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 9437

Special Issue Editor

KTH Royal Institute of Technology, Deptarment Computational Science and Technology, Computational Brain Science Lab, S-10044 Stockholm, Sweden
Interests: brain–computer interfaces; computational neuroscience;EEG; neural information processing; fuzzy systems; machine learning
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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

Manuscript Submission Information

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Keywords

  • Electroencephalography (EEG)
  • Pattern recognition, classification, clustering
  • Brain–Computer Interface
  • Fuzzy systems
  • Probabilistic models
  • Neural networks

Published Papers (2 papers)

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Research

14 pages, 1783 KiB  
Article
EEG Theta Power Activity Reflects Workload among Army Combat Drivers: An Experimental Study
by Carolina Diaz-Piedra, María Victoria Sebastián and Leandro L. Di Stasi
Brain Sci. 2020, 10(4), 199; https://doi.org/10.3390/brainsci10040199 - 28 Mar 2020
Cited by 29 | Viewed by 4475
Abstract
We aimed to evaluate the effects of mental workload variations, as a function of the road environment, on the brain activity of army drivers performing combat and non-combat scenarios in a light multirole vehicle dynamic simulator. Forty-one non-commissioned officers completed three standardized driving [...] Read more.
We aimed to evaluate the effects of mental workload variations, as a function of the road environment, on the brain activity of army drivers performing combat and non-combat scenarios in a light multirole vehicle dynamic simulator. Forty-one non-commissioned officers completed three standardized driving exercises with different terrain complexities (low, medium, and high) while we recorded their electroencephalographic (EEG) activity. We focused on variations in the theta EEG power spectrum, a well-known index of mental workload. We also assessed performance and subjective ratings of task load. The theta EEG power spectrum in the frontal, temporal, and occipital areas were higher during the most complex scenarios. Performance (number of engine stops) and subjective data supported these findings. Our findings strengthen previous results found in civilians on the relationship between driver mental workload and the theta EEG power spectrum. This suggests that EEG activity can give relevant insight into mental workload variations in an objective, unbiased fashion, even during real training and/or operations. The continuous monitoring of the warfighter not only allows instantaneous detection of over/underload but also might provide online feedback to the system (either automated equipment or the crew) to take countermeasures and prevent fatal errors. Full article
(This article belongs to the Special Issue Handling Uncertainty in EEG Signal Pattern Recognition)
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22 pages, 12818 KiB  
Article
Improved EOG Artifact Removal Using Wavelet Enhanced Independent Component Analysis
by Mohamed F. Issa and Zoltan Juhasz
Brain Sci. 2019, 9(12), 355; https://doi.org/10.3390/brainsci9120355 - 04 Dec 2019
Cited by 39 | Viewed by 4556
Abstract
Electroencephalography (EEG) signals are frequently contaminated with unwanted electrooculographic (EOG) artifacts. Blinks and eye movements generate large amplitude peaks that corrupt EEG measurements. Independent component analysis (ICA) has been used extensively in manual and automatic methods to remove artifacts. By decomposing the signals [...] Read more.
Electroencephalography (EEG) signals are frequently contaminated with unwanted electrooculographic (EOG) artifacts. Blinks and eye movements generate large amplitude peaks that corrupt EEG measurements. Independent component analysis (ICA) has been used extensively in manual and automatic methods to remove artifacts. By decomposing the signals into neural and artifactual components and artifact components can be eliminated before signal reconstruction. Unfortunately, removing entire components may result in losing important neural information present in the component and eventually may distort the spectral characteristics of the reconstructed signals. An alternative approach is to correct artifacts within the independent components instead of rejecting the entire component, for which wavelet transform based decomposition methods have been used with good results. An improved, fully automatic wavelet-based component correction method is presented for EOG artifact removal that corrects EOG components selectively, i.e., within EOG activity regions only, leaving other parts of the component untouched. In addition, the method does not rely on reference EOG channels. The results show that the proposed method outperforms other component rejection and wavelet-based EOG removal methods in its accuracy both in the time and the spectral domain. The proposed new method represents an important step towards the development of accurate, reliable and automatic EOG artifact removal methods. Full article
(This article belongs to the Special Issue Handling Uncertainty in EEG Signal Pattern Recognition)
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