Exploring Dependence of Subject-Specific Training Strategies for EEG Based Brain-Computer Interfaces

Design and deployment of brain-computer interface enabled assistive systems poses many practical questions, including the decision of whether to use a pretrained model or to customize a model to each unique end-user. In this work, we apply this question to the application domain of a passive brain-...

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Bibliographic Details
Published inProceedings of IEEE Southeastcon pp. 848 - 853
Main Authors de Wit, T. Warren, Menon, Vineetha, Davis, Thomas
Format Conference Proceeding
LanguageEnglish
Published IEEE 15.03.2024
Subjects
autonomous systems
Brain modeling
brain-computer interface
Brain-computer interfaces
context fusion
customization
decision fusion
deep learning
deployment
disaster relief
Disasters
Drones
EEG
Electroencephalography
human-robot teaming
Object detection
subject-independence
Training
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Summary:Design and deployment of brain-computer interface enabled assistive systems poses many practical questions, including the decision of whether to use a pretrained model or to customize a model to each unique end-user. In this work, we apply this question to the application domain of a passive brain- controlled drone for use in disaster relief and hostage rescue situations. A six-class intent (e.g. EEG) recognition experiment is performed with 42 subjects. This pilot study explores and evaluates the effects of subject-specific ("customized") versus subject- independent ("uncustomized") modeling approaches. Based on experimental validation, we present our discussions on the observed pros and cons of each training strategy. In this study, it was noted that the uncustomized training approach had the best target detection performance with a reduction in variance. Additionally, its deployment readiness attribute make it a more relevant and feasible option for our intended use case.
ISSN:1558-058X
DOI:10.1109/SoutheastCon52093.2024.10500151