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Context-aware adaptive spelling in motor imagery BCI
S Perdikis1, R Leeb, J D R Millán
1Chair in Brain-Machine Interface, Center for Neuroprosthetics, Institute of Bioengineering, School of Engineering, École Polytechnique Fédérale de Lausanne, Campus Biotech H4, Chemin des Mines 9, CH-1202, Geneva, Switzerland.
Journal of Neural Engineering
|May 7, 2016
Summary
This novel brain-computer interface (BCI) speller uses context to adapt in real-time, improving spelling performance and reducing the need for recalibration for users.
Area of Science:
- Neuroscience
- Computer Science
- Rehabilitation Engineering
Background:
- Brain-computer interfaces (BCIs) offer communication pathways for individuals with severe motor impairments.
- Motor imagery (MI)-based BCIs rely on decoding brain signals related to imagined movements.
- Signal non-stationarity and the need for extensive calibration pose significant challenges for BCI usability.
Purpose of the Study:
- To introduce the first motor imagery-based adaptive BCI speller that leverages application context.
- To improve simultaneous classifier adaptation and spelling accuracy.
- To evaluate the system's ability to handle brain signal non-stationarity and guide naive users.
Main Methods:
- Developed a co-adaptive framework integrating the BrainTree speller with smooth-batch linear discriminant analysis (SB-LDA).
- Utilized BrainTree's language model for contextual assistance to enhance online expectation-maximization maximum-likelihood estimation in SB-LDA.
- Conducted online spelling experiments with ten able-bodied users to assess performance.
Main Results:
- The system successfully restored single-sample classification and BCI command accuracy, enhancing spelling speed for expert users.
- Context-aware adaptation significantly outperformed unsupervised adaptation and matched supervised adaptation performance.
- The proposed algorithm proved advantageous for 30% of users compared to the state-of-the-art PMean approach.
Conclusions:
- Demonstrated the feasibility of bypassing supervised BCI recalibration without compromising adaptation quality.
- Highlighted the potential for context-aware adaptation to improve BCI performance and user experience.
- Indicated that this adaptation method may not be optimal for initial BCI training phases.
