TY - GEN
T1 - Tactile brain-computer interface using classification of P300 responses evoked by full body spatial vibrotactile stimuli
AU - Kodama, Takumi
AU - Makino, Shoji
AU - Rutkowski, Tomasz M.
N1 - Publisher Copyright:
© 2016 Asia Pacific Signal and Information Processing Association.
PY - 2017/1/17
Y1 - 2017/1/17
N2 - In this study we propose a novel stimulus-driven brain-computer interface (BCI) paradigm, which generates control commands based on classification of somatosensory modality P300 responses. Six spatial vibrotactile stimulus patterns are applied to entire back and limbs of a user. The aim of the current project is to validate an effectiveness of the vibrotactile stimulus patterns for BCI purposes and to establish a novel concept of tactile modality communication link, which shall help locked-in syndrome (LIS) patients, who lose their sight and hearing due to sensory disabilities. We define this approach as a full-body BCI (fbBCI) and we conduct psychophysical stimulus evaluation and realtime EEG response classification experiments with ten healthy body-able users. The grand mean averaged psychophysical stimulus pattern recognition accuracy have resulted at 98.18%, whereas the realtime EEG accuracy at 53.67%. An information-transfer-rate (ITR) scores of all the tested users have ranged from 0.042 to 4.154 bit/minute.
AB - In this study we propose a novel stimulus-driven brain-computer interface (BCI) paradigm, which generates control commands based on classification of somatosensory modality P300 responses. Six spatial vibrotactile stimulus patterns are applied to entire back and limbs of a user. The aim of the current project is to validate an effectiveness of the vibrotactile stimulus patterns for BCI purposes and to establish a novel concept of tactile modality communication link, which shall help locked-in syndrome (LIS) patients, who lose their sight and hearing due to sensory disabilities. We define this approach as a full-body BCI (fbBCI) and we conduct psychophysical stimulus evaluation and realtime EEG response classification experiments with ten healthy body-able users. The grand mean averaged psychophysical stimulus pattern recognition accuracy have resulted at 98.18%, whereas the realtime EEG accuracy at 53.67%. An information-transfer-rate (ITR) scores of all the tested users have ranged from 0.042 to 4.154 bit/minute.
UR - http://www.scopus.com/inward/record.url?scp=85013810729&partnerID=8YFLogxK
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U2 - 10.1109/APSIPA.2016.7820734
DO - 10.1109/APSIPA.2016.7820734
M3 - Conference contribution
AN - SCOPUS:85013810729
T3 - 2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2016
BT - 2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2016
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2016
Y2 - 13 December 2016 through 16 December 2016
ER -