A comprehensive dataset for home appliance control using ERP-based BCIs with the application of inter-subject transfer learning
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View abstract on PubMed
Summary
This summary is machine-generated.This study introduces a new dataset for brain-computer interfaces (BCIs) that use electroencephalography (EEG) and event-related potentials (ERPs) to control home appliances. Transfer learning significantly improved BCI performance across diverse users and environments.
Area Of Science
- Neuroscience
- Computer Science
- Human-Computer Interaction
Background
- Brain-computer interfaces (BCIs) offer revolutionary human-computer interaction by directly linking brain activity to computer systems.
- Non-invasive BCIs utilizing electroencephalography (EEG) and event-related potentials (ERPs) show promise for practical applications like home appliance control.
Purpose Of The Study
- To present a comprehensive dataset of online ERP-based BCIs for controlling various home appliances in diverse environments.
- To address inter-subject variability in ERPs using transfer learning for improved BCI generalizability.
Main Methods
- Collected online BCI data from 84 subjects controlling appliances (TV, door lock, light, speaker, air conditioner) via LCD and augmented reality (AR).
- Employed two transfer learning approaches: 'within-paradigm' for generalization within the same stimulus presentation, and 'cross-paradigm' to extend models to different environments.
Main Results
- Transfer learning effectively enhanced the generalizability of ERP-based BCIs.
- Demonstrated successful application of transfer learning across different subjects and diverse stimulus presentation environments.
Conclusions
- The developed dataset and transfer learning methods significantly improve the robustness and applicability of ERP-based BCIs for real-world home appliance control.
- This work paves the way for more personalized and adaptive BCI systems.
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