Towards Fast Single-Trial Online ERP based Brain-Computer Interface using dry EEG electrodes and neural networks: a pilot study
This work addresses improving the practicality of ERP-based BCIs for users, though it is incremental as it builds on existing stimulus and deep learning methods.
The study tackled the challenge of speeding up spelling in ERP-based BCIs by combining colored inverted face stimulation with convolutional neural networks, achieving high online accuracy with two subjects exceeding 90% correct symbol detection and a transfer rate above 60 bits per minute.
Speeding up the spelling in event-related potentials (ERP) based Brain-Computer Interfaces (BCI) requires eliciting strong brain responses in a short span of time, as much as the accurate classification of such evoked potentials remains challenging and imposes hard constraints for signal processing and machine learning techniques. Recent advances in stimulus presentation and deep learning showcased a promising direction in significantly improving the efficacy of those systems, in this study we propose the combination of colored inverted face stimulation with classification using convolutional neural networks in the hard settings of dry electrodes and fast flashing single-trial ERP-based BCI. The high online accuracy achieved, with two subjects passing the 90 percent correct symbol detection bar and a transfer rate above 60 bits per minute, demonstrates the approach potential in improving the practicality of ERP based BCIs.