A Tri-Hybrid Brain-Computer Interface for Neuro-Information Systems

Daniel Godfrey (), Chantel Findlay, Dinesh Mulchandani, Ravishankar Subramanilyer, Colin Conrad and Aaron Newman
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Daniel Godfrey: Dalhousie University
Chantel Findlay: Dalhousie University
Dinesh Mulchandani: Dalhousie University
Ravishankar Subramanilyer: Dalhousie University
Colin Conrad: Dalhousie University
Aaron Newman: Dalhousie University

A chapter in Information Systems and Neuroscience, 2020, pp 291-297 from Springer

Abstract: Abstract Brain-computer interfaces (BCIs) are computerized systems that convert brain activity into control commands to operate software or external devices. Though promising, BCIs currently have limited practicality and usership due to poor signal classification and large training data requirements. The present study aims to overcome both challenges by combining three brain signals. This paradigm could improve existing BCI technical efficacy, and extrapolate to applications where hands-free visual interfaces could equip users with communication and information resources that improve work processes.

Keywords: Brain-computer interface (BCI); Event-related potential (ERP); Machine learning; Hybrid-BCI; Visual interfaces (search for similar items in EconPapers)
Date: 2020
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DOI: 10.1007/978-3-030-60073-0_34

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