Investigation of the Influence of ECoG Grid Spatial Density on Decoding Hand Flexion and Extension

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.