title: Classification of binary intentions for individuals with impaired oculomotor function: 'eyes-closed' SSVEP-based brain-computer interface (BCI).
abstract: OBJECTIVE
  Some patients suffering from severe neuromuscular diseases have difficulty controlling not only their bodies but also their eyes. Since these patients have difficulty gazing at specific visual stimuli or keeping their eyes open for a long time, they are unable to use the typical steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) systems. In this study, we introduce a new paradigm for SSVEP-based BCI, which can be potentially suitable for disabled individuals with impaired oculomotor function.
  
  
  METHODS
  The proposed electroencephalography (EEG)-based BCI system allows users to express their binary intentions without needing to open their eyes. A pair of glasses with two light emitting diodes flickering at different frequencies was used to present visual stimuli to participants with their eyes closed, and we classified the recorded EEG patterns in the online experiments conducted with five healthy participants and one patient with severe amyotrophic lateral sclerosis (ALS).
  
  
  RESULTS
  Through offline experiments performed with 11 participants, we confirmed that human SSVEP could be modulated by visual selective attention to a specific light stimulus penetrating through the eyelids. Furthermore, the recorded EEG patterns could be classified with accuracy high enough for use in a practical BCI system. After customizing the parameters of the proposed SSVEP-based BCI paradigm based on the offline analysis results, binary intentions of five healthy participants were classified in real time. The average information transfer rate of our online experiments reached 10.83 bits min(-1). A preliminary online experiment conducted with an ALS patient showed a classification accuracy of 80%.
  
  
  CONCLUSIONS
  The results of our offline and online experiments demonstrated the feasibility of our proposed SSVEP-based BCI paradigm. It is expected that our 'eyes-closed' SSVEP-based BCI system can be potentially used for communication of disabled individuals with impaired oculomotor function.
authors.affiliation: Department of Biomedical Engineering, Hanyang University, Seoul 133-731, Korea.
authors.forename: Jeong-Hwan
authors.initials: JH
authors.lastname: Lim
authors.forename: Han-Jeong
authors.initials: HJ
authors.lastname: Hwang
authors.forename: Chang-Hee
authors.initials: CH
authors.lastname: Han
authors.forename: Ki-Young
authors.initials: KY
authors.lastname: Jung
authors.forename: Chang-Hwan
authors.initials: CH
authors.lastname: Im
mesh_terms: D000328:Adult; D000690:Amyotrophic Lateral Sclerosis / Q000503:physiopathology; D062207:Brain-Computer Interfaces; D003199:Computer Systems; D003627:Data Interpretation, Statistical; D006233:Persons with Disabilities; D004569:Electroencephalography; D005073:Evoked Potentials, Somatosensory / Q000502:physiology*; D005133:Eye Movements / Q000502:physiology; D005260:Female; D007839:Functional Laterality / Q000502:physiology; D006801:Humans; D008297:Male; D009886:Ophthalmoplegia / Q000503:physiopathology*; D010775:Photic Stimulation; D055815:Young Adult