Automated EEG artifact elimination by applying machine learning algorithms to ICA-based features

Radüntz, Thea; Scouten, Jon; Hochmuth, Olaf; Meffert, Beate

doi:10.1088/1741-2552/aa69d1

Cited by 115 publications

(67 citation statements)

References 21 publications

(27 reference statements)

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“…The focus was on frontal, parietal and occipital EEG channels according to previous findings. Independent component analysis (ICA) was used to determine specific reactions of spatio-temporal different sources (Gardony et al, 2017) and allowed the successful detection and elimination of artifacts (Mognon et al, 2011;Radüntz et al, 2017;Puma et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Indexing Mental Workload During Simulated Air Traffic Control Tasks by Means of Dual Frequency Head Maps

et al. 2020

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In our digitized society, advanced information and communication technology and highly interactive work environments impose high demands on cognitive capacity. Optimal workload conditions are important for assuring employee's health and safety of other persons. This is particularly relevant in safety-critical occupations, such as air traffic control. For measuring mental workload using the EEG, we have developed the method of Dual Frequency Head Maps (DFHM). The method was tested and validated already under laboratory conditions. However, validation of the method regarding reliability and reproducibility of results under realistic settings and real world scenarios was still required. In our study, we examined 21 air traffic controllers during arrival management tasks. Mental workload variations were achieved by simulation scenarios with different number of aircraft and the occurrence of a priority-flight request as an exceptional event. The workload was assessed using the EEG-based DFHM-workload index and instantaneous self-assessment questionnaire. The DFHM-workload index gave stable results with highly significant correlations between scenarios with similar traffic-load conditions (r between 0.671 and 0.809, p ≤ 0.001). For subjects reporting that they experienced workload variation between the different scenarios, the DFHM-workload index yielded significant differences between traffic-load levels and priority-flight request conditions. For subjects who did not report to experience workload variations between the scenarios, the DFHM-workload index did not yield any significant differences for any of the factors. We currently conclude that the DFHM-workload index reveals potential for applications outside the laboratory and yields stable results without retraining of the classifiers neither regarding new subjects nor new tasks.

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Section: Introductionmentioning

confidence: 99%

Indexing Mental Workload During Simulated Air Traffic Control Tasks by Means of Dual Frequency Head Maps

et al. 2020

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“…The technique is however not perfect, and as previously mentioned, relevant brain signal might have been filtered out and some noise signals might still be present. Machine learning approaches for further automation of these decomposition-based rejection methods have been proposed [111,112]. Another approach to minimize the effect of artifacts is by proper feature selection representing the signal of interest.…”

Section: Challenges and Prospects For Neurofeedback Systemsmentioning

confidence: 99%

A framework for closed-loop neurofeedback for real-time EEG decoding

Tuckute

Kjaer

et al. 2019

Preprint

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Neurofeedback based on real-time brain imaging allows for targeted training of brain activity with demonstrated clinical applications. A rapid technical development of electroencephalography (EEG)-based systems and an increasing interest in cognitive training has lead to a call for accessible and adaptable software frameworks. Here, we present and outline the core components of a novel open-source neurofeedback framework based on scalp EEG signals for real-time neuroimaging, state classification and closed-loop feedback.The software framework includes real-time signal preprocessing, adaptive artifact rejection, and cognitive state classification from scalp EEG. The framework is implemented using exclusively Python source code to allow for diverse functionality, high modularity, and easy extendibility of software development depending on the experimenter's needs.As a proof of concept, we demonstrate the functionality of our new software framework by implementing an attention training paradigm using a consumer-grade, dry-electrode EEG system. Twenty-two participants were trained on a single neurofeedback session with behavioral pre-and post-training sessions within three consecutive days. We demonstrate a mean decoding error rate of 34.3% (chance=50%) of subjective attentional states. Hence, cognitive states were decoded in real-time by continuously updating classification models on recently recorded EEG data without the need for any EEG recordings prior to the neurofeedback session.The proposed software framework allows a wide range of users to actively engage in the development of novel neurofeedback tools to accelerate improvements in neurofeedback as a translational and therapeutic tool.

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“…The practical use of ICA has been limited by its computational cost and the need for user intervention. Only recently, a real-time recursive ICA algorithm has been proposed (Hsu et al, 2016), as well as a number of automatic methods for minimizing the subjectivity of manual component selection (Tamburro et al, 2018;Pion-Tonachini et al, 2017;Radüntz et al, 2017). Despite these advances, turning ICA into a brain imaging modality requires that after source separation, we solve the inverse problem of localizing the set of identified brain components into the cortical space.…”

Section: Introductionmentioning

confidence: 99%

A Bayesian framework for unifying data cleaning, source separation and imaging of electroencephalographic signals

Ojeda

Klug

Kreutz-Delgado

et al. 2019

Preprint

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Electroencephalographic (EEG) source imaging depends upon sophisticated signal processing algorithms to deal with the problems of data cleaning, source separation, and localization. Typically, these problems are sequentially addressed by independent heuristics, limiting the use of EEG images on a variety of applications. Here, we propose a unifying empirical Bayes framework in which these dissimilar problems can be solved using a single algorithm. We use spatial sparsity constraints to adaptively segregate brain sources into maximally independent components with known anatomical support, while minimally overlapping artifactual activity. The framework yields a recursive inverse spatiotemporal filter that can be used for offline and online applications. We call this filter Recursive Sparse Bayesian Learning (RSBL). Of theoretical relevance, we demonstrate the connections between Infomax Independent Component Analysis and RSBL. We use simulations to show that RSBL can separate and localize cortical and artifact components that overlap in space and time from noisy data. On real data, we use RSBL to analyze single-trial error-related potentials, finding sources in the cingulate gyrus. We further benchmark our algorithm on two unrelated EEG studies showing that: 1) it outperforms Infomax for source separation on short time-scales and 2), unlike the popular Artifact Subspace Removal algorithm, it can reduce artifacts without significantly distorting clean epochs. Finally, we analyze mobile brain/body imaging data to characterize the brain dynamics supporting heading computation during full-body rotations, replicating the main findings of previous experimental literature.

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Automated EEG artifact elimination by applying machine learning algorithms to ICA-based features

Cited by 115 publications

References 21 publications

Indexing Mental Workload During Simulated Air Traffic Control Tasks by Means of Dual Frequency Head Maps

Indexing Mental Workload During Simulated Air Traffic Control Tasks by Means of Dual Frequency Head Maps

A framework for closed-loop neurofeedback for real-time EEG decoding

A Bayesian framework for unifying data cleaning, source separation and imaging of electroencephalographic signals

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