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Brain–computer interfaces for communication and control
BCI2000: a general-purpose brain-computer interface (BCI) system
- G. Schalk, D. McFarland, T. Hinterberger, N. Birbaumer, J. Wolpaw
- Computer ScienceIEEE Transactions on Biomedical Engineering
- 24 May 2004
This report is intended to describe to investigators, biomedical engineers, and computer scientists the concepts that the BCI2000 system is based upon and gives examples of successful BCI implementations using this system.
Brain-computer interfaces for communication and control
The brain's electrical signals enable people without muscle control to physically interact with the world through the use of their brains' electrical signals.
Control of a two-dimensional movement signal by a noninvasive brain-computer interface in humans.
- J. Wolpaw, D. McFarland
- Biology, MedicineProceedings of the National Academy of Sciences…
- 21 December 2004
It is shown that a noninvasive BCI that uses scalp-recorded electroencephalographic activity and an adaptive algorithm can provide humans, including people with spinal cord injuries, with multidimensional point-to-point movement control that falls within the range of that reported with invasive methods in monkeys.
Brain-computer interface technology: a review of the first international meeting.
- J. Wolpaw, N. Birbaumer, T. Vaughan
- Computer ScienceIEEE transactions on rehabilitation engineering…
- 1 June 2000
The first international meeting devoted to brain-computer interface research and development is summarized, which focuses on the development of appropriate applications, identification of appropriate user groups, and careful attention to the needs and desires of individual users.
A comparison of classification techniques for the P300 Speller.
The results indicate that while all methods attained acceptable performance levels, SWLDA and FLD provide the best overall performance and implementation characteristics for practical classification of P300 Speller data.
Toward enhanced P300 speller performance
Spatial filter selection for EEG-based communication.
EMG contamination of EEG: spectral and topographical characteristics
Mu and Beta Rhythm Topographies During Motor Imagery and Actual Movements
Evidence that motor imagery could play an important role in EEG-based communication is supplied, and it is suggested that mu and beta rhythms might provide independent control signals.