title: Classification of single-trial electroencephalogram during finger movement.
abstract: We present an algorithm to discriminate between the single-trial electroencephalograms (EEG) of two different finger movement tasks. The method uses a spatio-temporal analysis to classify the EEG recorded during voluntary left versus right finger movement tasks. This algorithm produced a classification accuracy of 92.1% on the data from five subjects, without requiring subject training or data selection. This technique can be employed in an EEG-based brain-computer interface due to its high recognition rate, insensitivity to noise, and simplicity in computation.
authors.affiliation: Department of Biomedical Engineering, Tsinghua University, Beijing 100084, China. liyong98@mails.tsinghua.edu.cn
authors.forename: Yong
authors.initials: Y
authors.lastname: Li
authors.forename: Xiaorong
authors.initials: X
authors.lastname: Gao
authors.forename: Hesheng
authors.initials: H
authors.lastname: Liu
authors.forename: Shangkai
authors.initials: S
authors.lastname: Gao
mesh_terms: D000328:Adult; D000465:Algorithms; D002540:Cerebral Cortex / Q000502:physiology; D004569:Electroencephalography / Q000145:classification* / Q000379:methods*; D019054:Evoked Potentials, Motor / Q000502:physiology*; D005385:Fingers / Q000502:physiology*; D006801:Humans; D008297:Male; D009044:Motor Cortex / Q000502:physiology; D009068:Movement / Q000502:physiology*; D010363:Pattern Recognition, Automated; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013003:Somatosensory Cortex / Q000502:physiology; D013647:Task Performance and Analysis