Identification of arm movements using correlation of electrocorticographic spectral components and kinematic recordings

Abstract

The purpose of this study was to explore the possibility of using electrocorticographic (ECoG) recordings from subdural electrodes placed over the motor cortex to identify the upper limb motion performed by a human subject. More specifically, we were trying to identify features in the ECoG signals that could help us determine the type of movement performed by an individual. Two subjects who had subdural electrodes implanted over the motor cortex were asked to perform various motor tasks with the upper limb contralateral to the site of electrode implantation. ECoG signals and upper limb kinematics were recorded while the participants were performing the movements. ECoG frequency components were identified that correlated well with the performed movements measured along 6D coordinates (X, Y, Z, roll, yaw and pitch). These frequencies were grouped using histograms. The resulting histograms had consistent and unique shapes that were representative of individual upper limb movements performed by the participants. Thus, it was possible to identify which movement was performed by the participant without prior knowledge of the arm and hand kinematics. To confirm these findings, a nearest neighbour classifier was applied to identify the specific movement that each participant had performed. The achieved classification accuracy was 89%.

Control of a neuroprosthesis for grasping using off-line classification of electrocorticographic signals: case study

“…The ECoG signals were downsampled to 200 samples per second and subtracted from each other to create differential signals using the recordings from non-adjacent electrodes (for example, contacts 3 and 1). 17 Using only the right upper limb, subject 1 performed wrist flexion, reaching to the right and reaching to the left movements after an auditory cue and held the final position of the movement until a second auditory cue. A motion sensor was placed over the dorsal aspect of the third metacarpal of the right hand to record the movement using a six-dimensional (X, Y, Z, roll, yaw and pitch) motioncapture system (Fastrak, Polhemus Inc., Colchester, VT, USA).…”

“…Only position recordings (X, Y and Z) were used for this study. 17 Each movement was repeated at least 35 times, and each trial was visually inspected to identify mistrials, defined as: (1) a trial in which the individual had performed a movement different from what had been instructed; (2) the participant had started to move before the auditory cue; or (3) the movement was not completed. Table 1 and Figure 2 show the movements performed along with the number of trials used to carry out this study.…”

Prediction of Three-Dimensional Arm Trajectories Based on ECoG Signals Recorded from Human Sensorimotor Cortex

“…Researchers have been applying ECoG in humans for several years now and in numerous applications. The classification of hand movement directions or grasp types [28]–[33], one-, two-, or three-dimensional cursor control [27], [34]–[40], and prediction of finger flexion [41] are just some examples of ECoG applications in human patients. Studies concerning the prediction of three-dimensional (3D) trajectory or muscle activities from primate ECoG have shown outstanding results [42]–[45].…”

State and trajectory decoding of upper extremity movements from electrocorticogram

“…Successful ECoG-based BCI systems require the ability to robustly decode movement kinematic parameters. Examples include the decoding of finger trajectories [1], [2], [3], [4], [5], [6], [7], elbow and hand movements [8], [9], and reaching directions [8], [10], [11], [12], [13].…”