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Turning Gaming EEG Peripherals into Trainable Brain Computer Interfaces

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Part of the Lecture Notes in Computer Science book series (LNAI,volume 9457)


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 discrimination 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.


  • EEG
  • Machine learning
  • Device control
  • BCI
  • K-nearest Neighbors
  • SVM

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Correspondence to Frederic Maire .

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Senadeera, M., Maire, F., Rakotonirainy, A. (2015). Turning Gaming EEG Peripherals into Trainable Brain Computer Interfaces. In: Pfahringer, B., Renz, J. (eds) AI 2015: Advances in Artificial Intelligence. AI 2015. Lecture Notes in Computer Science(), vol 9457. Springer, Cham.

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