The brain response interface: communication through visually-induced electrical brain responses
Journal of Microcomputer Applications - Special issue on computers for handicapped people
Adaptive filter theory (3rd ed.)
Adaptive filter theory (3rd ed.)
Proceedings of the 25th international conference on Machine learning
Classifying EEG data into different memory loads across subjects
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
Artificial Intelligence in Medicine
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Most EEG-based brain-computer interface (BCI) paradigms come along with specific electrode positions, for example, for a visual-based BCI, electrode positions close to the primary visual cortex are used. For new BCI paradigms it is usually not known where task relevant activity can be measured from the scalp. For individual subjects, Lal et al. in 2004 showed that recording positions can be found without the use of prior knowledge about the paradigm used. However it remains unclear to what extent their method of recursive channel elimination (RCE) can be generalized across subjects. In this paper we transfer channel rankings from a group of subjects to a new subject. For motor imagery tasks the results are promising, although cross-subject channel selection does not quite achieve the performance of channel selection on data of single subjects. Although the RCE method was not provided with prior knowledge about the mental task, channels that are well known to be important (from a physiological point of view) were consistently selected whereas task-irrelevant channels were reliably disregarded.