Investigating Data Cleaning Methods to Improve Performance of Brain–Computer Interfaces Based on Stereo-Electroencephalography
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 SEE...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2021-10-01
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| Series: | Frontiers in Neuroscience |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fnins.2021.725384/full |
| _version_ | 1818358012446769152 |
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| author | Shengjie Liu Guangye Li Shize Jiang Xiaolong Wu Jie Hu Dingguo Zhang Liang Chen |
| author_facet | Shengjie Liu Guangye Li Shize Jiang Xiaolong Wu Jie Hu Dingguo Zhang Liang Chen |
| author_sort | Shengjie Liu |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-13T20:22:14Z |
| format | Article |
| id | doaj.art-fed8c3b1d1554952a14f3550c0ec8d26 |
| institution | Directory Open Access Journal |
| issn | 1662-453X |
| language | English |
| last_indexed | 2024-12-13T20:22:14Z |
| publishDate | 2021-10-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neuroscience |
| spelling | doaj.art-fed8c3b1d1554952a14f3550c0ec8d262022-12-21T23:32:40ZengFrontiers Media S.A.Frontiers in Neuroscience1662-453X2021-10-011510.3389/fnins.2021.725384725384Investigating Data Cleaning Methods to Improve Performance of Brain–Computer Interfaces Based on Stereo-ElectroencephalographyShengjie Liu0Guangye Li1Shize Jiang2Xiaolong Wu3Jie Hu4Dingguo Zhang5Liang Chen6State Key Laboratory of Mechanical Systems and Vibrations, Institute of Robotics, Shanghai Jiao Tong University, Shanghai, ChinaState Key Laboratory of Mechanical Systems and Vibrations, Institute of Robotics, Shanghai Jiao Tong University, Shanghai, ChinaDepartment of Neurosurgery of Huashan Hospital, Fudan University, Shanghai, ChinaDepartment of Electronic and Electrical Engineering, University of Bath, Bath, United KingdomDepartment of Neurosurgery of Huashan Hospital, Fudan University, Shanghai, ChinaDepartment of Electronic and Electrical Engineering, University of Bath, Bath, United KingdomDepartment of Neurosurgery of Huashan Hospital, Fudan University, Shanghai, ChinaStereo-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.https://www.frontiersin.org/articles/10.3389/fnins.2021.725384/fullbrain–computer interfacestereo-electroencephalographydata cleaningre-referencing methodgesture decoding |
| spellingShingle | Shengjie Liu Guangye Li Shize Jiang Xiaolong Wu Jie Hu Dingguo Zhang Liang Chen Investigating Data Cleaning Methods to Improve Performance of Brain–Computer Interfaces Based on Stereo-Electroencephalography Frontiers in Neuroscience brain–computer interface stereo-electroencephalography data cleaning re-referencing method gesture decoding |
| title | Investigating Data Cleaning Methods to Improve Performance of Brain–Computer Interfaces Based on Stereo-Electroencephalography |
| title_full | Investigating Data Cleaning Methods to Improve Performance of Brain–Computer Interfaces Based on Stereo-Electroencephalography |
| title_fullStr | Investigating Data Cleaning Methods to Improve Performance of Brain–Computer Interfaces Based on Stereo-Electroencephalography |
| title_full_unstemmed | Investigating Data Cleaning Methods to Improve Performance of Brain–Computer Interfaces Based on Stereo-Electroencephalography |
| title_short | Investigating Data Cleaning Methods to Improve Performance of Brain–Computer Interfaces Based on Stereo-Electroencephalography |
| title_sort | investigating data cleaning methods to improve performance of brain computer interfaces based on stereo electroencephalography |
| topic | brain–computer interface stereo-electroencephalography data cleaning re-referencing method gesture decoding |
| url | https://www.frontiersin.org/articles/10.3389/fnins.2021.725384/full |
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