A hierarchical classification strategy for robust detection of passive/active mental state using user-voluntary pitch imagery task

Young Jin Kee, Min Ho Lee, John Williamson, Seong Whan Lee

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish
Title of host publicationProceedings - 4th Asian Conference on Pattern Recognition, ACPR 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages876-881
Number of pages6
ISBN (Electronic)9781538633540
DOIs
Publication statusPublished - 2018 Dec 13
Event4th Asian Conference on Pattern Recognition, ACPR 2017 - Nanjing, China
Duration: 2017 Nov 262017 Nov 29

Other

Other4th Asian Conference on Pattern Recognition, ACPR 2017
CountryChina
CityNanjing
Period17/11/2617/11/29

Fingerprint

Electroencephalography
Decoding
Brain computer interface
Brain
Experiments

Keywords

  • Brain-computer interface
  • Electroencephalography
  • Event-related potential
  • Pitch-imagery task

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Vision and Pattern Recognition
  • Signal Processing

Cite this

Kee, Y. J., Lee, M. H., Williamson, J., & Lee, S. W. (2018). A hierarchical classification strategy for robust detection of passive/active mental state using user-voluntary pitch imagery task. In Proceedings - 4th Asian Conference on Pattern Recognition, ACPR 2017 (pp. 876-881). [8575943] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ACPR.2017.133

A hierarchical classification strategy for robust detection of passive/active mental state using user-voluntary pitch imagery task. / Kee, Young Jin; Lee, Min Ho; Williamson, John; Lee, Seong Whan.

Proceedings - 4th Asian Conference on Pattern Recognition, ACPR 2017. Institute of Electrical and Electronics Engineers Inc., 2018. p. 876-881 8575943.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Kee, YJ, Lee, MH, Williamson, J & Lee, SW 2018, A hierarchical classification strategy for robust detection of passive/active mental state using user-voluntary pitch imagery task. in Proceedings - 4th Asian Conference on Pattern Recognition, ACPR 2017., 8575943, Institute of Electrical and Electronics Engineers Inc., pp. 876-881, 4th Asian Conference on Pattern Recognition, ACPR 2017, Nanjing, China, 17/11/26. https://doi.org/10.1109/ACPR.2017.133
Kee YJ, Lee MH, Williamson J, Lee SW. A hierarchical classification strategy for robust detection of passive/active mental state using user-voluntary pitch imagery task. In Proceedings - 4th Asian Conference on Pattern Recognition, ACPR 2017. Institute of Electrical and Electronics Engineers Inc. 2018. p. 876-881. 8575943 https://doi.org/10.1109/ACPR.2017.133
Kee, Young Jin ; Lee, Min Ho ; Williamson, John ; Lee, Seong Whan. / A hierarchical classification strategy for robust detection of passive/active mental state using user-voluntary pitch imagery task. Proceedings - 4th Asian Conference on Pattern Recognition, ACPR 2017. Institute of Electrical and Electronics Engineers Inc., 2018. pp. 876-881
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abstract = "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.",
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