Reconstruction of reaching movement trajectories using electrocorticographic signals in humans.

Omid Talakoub,Willy Wong,Milos R Popovic,Clement Hamani,Jessie Navarro,Cesar Marquez-Chin,Erich T Fonoff,Dingguo Zhang

doi:10.1371/journal.pone.0182542

Abstract

In this study, we used electrocorticographic (ECoG) signals to extract the onset of arm movement as well as the velocity of the hand as a function of time. ECoG recordings were obtained from three individuals while they performed reaching tasks in the left, right and forward directions. The ECoG electrodes were placed over the motor cortex contralateral to the moving arm. Movement onset was detected from gamma activity with near perfect accuracy (> 98%), and a multiple linear regression model was used to predict the trajectory of the reaching task in three-dimensional space with an accuracy exceeding 85%. An adaptive selection of frequency bands was used for movement classification and prediction. This demonstrates the efficacy of developing a real-time brain-machine interface for arm movements with as few as eight ECoG electrodes.

Highlights

A brain-machine interface uses brain signals as a means of controlling external devices
The event-related desynchronization (ERD)/event-related synchronization (ERS) results are shown in Table 1 where it is evident that the centre frequency and the magnitude of power changes are different for each participant
The frequency bands over which maximal ERD/ERS was found were different for each participant

Summary

Introduction

A brain-machine interface (or BMI) uses brain signals as a means of controlling external devices. The scientific community has focused considerable efforts in developing BMI systems that would allow a person with an amputation or with a high-level spinal cord injury to control complex prosthetic devices or multi-degree-of-freedom robotic arms [1,2,3,4,5].To generate control commands for such a device, it is necessary to detect several control parameters These parameters can include: (i) movement onset; (ii) direction of movement; (iii) desired end-position of the arm and/or hand; (iv) position over time; (v) velocity; and (vi) grasping posture to be performed once hand reaches the object.

Objectives

Methods

Results

Discussion

Conclusion