Real-time Mental State Recognition using a Wearable EEG

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Details zur Publikation

Autor(en): Richer R, Zhao N, Amores J, Eskofier B, Paradiso JA
Herausgeber: IEEE
Jahr der Veröffentlichung: 2018


Abstract

The increasing quality and availability of low-cost EEG systems offer new possibilities for non-medical purposes. Existing openly available algorithms to assess the user’s mental state in real-time have been mainly performed with medical-grade equipment. In this paper, an approach to assess the user’s Focus or Relax states in real-time using a consumer-grade, wearable EEG headband is evaluated. One naive measure and four entropy-based measures, computed using relative frequency band powers in the EEG signal, were introduced. Classifiers for relax and focus state detection, based on the estimation of probability distributions, were developed and evaluated in a user study. Results showed that the Tsallis entropy-based measure performed best for the Focus score, whereas the Renyi measure performed best for the Relax score. Sensitivities of 82.0% and 80.4% with specificities of 82.8% and 80.8% were achieved for the Focus and Relax scores, respectively. The results demonstrated the possibilities of using a wearable EEG system for real-time mental state recognition.





FAU-Autoren / FAU-Herausgeber

Eskofier, Björn Prof. Dr.
Lehrstuhl für Informatik 14 (Maschinelles Lernen und Datenanalytik)
Richer, Robert
Lehrstuhl für Informatik 14 (Maschinelles Lernen und Datenanalytik)


Autor(en) der externen Einrichtung(en)
Massachusetts Institute of Technology (MIT)


Zitierweisen

APA:
Richer, R., Zhao, N., Amores, J., Eskofier, B., & Paradiso, J.A. (2018). Real-time Mental State Recognition using a Wearable EEG. In IEEE (Eds.), . Honolulu, Hawaii, US.

MLA:
Richer, Robert, et al. "Real-time Mental State Recognition using a Wearable EEG." Proceedings of the 40th International Engineering in Medicine and Biology Conference (EMBC), Honolulu, Hawaii Ed. IEEE, 2018.

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Zuletzt aktualisiert 2018-11-07 um 13:08