and 2b, respectively. Compared with the baseline algorithm, filter bank common spatial pattern (FBCSP), the proposed algorithm improves by 11.44 and 7.11% on two datasets, respectively ( < 0.05).
It is demonstrated that FBACSP has a strong ability to decode MI-EEG. In addition, the analysis based on mutual information, t-SNE, and Shapley values further proves that ASP features have excellent decoding ability for MI-EEG signals and explains the improvement of classification performance by the introduction of ASP features. These findings may provide useful information to optimize EEG-based BCI systems and further improve the performance of non-invasive BCI.
作为一种典型的自定节奏脑机接口（BCI）系统，运动想象（MI）脑机接口已广泛应用于机器人控制、中风康复以及对中风或脊髓损伤患者的辅助等领域。许多研究都集中在通过共同空间模式（CSP）方法获得的传统空间滤波器上。然而，CSP方法只能针对特定输入信号获得固定的空间滤波器。此外，CSP方法仅关注两种脑电图（EEG）信号的方差差异，因此EEG信号的解码能力有限。
为弥补这些不足，本研究引入了一种名为自适应空间模式（ASP）的新型空间滤波器求解范式，其目的是在空间滤波后最小化MI-EEG的类内能量矩阵并最大化类间矩阵。我们提出的用于MI-EEG解码的方法，滤波器组自适应与共同空间模式（FBACSP），将ASP空间滤波器与多个频带的CSP特征相结合。通过双阶段特征选择策略，它采用粒子群优化算法进行空间滤波器优化，在MI分类方面超越了传统的CSP方法。为了简化特征集并提高识别效率，它首先使用互信息对每个频带中的CSP特征进行修剪，然后将这些特征与ASP特征合并。
在BCI竞赛IV的两个公共数据集（2a和2b）上进行了对比实验，结果显示FBACSP具有出色的平均识别准确率。所提方法在数据集2a和2b上的分类准确率分别达到了74.61%和81.19%。与基线算法滤波器组共同空间模式（FBCSP）相比，所提算法在两个数据集上分别提高了11.44%和7.11%（<0.05）。
结果表明FBACSP具有很强的MI-EEG解码能力。此外，基于互信息、t-SNE和Shapley值的分析进一步证明了ASP特征对MI-EEG信号具有出色的解码能力，并解释了引入ASP特征后分类性能的提升。这些发现可能为优化基于EEG的BCI系统提供有用信息，并进一步提高非侵入性BCI的性能。
