3.8 Proceedings Paper

Decoding prefrontal cognitive states from electroencephalography in virtual-reality environment

Publisher

IEEE

Keywords

Electroencephalography; Brain-Computer Interface; Convolutional Neural Networks; Virtual Reality

Funding

  1. Institute of Information & Communications Technology Planning & Evaluation(IITP) - Korea government (MSIT) [2019-0-01371, 2017-0-00451]
  2. ICT R&D program of MSIP/IITP [2016-0-00563]
  3. National Research Foundation of Korea(NRF) - Korea government (MSIT) [NRF-2019M3E5D2A01066267]

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Rapid advances in deep learning enabled us to develop various brain-computer interface (BCI) applications. This study presents a novel BCI framework in virtual-reality environment based on an electroencephalography (EEG) decoder for learning strategy classification. We collected 9 subjects' EEG data using a 6-channel EEG-VR headset, and implemented 2D convolutional neural networks with spectrograms as input features. The proposed method achieved 82% classification accuracy, despite a small number of channels and noise artifact. The results suggest a possibility of exploiting BCI technologies for various VR applications.

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