Real World BCI: Cross-Domain Learning and Practical Applications
Abstract
In order to develop real-world BCI solutions machine learning models must generalize not only to unseen users but also to unseen scenarios. In this concept paper we describe our initial investigation into Deep Learning tools to create generalized models for both cross-subject and cross-domain learning. We demonstrate our approach using two different, laboratory grade data sets to train a learning model that we then apply to a third more complex scenario. While our results indicate that cross-domain learning is possible, we also identify potential avenues for further research and development (such as disentangling spatially or temporally overlapping responses). Finally, we describe our work to implement a system that uses cross-domain learning to develop a real-time application for performing BCI-based Human-Centric Scene Analysis.
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EEGNet: a compact convolutional neural network for EEG-based brain-computer interfaces
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- Authors
- Gordon, S. M., Jaswa, M., Solon, A. J., & Lawhern, V. J.
- Affiliations
- DCS Corporation (United States) (US)
- DEVCOM Army Research Laboratory (US)
- Venue
- Proceedings of the 2017 ACM Workshop on An Application-oriented Approach to BCI out of the laboratory
- Publisher
- ACM
- Published
- 2017-03-13
- Pages
- 25-28
- OpenAlex
- W2592757158
- Cited by
- 20
- References
- 7
- Citations by year
- 2025: 3 · 2024: 6 · 2023: 3 · 2021: 2 · 2020: 2 · 2019: 1 · 2018: 3
- FWCI
- 2.68
- Type
- conference paper
- Language
- en
- Open access
- closed
- Funders
- Army Research Laboratory (W911NF-10-2-0022)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
- Physical Sciences › Computer Science › Human-Computer Interaction › Gaze Tracking and Assistive Technology
- Keywords
- brain-computer interface, cross-domain learning, cross-subject generalization, deep learning
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