Investigation of Brain-Computer Interfaces That Apply Sound-Evoked Event-Related Potentials

2010 ◽  
pp. 127-134 ◽  
Author(s):  
Masumi Kogure ◽  
Shuichi Matsuzaki ◽  
Yasuhiro Wada
Keyword(s):  
Event Related Potentials ◽  
Brain Computer Interfaces ◽  
Computer Interfaces ◽  
Related Potentials

scholarly journals Anytime collaborative brain–computer interfaces for enhancing perceptual group decision-making

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Saugat Bhattacharyya ◽  
Davide Valeriani ◽  
Caterina Cinel ◽  
Luca Citi ◽  
Riccardo Poli
Keyword(s):  
Decision Making ◽  
Group Decision Making ◽  
Group Decision ◽  
Brain Activity ◽  
Event Related Potentials ◽  
Brain Computer Interfaces ◽  
Appearance Time ◽  
Perceptual Group ◽  
Computer Interfaces ◽  
Related Potentials

AbstractIn this paper we present, and test in two realistic environments, collaborative Brain-Computer Interfaces (cBCIs) that can significantly increase both the speed and the accuracy of perceptual group decision-making. The key distinguishing features of this work are: (1) our cBCIs combine behavioural, physiological and neural data in such a way as to be able to provide a group decision at any time after the quickest team member casts their vote, but the quality of a cBCI-assisted decision improves monotonically the longer the group decision can wait; (2) we apply our cBCIs to two realistic scenarios of military relevance (patrolling a dark corridor and manning an outpost at night where users need to identify any unidentified characters that appear) in which decisions are based on information conveyed through video feeds; and (3) our cBCIs exploit Event-Related Potentials (ERPs) elicited in brain activity by the appearance of potential threats but, uniquely, the appearance time is estimated automatically by the system (rather than being unrealistically provided to it). As a result of these elements, in the two test environments, groups assisted by our cBCIs make both more accurate and faster decisions than when individual decisions are integrated in more traditional manners.

scholarly journals Anytime Collaborative Brain-Computer Interfaces for Enhancing Group Decision-Making in Realistic Environments

2020 ◽  
Author(s):  
Saugat Bhattacharyya ◽  
Davide Valeriani ◽  
Caterina Cinel ◽  
Luca Citi ◽  
Riccardo Poli
Keyword(s):  
Decision Making ◽  
Group Decision Making ◽  
Group Decision ◽  
Brain Activity ◽  
Event Related Potentials ◽  
Brain Computer Interfaces ◽  
Appearance Time ◽  
Computer Interfaces ◽  
Related Potentials ◽  
Individual Decisions

Abstract In this paper we present and test collaborative Brain-Computer Interfaces (cBCIs) that can significantly increase both the speed and the accuracy of group decision-making in realistic situations. The key distinguishing features of this work are: (1) our cBCIs combine behavioural, physiological and neural data in such a way as to be able to provide a group decision at any time after the quickest team member casts their vote, but the quality of a cBCI-assisted decision improves monotonically the longer the group decision can wait; (2) we apply our cBCIs to two realistic scenarios of military relevance (patrolling a dark corridor and manning an outpost at night where users need to identify any unidentified characters that appear) in which decisions are based on information conveyed through video feeds; and (3) our cBCIs exploit Event-Related Potentials (ERPs) elicited in brain activity by the appearance of potential threats but, uniquely, the appearance time is estimated automatically by the system (rather than being unrealistically provided to it). As a result of these elements, groups assisted by our cBCIs make both more accurate and faster decisions than when individual decisions are integrated in more traditional manners.

Unsupervised learning for brain–computer interfaces based on event-related potentials

2018 ◽  
pp. 103-123
Author(s):  
Pieter-Jan Kindermans ◽  
David Hübner ◽  
Thibault Verhoeven ◽  
Klaus-Robert Müller ◽  
Michael Tangermann
Keyword(s):  
Unsupervised Learning ◽  
Event Related Potentials ◽  
Brain Computer Interfaces ◽  
Computer Interfaces ◽  
Related Potentials

scholarly journals Online Home Appliance Control Using EEG-Based Brain–Computer Interfaces

Electronics ◽  
2019 ◽  
Vol 8 (10) ◽  
pp. 1101 ◽  
Author(s):  
Minju Kim ◽  
Min-Ki Kim ◽  
Minho Hwang ◽  
Hyun-Young Kim ◽  
Jeongho Cho ◽  
...  
Keyword(s):  
Daily Living ◽  
Event Related Potentials ◽  
Home Automation ◽  
Brain Computer Interfaces ◽  
Home Appliances ◽  
Computer Interfaces ◽  
Related Potentials ◽  
Home Appliance ◽  
Door Lock ◽  
The Internet Of Things

Brain–computer interfaces (BCIs) allow patients with paralysis to control external devices by mental commands. Recent advances in home automation and the Internet of things may extend the horizon of BCI applications into daily living environments at home. In this study, we developed an online BCI based on scalp electroencephalography (EEG) to control home appliances. The BCI users controlled TV channels, a digital door-lock system, and an electric light system in an unshielded environment. The BCI was designed to harness P300 and N200 components of event-related potentials (ERPs). On average, the BCI users could control TV channels with an accuracy of 83.0% ± 17.9%, the digital door-lock with 78.7% ± 16.2% accuracy, and the light with 80.0% ± 15.6% accuracy, respectively. Our study demonstrates a feasibility to control multiple home appliances using EEG-based BCIs.

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