TY - JOUR
T1 - Investigating Data Cleaning Methods to Improve Performance of Brain–Computer Interfaces Based on Stereo-Electroencephalography
AU - Liu, Shengjie
AU - Li, Guangye
AU - Jiang, Shize
AU - Wu, Xiaolong
AU - Hu, Jie
AU - Zhang, Dingguo
AU - Chen, Liang
N1 - 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
PY - 2021/10/6
Y1 - 2021/10/6
N2 - 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.
AB - 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.
KW - brain–computer interface
KW - data cleaning
KW - gesture decoding
KW - re-referencing method
KW - stereo-electroencephalography
UR - http://www.scopus.com/inward/record.url?scp=85117482948&partnerID=8YFLogxK
U2 - 10.3389/fnins.2021.725384
DO - 10.3389/fnins.2021.725384
M3 - Article
AN - SCOPUS:85117482948
VL - 15
JO - Frontiers in Neuroscience
JF - Frontiers in Neuroscience
SN - 1662-4548
M1 - 725384
ER -