title: Psychophysiological classification and experiment study for spontaneous EEG based on two novel mental tasks.
abstract: BACKGROUND
  Study of imagination offers a perfect setting for study of a large variety of states of consciousness.
  
  
  OBJECTIVE
  Here, we studied the characteristics of two electroencephalographic (EEG) patterns evoked by two different imaginary tasks and evaluated the binary classification performance.
  
  
  METHODS
  Fifteen individuals (11 male and 4 female, age range of 22 to 33) participated in five sessions of 32-channel EEG recordings. Only by analyzing the subjects' output EEG signals from the central parieto-occipital region of PZ electrode, under the circumstances of consciousness of relaxation-meditation or tension-imagination, we carried out the experiment of feature extraction for spontaneous EEG, as the subjects were blindfolded but asked to open their eyes all the same. The Hilbert-Huang Transform (HHT) was utilized to obtain the Hilbert time-frequency amplitude spectrum, and then with the feature vector set extracted, a two-class Fisher linear discriminant analysis classifier was trained for classification of data epochs of those two tasks.
  
  
  RESULTS
  The overall result was that about 90% (± 5%) of the epochs could be correctly classified to their originating task.
  
  
  CONCLUSIONS
  This study not only brings new opportunities for consciousness studies, but also provides a new classification paradigm for achieving control of robots based on the brain-computer interface (BCI).
authors.affiliation: School of Instrument Science and Engineering, Southeast University, Nanjing, Jiangsu, China.
authors.forename: Hui
authors.initials: H
authors.lastname: Wang
authors.affiliation: School of Instrument Science and Engineering, Southeast University, Nanjing, Jiangsu, China.
authors.forename: Aiguo
authors.initials: A
authors.lastname: Song
authors.affiliation: School of Instrument Science and Engineering, Southeast University, Nanjing, Jiangsu, China.
authors.forename: Bowei
authors.initials: B
authors.lastname: Li
authors.affiliation: School of Instrument Science and Engineering, Southeast University, Nanjing, Jiangsu, China.
authors.forename: Baoguo
authors.initials: B
authors.lastname: Xu
authors.affiliation: Biorobotics Lab, Electrical Engineering Department, University of Washington, Seattle, WA, USA.
authors.forename: Yangming
authors.initials: Y
authors.lastname: Li
mesh_terms: D000328:Adult; D000465:Algorithms; D062207:Brain-Computer Interfaces; D003243:Consciousness / Q000502:physiology*; D004569:Electroencephalography; D005260:Female; D006801:Humans; D007092:Imagination / Q000502:physiology*; D008297:Male; D019122:Meditation; D009778:Occipital Lobe / Q000378:metabolism; D010296:Parietal Lobe / Q000378:metabolism; D012063:Relaxation / Q000502:physiology*; D012815:Signal Processing, Computer-Assisted
keywords: EEG-based brain-computer interface (BCI); Hilbert-Huang transform; feature extraction; mental task of relaxation-meditation; mental task of tension-imagination; pattern classification