Trends in Online EEG Pattern Recognition: New Methods and Emerging Non-Medical Applications

Dear Colleagues,

The online analysis and interpretation of electrical brain activity has long been exploited, mostly in medical contexts and for clinical applications. With the advent of brain–computer interfacing (BCI), the scope of applications has grown, particularly for systems relying on online (often real-time) electroencephalography (EEG)-based neurofeedback, enabling closed-loop human (brain)–computer interaction. Despite tremendous engineering efforts, however, the robustness of EEG-based BCI technology remains a concern, especially in real-time scenarios. Some of the most urgent challenges in online EEG pattern recognition include the need to effectively handle uncertainties associated with the complexity and variability of brain dynamics, the capability for unsupervised and semi-supervised learning from unlabeled or scarcely/vaguely annotated EEG data, and the scope for multi-level adaptation, among others. Although there have been various methods proposed for addressing these challenges, typically exploiting machine-learning techniques and probabilistic inference as well as adaptive computational frameworks, there is no consensus about their effectiveness in EEG pattern recognition.   

This Special Issue aims at highlighting demanding aspects of automated online EEG analysis. In particular, it is intended to demonstrate recent methodological advances facilitating robust EEG signal pattern recognition, particularly in real-time scenarios with neurofeedback. Although the emphasis is on new approaches to uncertainty handling, online adaptation and learning from only a few labeled EEG trials, contributions reflecting recent methodological progress or research showcasing key challenges in robust online EEG analysis and interpretation is of considerable relevance. Finally, this Special Issue will also consider emerging research applications of online EEG pattern recognition, BCI in particular, beyond the medical domain, e.g., in studies of human cognition and behavior. We look forward to receiving your contributions to this Special Issue.

Dr. Pawel Andrzej Herman
Guest Editor

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• brain–computer interface
• cognitive neuroimaging
• electroencephalography (EEG)
• neurofeedback
• pattern recognition
• semi-supervised learning
• real-time EEG and single-trial analysis
• adaptive interfaces