Handling Uncertainty in EEG Signal Pattern Recognition
A special issue of Brain Sciences (ISSN 2076-3425). This special issue belongs to the section "Neural Engineering, Neuroergonomics and Neurorobotics".
Deadline for manuscript submissions: closed (30 June 2020) | Viewed by 11848
Special Issue Editor
Interests: brain–computer interfaces; computational neuroscience;EEG; neural information processing; fuzzy systems; machine learning
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
One of the urgent challenges in the automated analysis and interpretation of electrical brain activity is the effective handling of uncertainties associated with the complexity and variability of brain dynamics, reflected among others in the nonstationary nature of brain signals such as electroencephalogram (EEG). This poses a severe problem for existing approaches to EEG pattern recognition in a wide range of applications, from classification in on-line brain–computer interfaces (BCIs) or off-line diagnostic systems to unsupervised or semi-supervised learning algorithms for exploratory EEG analysis. Although there have been various methods proposed to mitigate the adverse effects of uncertainty in EEG signal pattern recognition tasks, typically exploiting fuzzy logic apparatus, probabilistic inference or adaptive computational frameworks, systematic evaluation of uncertainty handling capabilities has not been sufficiently emphasized in the literature.
This Special Issue aims at highlighting this challenging aspect of automated EEG analysis. In particular, it is intended to demonstrate methodological advances in modeling and accounting for a variety of uncertainty sources in EEG signal pattern recognition problems. Despite this methodological angle, research showcasing key challenges in robust handling uncertainty phenomena within a broad scope of EEG based applications is of considerable relevance. We look forward to receiving your contribution to this Special Issue.
Dr. Pawel Andrzej Herman
Guest Editor
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Keywords
- Electroencephalography (EEG)
- Pattern recognition, classification, clustering
- Brain–Computer Interface
- Fuzzy systems
- Probabilistic models
- Neural networks
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