title: Multi-class EEG classification of voluntary hand movement directions.
abstract: OBJECTIVE
  Studies have shown that low frequency components of brain recordings provide information on voluntary hand movement directions. However, non-invasive techniques face more challenges compared to invasive techniques.
  
  
  METHODS
  This study presents a novel signal processing technique to extract features from non-invasive electroencephalography (EEG) recordings for classifying voluntary hand movement directions. The proposed technique comprises the regularized wavelet-common spatial pattern algorithm to extract the features, mutual information-based feature selection, and multi-class classification using the Fisher linear discriminant. EEG data from seven healthy human subjects were collected while they performed voluntary right hand center-out movement in four orthogonal directions. In this study, the movement direction dependent signal-to-noise ratio is used as a parameter to denote the effectiveness of each temporal frequency bin in the classification of movement directions.
  
  
  RESULTS
  Significant (p < 0.005) movement direction dependent modulation in the EEG data was identified largely towards the end of movement at low frequencies (≤6 Hz) from the midline parietal and contralateral motor areas. Experimental results on single trial classification of the EEG data collected yielded an average accuracy of (80.24 ± 9.41)% in discriminating the four different directions using the proposed technique on features extracted from low frequency components.
  
  
  CONCLUSIONS
  The proposed feature extraction strategy provides very high multi-class classification accuracies, and the results are proven to be more statistically significant than existing methods. The results obtained suggest the possibility of multi-directional movement classification from single-trial EEG recordings using the proposed technique in low frequency components.
authors.affiliation: School of Computer Engineering, Nanyang Technological University, Singapore.
authors.forename: Neethu
authors.initials: N
authors.lastname: Robinson
authors.forename: Cuntai
authors.initials: C
authors.lastname: Guan
authors.forename: A P
authors.initials: AP
authors.lastname: Vinod
authors.forename: Kai Keng
authors.initials: KK
authors.lastname: Ang
authors.forename: Keng Peng
authors.initials: KP
authors.lastname: Tee
mesh_terms: D000328:Adult; D000465:Algorithms; D001696:Biomechanical Phenomena; D001921:Brain / Q000502:physiology; D003463:Cues; D003627:Data Interpretation, Statistical; D004558:Electric Stimulation; D004569:Electroencephalography / Q000145:classification* / Q000706:statistics & numerical data; D006225:Hand / Q000502:physiology*; D006801:Humans; D008297:Male; D009068:Movement / Q000502:physiology*; D009483:Neuropsychological Tests; D015203:Reproducibility of Results; D012815:Signal Processing, Computer-Assisted; D059629:Signal-To-Noise Ratio; D058067:Wavelet Analysis