Enhancing gesture decoding performance using signals from posterior parietal cortex: a stereo-electroencephalograhy (SEEG) study.
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TL;DR: This is the first human iEEG study demonstrating that PPC contains neural information about hand movement, supporting the role of PPC in hand shape encoding and suggesting that P PC could be a rich neural source for iEEg-based BMI.
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Abstract: Objective: Hand movement is a crucial function for humans' daily life. Developing brain-machine interface (BMI) to control a robotic hand by brain signals would help the severely paralyzed people partially regain the functional independence. Previous intracranial electroencephalography (iEEG)-based BMIs towards gesture decoding mostly used neural signals from the primary sensorimotor cortex while ignoring the hand movement related signals from posterior parietal cortex (PPC). Here, we propose combining iEEG recordings from PPC with that from primary sensorimotor cortex to enhance the gesture decoding performance of iEEG-based BMI. Approach: Stereoelectroencephalography (SEEG) signals from 25 epilepsy subjects were recorded when they performed a three-class hand gesture task. Across all 25 subjects, we identified 524, 114 and 221 electrodes from three regions of interest (ROIs), including PPC, postcentral cortex (POC) and precentral cortex (PRC), respectively. Based on the time-varying high gamma power (55-150 Hz) of SEEG signal, both the general activation in the task and the fine selectivity to gestures of each electrode in these ROIs along time was evaluated by the coefficient of determination r2. According to the activation along time, we further assessed the first activation time of each ROI. Finally, the decoding accuracy for gestures was obtained by linear support vector machine classifier to comparatively explore if the PPC will assist PRC and POC for gesture decoding. Main Results: We find that a majority(L: >60%, R: >40%) of electrodes in all the three ROIs present significant activation during the task. A large scale temporal activation sequence exists among the ROIs, where PPC activates first, PRC second and POC last. Among the activated electrodes, 15% (PRC), 26% (POC) and 4% (left PPC) of electrodes are significantly selective to gestures. Moreover, decoding accuracy obtained by combining the selective electrodes from three ROIs together is 5%, 3.6%, and 8% higher than that from only PRC and POC when decoding features across, before, and after the movement onset, were used. Significance: This is the first human iEEG study demonstrating that PPC contains neural information about fine hand movement, supporting the role of PPC in hand shape encoding. Combing PPC with primary sensorimotor cortex can provide more information to improve the gesture decoding performance. Our results suggest that PPC could be a rich neural source for iEEG-based BMI. Our findings also demonstrate the early involvement of human PPC in visuomotor task and thus may provide additional implications for further scientific research and BMI applications.
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Citations
Decoding continuous kinetic information of grasp from stereo-electroencephalographic (SEEG) recordings
Xiaolong Wu,Guangye Li,Shize Jiang,Scott Wellington,Shengjie Liu,Zehan Wu,Benjamin Metcalfe,Liang Chen,Dingguo Zhang +8 more
TL;DR: The result presented in this study demonstrated the potential of SEEG recordings for future BCI application and verified the possibility of decoding continuously changing grasp force using SEEg recordings.
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Investigating Data Cleaning Methods to Improve Performance of Brain-Computer Interfaces Based on Stereo-Electroencephalography.
TL;DR: In this article, five different data cleaning methods, including common average reference, gray-white matter reference, electrode shaft reference, bipolar reference, and Laplacian reference, were adopted to process the SEEG data and evaluated the effect of these methods on improving BCI decoding performance.
Deep Learning With Convolutional Neural Networks for Motor Brain-Computer Interfaces Based on Stereo-Electroencephalography (SEEG)
TL;DR: In this paper , an evaluation is performed on the decoding performance of deep learning methods on SEEG signals, and several methods, including filter bank common spatial pattern (FBCSP), shallow and deep CNN, ResNet, and a deep CNN variant named STSCNN, were used to classify the SEEG data.
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How Does Artificial Intelligence Contribute to iEEG Research?
Julia Berezutskaya,Anne-Lise Saive,Karim Jerbi,Marcel van Gerven +3 more
TL;DR: AI contributes to iEEG research by enabling the development of computational models for neuroscience and applied neurotechnology applications.
A Review of Motor Brain-Computer Interfaces using Intracranial Electroencephalography based on Surface Electrodes and Depth Electrodes
Xiaolong Wu,Benjamin Metcalfe,Shenghong He,Huiling Tan,Dingguo Zhang +4 more
TL;DR: This review focused on BCIs research using surface (ECoG) and depth electrodes (including SEEG, and DBS electrodes) for movement decoding on human subjects and demonstrated a distributed motor-related network that spanned multiple brain regions.
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