Liu Shengjie, Li Guangye, Jiang Shize, Wu Xiaolong, Hu Jie, Zhang Dingguo, Chen Liang
State Key Laboratory of Mechanical Systems and Vibrations, Institute of Robotics, Shanghai Jiao Tong University, Shanghai, China.
Department of Neurosurgery of Huashan Hospital, Fudan University, Shanghai, China.
Front Neurosci. 2021 Oct 6;15:725384. doi: 10.3389/fnins.2021.725384. eCollection 2021.
Stereo-electroencephalography (SEEG) utilizes localized and penetrating depth electrodes to directly measure electrophysiological brain activity. The implanted electrodes generally provide a sparse sampling of multiple brain regions, including both cortical and subcortical structures, making the SEEG neural recordings a potential source for the brain-computer interface (BCI) purpose in recent years. For SEEG signals, data cleaning is an essential preprocessing step in removing excessive noises for further analysis. However, little is known about what kinds of effect that different data cleaning methods may exert on BCI decoding performance and, moreover, what are the reasons causing the differentiated effects. To address these questions, we adopted five different data cleaning methods, including common average reference, gray-white matter reference, electrode shaft reference, bipolar reference, and Laplacian reference, to process the SEEG data and evaluated the effect of these methods on improving BCI decoding performance. Additionally, we also comparatively investigated the changes of SEEG signals induced by these different methods from multiple-domain (e.g., spatial, spectral, and temporal domain). The results showed that data cleaning methods could improve the accuracy of gesture decoding, where the Laplacian reference produced the best performance. Further analysis revealed that the superiority of the data cleaning method with excellent performance might be attributed to the increased distinguishability in the low-frequency band. The findings of this work highlighted the importance of applying proper data clean methods for SEEG signals and proposed the application of Laplacian reference for SEEG-based BCI.
立体脑电图（SEEG）利用局部穿透深度电极直接测量脑电生理活动。植入电极通常对包括皮质和皮质下结构在内的多个脑区进行稀疏采样，这使得SEEG神经记录近年来成为脑机接口（BCI）的潜在信号源。对于SEEG信号而言，数据清理是去除过多噪声以便进一步分析的关键预处理步骤。然而，对于不同的数据清理方法可能对BCI解码性能产生何种影响，以及造成这些差异影响的原因是什么，人们却知之甚少。为了解决这些问题，我们采用了五种不同的数据清理方法，包括公共平均参考、灰质-白质参考、电极轴参考、双极参考和拉普拉斯参考，来处理SEEG数据，并评估这些方法对提高BCI解码性能的效果。此外，我们还从多个域（如空间、频谱和时间域）比较研究了这些不同方法所引起的SEEG信号变化。结果表明，数据清理方法能够提高手势解码的准确性，其中拉普拉斯参考的性能最佳。进一步分析表明，性能优异的数据清理方法的优势可能归因于低频带可区分性的增加。这项工作的研究结果凸显了对SEEG信号应用适当数据清理方法的重要性，并提出了将拉普拉斯参考应用于基于SEEG的BCI。
