Paper Info
Title
A novel Morse code-inspired method for multiclass motor imagery brain–computer interface (BCI) design
Abstract
Motor imagery (MI)-based brain–computer interfaces (BCIs) allow disabled individuals to control external devices voluntarily, helping us to restore lost motor functions. However, the number of control commands available in MI-based BCIs remains limited, limiting the usability of BCI systems in control applications involving multiple degrees of freedom (DOF), such as control of a robot arm. To address this problem, we developed a novel Morse code-inspired method for MI-based BCI design to increase the number of output commands. Using this method, brain activities are modulated by sequences of MI (sMI) tasks, which are constructed by alternately imagining movements of the left or right hand or no motion. The codes of the sMI task was detected from EEG signals and mapped to special commands. According to permutation theory, an sMI task with N-length allows 2×(2N−1) possible commands with the left and right MI tasks under self-paced conditions. To verify its feasibility, the new method was used to construct a six-class BCI system to control the arm of a humanoid robot. Four subjects participated in our experiment and the averaged accuracy of the six-class sMI tasks was 89.4%. The Cohen's kappa coefficient and the throughput of our BCI paradigm are 0.88±0.060 and 23.5bits per minute (bpm), respectively. Furthermore, all of the subjects could operate an actual three-joint robot arm to grasp an object in around 49.1s using our approach. These promising results suggest that the Morse code-inspired method could be used in the design of BCIs for multi-DOF control.
Year
DOI
Venue
2015
10.1016/j.compbiomed.2015.08.011
Computers in Biology and Medicine
Keywords
Field
DocType
Electroencephalogram (EEG),Brain–computer interface (BCI),Morse code,Sequential motor imagery,Multi-DOF robot arm control
Computer vision,Robotic arm,GRASP,Controllability,Computer science,Motor skill,Brain–computer interface,Artificial intelligence,Robotics,Motor imagery,Humanoid robot
Journal
Volume
Issue
ISSN
66
C
0010-4825
Citations 
PageRank 
References 
8
0.61
11
Authors
6
Name
Order
Citations
PageRank
Jun Jiang1605.05
Zongtan Zhou241233.89
Erwei Yin31109.12
Yang Yu4191.91
Yadong Liu510514.04
Dewen Hu61290101.20