TY - GEN
T1 - Investigation of the Influence of ECoG Grid Spatial Density on Decoding Hand Flexion and Extension
AU - Jiang, Tianxiao
AU - Jiang, Tao
AU - Wang, Taylor
AU - Mei, Shanshan
AU - Liu, Qingzhu
AU - Li, Yunlin
AU - Wang, Xiaofei
AU - Prabhu, Sujit
AU - Sha, Zhiyi
AU - Ince, Nuri F.
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/10/26
Y1 - 2018/10/26
N2 - Electrocorticogram (ECoG) has been used as a reliable modality to control a brain machine interface (BMI). Recently, promising results of high-density ECoG have shown that non redundant information can be recorded with finer spatial resolution from the cortical surface. In this study, highdensity ECoG was recorded intraoperatively from two patients during awake brain surgery while performing instructed hand flexion and extension. Event related desynchronization (ERD) were found in the low frequency band (LFB: 8-32 Hz) band while event related synchronization (ERS) were found in the high frequency band (HFB: 60-200 Hz). The classification between hand flexion and extension was performed by using common spatial pattern (CSP) as a feature extraction technique and linear discriminant analysis (LDA) as a classifier. In order to compare the high-density ECoG and normal ECoG in terms of classifying between hand flexion and extension, we simulated a typical clinical ECoG (8 mm spacing) by averaging the neural activity of nearest four channels. The same classification methods were applied on the averaged recordings. In HFB, the classification error rate using simulated ECoG greatly increased and lagged the movement onset compared to the original highdensity ECoG. In LFB, the differences between them were not prominent. These results indicated that high-density ECoG is able to capture non-redundant task-related information from the motor cortex and potentially serves as a better modality to drive a neural prosthetic compared to typical clinical electrodes.
AB - Electrocorticogram (ECoG) has been used as a reliable modality to control a brain machine interface (BMI). Recently, promising results of high-density ECoG have shown that non redundant information can be recorded with finer spatial resolution from the cortical surface. In this study, highdensity ECoG was recorded intraoperatively from two patients during awake brain surgery while performing instructed hand flexion and extension. Event related desynchronization (ERD) were found in the low frequency band (LFB: 8-32 Hz) band while event related synchronization (ERS) were found in the high frequency band (HFB: 60-200 Hz). The classification between hand flexion and extension was performed by using common spatial pattern (CSP) as a feature extraction technique and linear discriminant analysis (LDA) as a classifier. In order to compare the high-density ECoG and normal ECoG in terms of classifying between hand flexion and extension, we simulated a typical clinical ECoG (8 mm spacing) by averaging the neural activity of nearest four channels. The same classification methods were applied on the averaged recordings. In HFB, the classification error rate using simulated ECoG greatly increased and lagged the movement onset compared to the original highdensity ECoG. In LFB, the differences between them were not prominent. These results indicated that high-density ECoG is able to capture non-redundant task-related information from the motor cortex and potentially serves as a better modality to drive a neural prosthetic compared to typical clinical electrodes.
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U2 - 10.1109/EMBC.2018.8513008
DO - 10.1109/EMBC.2018.8513008
M3 - Conference contribution
C2 - 30441039
AN - SCOPUS:85056620320
T3 - Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
SP - 3052
EP - 3055
BT - 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018
Y2 - 18 July 2018 through 21 July 2018
ER -