Abstract
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.
Original language | English |
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Article number | 725384 |
Journal | Frontiers in Neuroscience |
Volume | 15 |
DOIs | |
Publication status | Published - 6 Oct 2021 |
Bibliographical note
Funding Information:This work was supported by grants from the National Natural Science Foundation of China (no. 91848112), China Post-doctoral Science Foundation (no. 20Z102060158), National Key R&D Program of China (2018YFB1307301), Medical and Engineering Cross Foundation of Shanghai Jiao Tong University (no. AH0200003), and the Science
Keywords
- brain–computer interface
- data cleaning
- gesture decoding
- re-referencing method
- stereo-electroencephalography
ASJC Scopus subject areas
- General Neuroscience