Turning gaming EEG peripherals into trainable brain computer interfaces

Senadeera, Manisha, Maire, Frederic, & Rakotonirainy, Andry (2015) Turning gaming EEG peripherals into trainable brain computer interfaces. In Maher, Michael & Thiebaux, Sylvie (Eds.) 28th Australasian Joint Conference on Artificial Intelligence (AI 2015), 30 November – 4 December 2015, Canberra, A.C.T. (In Press)


Companies such as NeuroSky and Emotiv Systems are selling non-medical EEG devices for human computer interaction. These devices are significantly more affordable than their medical counterparts, and are mainly used to measure levels of engagement, focus, relaxation and stress. This information is sought after for marketing research and games. However, these EEG devices have the potential to enable users to interact with their surrounding environment using thoughts only, without activating any muscles. In this paper, we present preliminary results that demonstrate that despite reduced voltage and time sensitivity compared to medical-grade EEG systems, the quality of the signals of the Emotiv EPOC neuroheadset is sufficiently good in allowing discrimina tion between imaging events. We collected streams of EEG raw data and trained different types of classifiers to discriminate between three states (rest and two imaging events). We achieved a generalisation error of less than 2% for two types of non-linear classifiers.

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ID Code: 89489
Item Type: Conference Paper
Refereed: Yes
Additional URLs:
Keywords: MACHINE LEARNING, brain computer interface
Divisions: Current > Research Centres > Centre for Accident Research & Road Safety - Qld (CARRS-Q)
Current > Schools > School of Electrical Engineering & Computer Science
Current > QUT Faculties and Divisions > Faculty of Health
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Copyright Owner: Copyright 2015 [Please consult the author]
Deposited On: 26 Oct 2015 22:48
Last Modified: 20 Oct 2016 17:24

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