In this study human motor cortical activity was recorded with a customized micro-ECoG grid during individual finger movements. The quality of the recorded neural signals was characterized in the frequency domain from three different perspectives: (1) coherence between neural signals recorded from different electrodes, (2) modulation of neural signals by finger movement, and (3) accuracy of finger movement decoding. It was found that, for the high frequency band (60–120 Hz), coherence between neighboring micro-ECoG electrodes was 0.3. In addition, the high frequency band showed significant modulation by finger movement both temporally and spatially, and a classification accuracy of 73% (chance level: 20%) was achieved for individual finger movement using neural signals recorded from the micro-ECoG grid. These results suggest that the micro-ECoG grid presented here offers sufficient spatial and temporal resolution for the development of minimally-invasive brain-computer interface applications.

This paper presents a fuzzy logic model to decode the hand posture from electro-cortico graphic (ECoG) activity of the motor cortical areas. One subject was implanted with a micro-ECoG electrode array on the surface of the motor cortex. Neural signals were recorded from 14 electrodes on this array while Subject participated in three reach and grasp sessions. In each session, Subject reached and grasped a wooden toy hammer for five times. Optimal channels/electrodes which were active during the task were selected. Power spectral densities of optimal channels averaged over a time period of 1/2 second before the onset of the movement and 1 second after the onset of the movement were fed into a fuzzy logic model. This model decoded whether the posture of the hand is open or closed with 80% accuracy. Hand postures along the task time were decoded by using the output from the fuzzy logic model by two methods (i) velocity based decoding (ii) acceleration based decoding. The latter performed better when hand postures predicted by the model were compared to postures recorded by a data glove during the experiment. This fuzzy logic model was imported to MATLABSIMULINK to control a virtual hand.