Assessing differential representation of hand movements in multiple domains using stereo-electroencephalographic recordings
Invasive brain-computer interfaces (BCI) have made great progress in the reconstruction of fine hand movement parameters for paralyzed patients, where superficial measurement modalities including electrocorticography (ECoG) and micro-array recordings are mostly used. However, these recording techniq...
| Main Authors: | , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2022-04-01
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| Series: | NeuroImage |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1053811922000982 |
| _version_ | 1819326789458067456 |
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| author | Guangye Li Shize Jiang Jianjun Meng Guohong Chai Zehan Wu Zhen Fan Jie Hu Xinjun Sheng Dingguo Zhang Liang Chen Xiangyang Zhu |
| author_facet | Guangye Li Shize Jiang Jianjun Meng Guohong Chai Zehan Wu Zhen Fan Jie Hu Xinjun Sheng Dingguo Zhang Liang Chen Xiangyang Zhu |
| author_sort | Guangye Li |
| collection | DOAJ |
| description | Invasive brain-computer interfaces (BCI) have made great progress in the reconstruction of fine hand movement parameters for paralyzed patients, where superficial measurement modalities including electrocorticography (ECoG) and micro-array recordings are mostly used. However, these recording techniques typically focus on the signals from the sensorimotor cortex, leaving subcortical regions and other cortical regions related to the movements largely unexplored. As an intracranial recording technique for the presurgical assessments of brain surgery, stereo-encephalography (SEEG) inserts depth electrodes containing multiple contacts into the brain and thus provides the unique opportunity for investigating movement-related neural representation throughout the brain. Although SEEG samples neural signals with high spatial-temporal resolutions, its potential of being used to build BCIs has just been realized recently, and the decoding of SEEG activity related to hand movements has not been comprehensively investigated yet. Here, we systematically evaluated the factors influencing the performance of movement decoding using SEEG signals recorded from 32 human subjects performing a visually-cued hand movement task. Our results suggest that multiple regions in both lateral and depth directions present significant neural selectivity to the task, whereas the sensorimotor area, including both precentral and postcentral cortex, carries the richest discriminative neural information for the decoding. The posterior parietal and prefrontal cortex contribute gradually less, but still rich sources for extracting movement parameters. The insula, temporal and occipital cortex also contains useful task-related information for decoding. Under the cortex layer, white matter presents decodable neural patterns but yields a lower accuracy (42.0 ± 0.8%) than the cortex on average (44.2 ± 0.8%, p<0.01). Notably, collectively using neural signals from multiple task-related areas can significantly enhance the movement decoding performance by 6.9% (p<0.01) on average compared to using a single region. Among the different spectral components of SEEG activity, the high gamma and delta bands offer the most informative features for hand movements reconstruction. Additionally, the phase-amplitude coupling strength between these two frequency ranges correlates positively with the performance of movement decoding. In the temporal domain, maximum decoding accuracy is first reached around 2 s after the onset of movement commands. In sum, this study provides valuable insights for the future motor BCIs design employing both SEEG recordings and other recording modalities. |
| first_indexed | 2024-12-24T13:00:32Z |
| format | Article |
| id | doaj.art-eed7101ee9704849b7c916c1cfaec3fe |
| institution | Directory Open Access Journal |
| issn | 1095-9572 |
| language | English |
| last_indexed | 2024-12-24T13:00:32Z |
| publishDate | 2022-04-01 |
| publisher | Elsevier |
| record_format | Article |
| series | NeuroImage |
| spelling | doaj.art-eed7101ee9704849b7c916c1cfaec3fe2022-12-21T16:54:09ZengElsevierNeuroImage1095-95722022-04-01250118969Assessing differential representation of hand movements in multiple domains using stereo-electroencephalographic recordingsGuangye Li0Shize Jiang1Jianjun Meng2Guohong Chai3Zehan Wu4Zhen Fan5Jie Hu6Xinjun Sheng7Dingguo Zhang8Liang Chen9Xiangyang Zhu10State Key Laboratory of Mechanical Systems and Vibrations, Shanghai Jiao Tong University, Shanghai, China; Institute of Robotics, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, ChinaDepartment of Neurosurgery of Huashan Hospital, Fudan University, 12 Wulumuqi Road, Jingan District, Shanghai 200040, ChinaState Key Laboratory of Mechanical Systems and Vibrations, Shanghai Jiao Tong University, Shanghai, China; Institute of Robotics, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, ChinaState Key Laboratory of Mechanical Systems and Vibrations, Shanghai Jiao Tong University, Shanghai, China; Institute of Robotics, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, ChinaDepartment of Neurosurgery of Huashan Hospital, Fudan University, 12 Wulumuqi Road, Jingan District, Shanghai 200040, ChinaDepartment of Neurosurgery of Huashan Hospital, Fudan University, 12 Wulumuqi Road, Jingan District, Shanghai 200040, ChinaDepartment of Neurosurgery of Huashan Hospital, Fudan University, 12 Wulumuqi Road, Jingan District, Shanghai 200040, ChinaState Key Laboratory of Mechanical Systems and Vibrations, Shanghai Jiao Tong University, Shanghai, China; Institute of Robotics, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, ChinaDepartment of Electronic and Electrical Engineering, University of Bath, Bath, UKCo-corresponding author.; Department of Neurosurgery of Huashan Hospital, Fudan University, 12 Wulumuqi Road, Jingan District, Shanghai 200040, ChinaCorresponding author at: Institute of Robotics, Shanghai Jiao Tong University, 800 Dongchuan Road, Minhang District, Shanghai 200240, China.; State Key Laboratory of Mechanical Systems and Vibrations, Shanghai Jiao Tong University, Shanghai, China; Institute of Robotics, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, ChinaInvasive brain-computer interfaces (BCI) have made great progress in the reconstruction of fine hand movement parameters for paralyzed patients, where superficial measurement modalities including electrocorticography (ECoG) and micro-array recordings are mostly used. However, these recording techniques typically focus on the signals from the sensorimotor cortex, leaving subcortical regions and other cortical regions related to the movements largely unexplored. As an intracranial recording technique for the presurgical assessments of brain surgery, stereo-encephalography (SEEG) inserts depth electrodes containing multiple contacts into the brain and thus provides the unique opportunity for investigating movement-related neural representation throughout the brain. Although SEEG samples neural signals with high spatial-temporal resolutions, its potential of being used to build BCIs has just been realized recently, and the decoding of SEEG activity related to hand movements has not been comprehensively investigated yet. Here, we systematically evaluated the factors influencing the performance of movement decoding using SEEG signals recorded from 32 human subjects performing a visually-cued hand movement task. Our results suggest that multiple regions in both lateral and depth directions present significant neural selectivity to the task, whereas the sensorimotor area, including both precentral and postcentral cortex, carries the richest discriminative neural information for the decoding. The posterior parietal and prefrontal cortex contribute gradually less, but still rich sources for extracting movement parameters. The insula, temporal and occipital cortex also contains useful task-related information for decoding. Under the cortex layer, white matter presents decodable neural patterns but yields a lower accuracy (42.0 ± 0.8%) than the cortex on average (44.2 ± 0.8%, p<0.01). Notably, collectively using neural signals from multiple task-related areas can significantly enhance the movement decoding performance by 6.9% (p<0.01) on average compared to using a single region. Among the different spectral components of SEEG activity, the high gamma and delta bands offer the most informative features for hand movements reconstruction. Additionally, the phase-amplitude coupling strength between these two frequency ranges correlates positively with the performance of movement decoding. In the temporal domain, maximum decoding accuracy is first reached around 2 s after the onset of movement commands. In sum, this study provides valuable insights for the future motor BCIs design employing both SEEG recordings and other recording modalities.http://www.sciencedirect.com/science/article/pii/S1053811922000982Stereo-electroencephalographySEEGMovement decodingBrain-computer interfaceNeural representation |
| spellingShingle | Guangye Li Shize Jiang Jianjun Meng Guohong Chai Zehan Wu Zhen Fan Jie Hu Xinjun Sheng Dingguo Zhang Liang Chen Xiangyang Zhu Assessing differential representation of hand movements in multiple domains using stereo-electroencephalographic recordings NeuroImage Stereo-electroencephalography SEEG Movement decoding Brain-computer interface Neural representation |
| title | Assessing differential representation of hand movements in multiple domains using stereo-electroencephalographic recordings |
| title_full | Assessing differential representation of hand movements in multiple domains using stereo-electroencephalographic recordings |
| title_fullStr | Assessing differential representation of hand movements in multiple domains using stereo-electroencephalographic recordings |
| title_full_unstemmed | Assessing differential representation of hand movements in multiple domains using stereo-electroencephalographic recordings |
| title_short | Assessing differential representation of hand movements in multiple domains using stereo-electroencephalographic recordings |
| title_sort | assessing differential representation of hand movements in multiple domains using stereo electroencephalographic recordings |
| topic | Stereo-electroencephalography SEEG Movement decoding Brain-computer interface Neural representation |
| url | http://www.sciencedirect.com/science/article/pii/S1053811922000982 |
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