TY - GEN
T1 - A hierarchical classification strategy for robust detection of passive/active mental state using user-voluntary pitch imagery task
AU - Kee, Young Jin
AU - Lee, Min Ho
AU - Williamson, John
AU - Lee, Seong Whan
N1 - Funding Information:
This research was supported by the MSIT(Ministry of Science and ICT), Korea, under the SW Starlab support program(IITP-2015-1107) supervised by the IITP(Institute for Information & Communications Technology Promotion).
Publisher Copyright:
© 2017 IEEE.
PY - 2018/12/13
Y1 - 2018/12/13
N2 - Event-related potentials (ERPs) represent neuronal activity in the brain elicited by external visual/auditory stimulation, and it is widely used in brain-computer interface (BCI) systems. The ERP responses are elicited a few milliseconds after attending to an oddball stimulus; target and non-Target stimulus are repeatedly flashed while the electroencephalography (EEG) is recording. ERP responses in the EEG signal have a poor signal-To-ratio in single-Trial analysis; therefore, the epochs of the target and non-Target trials are averaged over time in order to improve their decoding accuracy. Furthermore, these exogenous potentials can be naturally evoked by just looking at a target symbol. Therefore, the BCI system could generate unintended commands without considering the user's intention. In this study, we approach this dilemma by assuming that a greater effort for the mental task would evoke a stronger positive/negative ERP deflection. Three mental states are defined: passive gazing, active counting, and pitch-imagery. The experiments results showed significantly enhanced ERP patterns and averaged decoding accuracies of 80%, 95.4%, and 95.6%, respectively. The decoding accuracies between both active tasks and the passive task showed an averaged accuracy of 57.5% (gazing vs. counting) and 72.5% (gazing vs. pitch-imagery). Following this result, we proposed a hierarchy classification strategy where the passive or active mental state is decoded in the first stage, and the target stimuli are estimated in the second stage. Our work is the first to propose a system that classifies an intended or unintended brain state by considering the measurable differences of mental effort in the EEG signal so that unintended commands to the system are minimized.
AB - Event-related potentials (ERPs) represent neuronal activity in the brain elicited by external visual/auditory stimulation, and it is widely used in brain-computer interface (BCI) systems. The ERP responses are elicited a few milliseconds after attending to an oddball stimulus; target and non-Target stimulus are repeatedly flashed while the electroencephalography (EEG) is recording. ERP responses in the EEG signal have a poor signal-To-ratio in single-Trial analysis; therefore, the epochs of the target and non-Target trials are averaged over time in order to improve their decoding accuracy. Furthermore, these exogenous potentials can be naturally evoked by just looking at a target symbol. Therefore, the BCI system could generate unintended commands without considering the user's intention. In this study, we approach this dilemma by assuming that a greater effort for the mental task would evoke a stronger positive/negative ERP deflection. Three mental states are defined: passive gazing, active counting, and pitch-imagery. The experiments results showed significantly enhanced ERP patterns and averaged decoding accuracies of 80%, 95.4%, and 95.6%, respectively. The decoding accuracies between both active tasks and the passive task showed an averaged accuracy of 57.5% (gazing vs. counting) and 72.5% (gazing vs. pitch-imagery). Following this result, we proposed a hierarchy classification strategy where the passive or active mental state is decoded in the first stage, and the target stimuli are estimated in the second stage. Our work is the first to propose a system that classifies an intended or unintended brain state by considering the measurable differences of mental effort in the EEG signal so that unintended commands to the system are minimized.
KW - Brain-computer interface
KW - Electroencephalography
KW - Event-related potential
KW - Pitch-imagery task
UR - http://www.scopus.com/inward/record.url?scp=85060543148&partnerID=8YFLogxK
U2 - 10.1109/ACPR.2017.133
DO - 10.1109/ACPR.2017.133
M3 - Conference contribution
AN - SCOPUS:85060543148
T3 - Proceedings - 4th Asian Conference on Pattern Recognition, ACPR 2017
SP - 876
EP - 881
BT - Proceedings - 4th Asian Conference on Pattern Recognition, ACPR 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 4th Asian Conference on Pattern Recognition, ACPR 2017
Y2 - 26 November 2017 through 29 November 2017
ER -