A fuzzy logic model for hand posture control using human cortical activity recorded by micro-ECog electrodes

Abstract

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.

Progress in the Field of Micro-Electrocorticography

“…Micro-ECoG is becoming increasingly popular for its ability to provide higher temporal and spatial resolution than typical ECoG [6,17,32,33], often comparable to intracortical microelectrodes [33,34]. A major advantage is the smaller size of the electrode site, which allows for more precise and accurate readings and less invasive implantation than its ECoG predecessor [17,33,34].…”

Collaborative Approach in the Development of High‐Performance Brain–Computer Interfaces for a Neuroprosthetic Arm: Translation from Animal Models to Human Control

“…This work guided development of subsequent human study protocols and IDE applications. Finally, the EMU study generated critical pilot data and initial funding from multiple agencies that made it possible for us to form and grow our clinical BCI research team with a demonstrated track record. The EMU research conducted in patients undergoing clinical brain mapping in itself has important scientific, engineering, and clinical value as exemplified by the ever‐increasing number of research groups and publications based on this study setting …”

“…The EMU research conducted in patients undergoing clinical brain mapping in itself has important scientific, engineering, and clinical value as exemplified by the ever-increasing number of research groups and publications based on this study setting. [23][24][25][26][27][28][29][30][31][34][35][36][37][38][39][40][41][42][43]…”

Neural Interface Technology for Rehabilitation: Exploiting and Promoting Neuroplasticity

“…A micro-ECoG grid with 14 recording electrodes was implanted over the motor cortical area, and it was found that the high frequency band of neural signals recorded from this micro-ECoG grid showed significant modulation by hand movement, including individual finger movement 45 and grasping/pinching movement of the hand 46. Modulation of micro-ECoG signals by hand movement offers a neural substrate that is critical to the success of a BCI system aiming to restoring volitional control of finger movement.…”