TY - GEN
T1 - Towards an EEG-based intelligent wheelchair driving system with vibro-tactile stimuli
AU - Kim, Keun Tae
AU - Lee, Seong Whan
N1 - Funding Information:
This work was supported by Institute for Information and communications Technology Promotion(IITP) grant funded by the Korea government(MSIP) and Microsoft (No. R2212-15-0006, Development of real-time brain signal processing algorithms based on deep learning for BCI-racing)
Publisher Copyright:
© 2016 IEEE.
Copyright:
Copyright 2017 Elsevier B.V., All rights reserved.
PY - 2017/2/6
Y1 - 2017/2/6
N2 - Nowadays, the electroencephalography (EEG)-based wheelchair driving system, one of the major applications of brain-computer interface (BCI), that allows an individual with mobility impairments to perform daily living activities independently. In this context, user's intention identifying methods were developed by several research groups using various paradigms for the wheelchair driving. In this study, we use a steady-state somatosensory evoked potential (SSSEP) paradigm, which elicits brain responses to vibro-tactile stimulation of specific frequencies, for a user's intention identification to driving a wheelchair. The main focus of this study is to validate an effectiveness of our SSSEP-based wheelchair driving system via an online experiment with more challenging tasks than our recent study. In our system, a subject concentrated on one of vibro-tactile stimuli (attached on left-hand, right-hand, and foot) selectively for driving wheelchair (corresponding to turn-left, turn-right, and move-forward). Five healthy subjects participated in the online experiment, and the experimental results show that our SSSEP paradigm is suitable to EEG-based intelligent wheelchair driving system.
AB - Nowadays, the electroencephalography (EEG)-based wheelchair driving system, one of the major applications of brain-computer interface (BCI), that allows an individual with mobility impairments to perform daily living activities independently. In this context, user's intention identifying methods were developed by several research groups using various paradigms for the wheelchair driving. In this study, we use a steady-state somatosensory evoked potential (SSSEP) paradigm, which elicits brain responses to vibro-tactile stimulation of specific frequencies, for a user's intention identification to driving a wheelchair. The main focus of this study is to validate an effectiveness of our SSSEP-based wheelchair driving system via an online experiment with more challenging tasks than our recent study. In our system, a subject concentrated on one of vibro-tactile stimuli (attached on left-hand, right-hand, and foot) selectively for driving wheelchair (corresponding to turn-left, turn-right, and move-forward). Five healthy subjects participated in the online experiment, and the experimental results show that our SSSEP paradigm is suitable to EEG-based intelligent wheelchair driving system.
KW - Brain-computer interface (BCI)
KW - Brain-controlled wheelchair
KW - Electroencephalography (EEG)
KW - Vibro-tactile stimuli
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U2 - 10.1109/SMC.2016.7844595
DO - 10.1109/SMC.2016.7844595
M3 - Conference contribution
AN - SCOPUS:85015783147
T3 - 2016 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2016 - Conference Proceedings
SP - 2382
EP - 2385
BT - 2016 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2016 - Conference Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2016 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2016
Y2 - 9 October 2016 through 12 October 2016
ER -