TY - JOUR
T1 - Comparative analysis of features extracted from EEG spatial, spectral and temporal domains for binary and multiclass motor imagery classification
AU - Lee, Seung Bo
AU - Kim, Hyun Ji
AU - Kim, Hakseung
AU - Jeong, Ji Hoon
AU - Lee, Seong Whan
AU - Kim, Dong Joo
N1 - Funding Information:
This work was supported by Institute for Information & communications Technology Promotion(IITP) grant funded by the Korea government(MSIT) (No. 2017-0-00432, Development of non-invasive integrated BCI SW platform to control home appliances and external devices by user's thought via AR/VR interface).
Publisher Copyright:
© 2019 Elsevier Inc.
PY - 2019/10
Y1 - 2019/10
N2 - The electroencephalogram (EEG) remains the predominant source of neurophysiological signals for motor imagery-based brain-computer interfaces (MI-BCIs). Various features can be derived from three distinctive domains (i.e., spatial, temporal and spectral); however, the efficacies of the existing feature extraction methods when discriminating complex multiclass MI tasks have yet to be reported. This study investigates the performances of EEG feature extraction techniques from varying domains against different levels of complex, multiclass MI tasks. Ten healthy volunteers underwent multiple complex MI tasks via a robotic arm (i.e., hand grasping and wrist twisting; grasp, spread, pronation and supination). The discrimination performances of various feature extraction (i.e., common spatial patterns (CSP), time domain parameters (TDP), and power spectral density (PSD)) and classification methods for EEG were tested to perform binary (hand grasping/wrist twisting), ternary ((A) grasp/spread/wrist twisting and (B) hand grasping/pronation/supination) and quaternary (grasp/spread/pronation/supination) discrimination. Based on the available data, the combination of shrinkage-regularized linear discriminant analysis (SRLDA) and TDP achieved the highest accuracy. The findings suggest that multiclass complex MI-BCI task discrimination could gain more benefit from analyzing simple and symbolic features such as TDP rather than more complex features such as CSP and PSD.
AB - The electroencephalogram (EEG) remains the predominant source of neurophysiological signals for motor imagery-based brain-computer interfaces (MI-BCIs). Various features can be derived from three distinctive domains (i.e., spatial, temporal and spectral); however, the efficacies of the existing feature extraction methods when discriminating complex multiclass MI tasks have yet to be reported. This study investigates the performances of EEG feature extraction techniques from varying domains against different levels of complex, multiclass MI tasks. Ten healthy volunteers underwent multiple complex MI tasks via a robotic arm (i.e., hand grasping and wrist twisting; grasp, spread, pronation and supination). The discrimination performances of various feature extraction (i.e., common spatial patterns (CSP), time domain parameters (TDP), and power spectral density (PSD)) and classification methods for EEG were tested to perform binary (hand grasping/wrist twisting), ternary ((A) grasp/spread/wrist twisting and (B) hand grasping/pronation/supination) and quaternary (grasp/spread/pronation/supination) discrimination. Based on the available data, the combination of shrinkage-regularized linear discriminant analysis (SRLDA) and TDP achieved the highest accuracy. The findings suggest that multiclass complex MI-BCI task discrimination could gain more benefit from analyzing simple and symbolic features such as TDP rather than more complex features such as CSP and PSD.
KW - Brain-computer interface
KW - Electroencephalogram
KW - Feature extraction
KW - Motor imagery
KW - Multiclass discrimination
UR - http://www.scopus.com/inward/record.url?scp=85067252628&partnerID=8YFLogxK
U2 - 10.1016/j.ins.2019.06.008
DO - 10.1016/j.ins.2019.06.008
M3 - Article
AN - SCOPUS:85067252628
SN - 0020-0255
VL - 502
SP - 190
EP - 200
JO - Information Sciences
JF - Information Sciences
ER -