Modern Problems of Brain-Signal Analysis and Approaches to Their Solution

Furman, Ya. A.; Sevastyanov, V. V.; Ivanov, K. O.

doi:10.1134/s1054661819010103

Cited by 3 publications

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“…The frequency bands of the EEG signal (α, β, δ, θ, γ) are distinct and analyzed by statistical tools, in relation to their spectrum amplitude, coherence, latency, etc. Automatic classification (recognition) of EEG is the inevitable direction in analyzing these signals, however, quality criteria of the signal and its disturbances remain not categorically analyzed by neuroscientists in their research ( Furman et al, 2019 ), as the superposition of elementary sources of pyramidal neurons. These elements may be the reason for recommending individual models (or user-dependents) so that a generalization is challenging in the current context.…”

Section: Introductionmentioning

confidence: 99%

Attention Detection in Virtual Environments Using EEG Signals: A Scoping Review

Souza

Naves

2021

Front. Physiol.

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The competitive demand for attention is present in our daily lives, and the identification of neural processes in the EEG signals associated with the demand for specific attention can be useful to the individual’s interactions in virtual environments. Since EEG-based devices can be portable, non-invasive, and present high temporal resolution technology for recording neural signal, the interpretations of virtual systems user’s attention, fatigue and cognitive load based on parameters extracted from the EEG signal are relevant for several purposes, such as games, rehabilitation, and therapies. However, despite the large amount of studies on this subject, different methodological forms are highlighted and suggested in this work, relating virtual environments, demand of attention, workload and fatigue applications. In our summarization, we discuss controversies, current research gaps and future directions together with the background and final sections.

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Section: Introductionmentioning

confidence: 99%