title: A high performance sensorimotor beta rhythm-based brain-computer interface associated with human natural motor behavior.
abstract: To explore the reliability of a high performance brain-computer interface (BCI) using non-invasive EEG signals associated with human natural motor behavior does not require extensive training. We propose a new BCI method, where users perform either sustaining or stopping a motor task with time locking to a predefined time window. Nine healthy volunteers, one stroke survivor with right-sided hemiparesis and one patient with amyotrophic lateral sclerosis (ALS) participated in this study. Subjects did not receive BCI training before participating in this study. We investigated tasks of both physical movement and motor imagery. The surface Laplacian derivation was used for enhancing EEG spatial resolution. A model-free threshold setting method was used for the classification of motor intentions. The performance of the proposed BCI was validated by an online sequential binary-cursor-control game for two-dimensional cursor movement. Event-related desynchronization and synchronization were observed when subjects sustained or stopped either motor execution or motor imagery. Feature analysis showed that EEG beta band activity over sensorimotor area provided the largest discrimination. With simple model-free classification of beta band EEG activity from a single electrode (with surface Laplacian derivation), the online classifications of the EEG activity with motor execution/motor imagery were: >90%/ approximately 80% for six healthy volunteers, >80%/ approximately 80% for the stroke patient and approximately 90%/ approximately 80% for the ALS patient. The EEG activities of the other three healthy volunteers were not classifiable. The sensorimotor beta rhythm of EEG associated with human natural motor behavior can be used for a reliable and high performance BCI for both healthy subjects and patients with neurological disorders.
  
  
  CONCLUSIONS
  The proposed new non-invasive BCI method highlights a practical BCI for clinical applications, where the user does not require extensive training.
authors.affiliation: Human Motor Control Section, Medical Neurological Branch, National Institute of Neurological Disorders, National Institutes of Health, Bethesda, MD 20892, USA. obai@vcu.edu
authors.forename: Ou
authors.initials: O
authors.lastname: Bai
authors.forename: Peter
authors.initials: P
authors.lastname: Lin
authors.forename: Sherry
authors.initials: S
authors.lastname: Vorbach
authors.forename: Mary Kay
authors.initials: MK
authors.lastname: Floeter
authors.forename: Noriaki
authors.initials: N
authors.lastname: Hattori
authors.forename: Mark
authors.initials: M
authors.lastname: Hallett
mesh_terms: D000328:Adult; D000465:Algorithms; D000690:Amyotrophic Lateral Sclerosis / Q000503:physiopathology / Q000523:psychology; D001611:Beta Rhythm; D001921:Brain / Q000502:physiology*; D002138:Calibration; D003344:Cortical Synchronization; D003627:Data Interpretation, Statistical; D004576:Electromyography; D005071:Evoked Potentials / Q000502:physiology; D005260:Female; D006801:Humans; D007092:Imagination / Q000502:physiology; D008297:Male; D008875:Middle Aged; D009068:Movement / Q000502:physiology*; D010291:Paresis / Q000503:physiopathology / Q000523:psychology; D012372:ROC Curve; D020521:Stroke / Q000523:psychology; D014584:User-Computer Interface; D018910:Video Games