IEEE Transactions on Robotics - Special issue on rehabilitation robotics
Comparison of classification methods for P300 brain-computer interface on disabled subjects
Computational Intelligence and Neuroscience - Special issue on Selected Papers from the 4th International Conference on Bioinspired Systems and Cognitive Signal Processing
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Brain Computer Interfaces (BCIs) are used to translate the input of Electroencephalogram (EEG), digitally-recorded via electrodes on the user's scalp, into output commands that control external devices. A P300-based BCI speller system is based upon visual Event-Related Potentials (ERPs) in response to stimulation, as derived from EEG, and is used to type on a computer screen. The Row-Column speller Paradigm (RCP), utilizing a 6-by-6 character matrix, has been a widely-used successful P300 speller, despite inherent problems of adjacency, crowding, and fatigue. RCP is compared here with a new P300 speller interface, the Zigzag Paradigm (ZP). In the ZP interface every second row of the 6-by-6 character matrix is offset to the right by $d/2$ cm, where $d$ cm is the horizontal distance between two adjacent characters. This shifting addressed the adjacency problem by removing all vertical adjacent characters and increasing the distance between most adjacent characters. This shifting also addressed the crowding problem, for most characters, by reducing the number of other characters surrounding a character; critically the target character. A user study upon neurologically-normal individuals revealed significant improvements in online classification performance with the ZP, as supported the view that ZP effectively addressed adjacency and crowding problems. Subjective ratings also revealed that the ZP was more comfortable and caused less fatigue. Theoretical and practical implications of the applicability of the ZP for patients with neuromuscular diseases are discussed.