Eeg Indices To Time-On-Task Effects And To A Workload Manipulation (Cueing)

Kamzanova, Altyngul; Matthews, Gerald; Kustubayeva, Almira; Jakupov, S.M.

doi:10.5281/zenodo.1071802

Cited by 5 publications

(1 citation statement)

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“…For example, the alpha band (8-12 Hz) is known to be more active in stressed individuals and the theta (4-7 Hz) and low beta (13)(14)(15)(16)(17)(18)(19)(20) bands are known to be active in fatigued persons (Nayak and Anilkumar, 2021). Simple functions of the relative masses of the sub-waveforms are also popular and useful features (Kamzanova et al, 2011). Similarly, functions of data from two channels of the EEG-device (such as frontal alpha asymmetry) have been shown to have different characteristics under different mental states and can be useful features for classification (Fischer et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Mental State Classification Using Multi-Graph Features

Chen

Helm

Lytvynets

et al. 2022

Front. Hum. Neurosci.

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We consider the problem of extracting features from passive, multi-channel electroencephalogram (EEG) devices for downstream inference tasks related to high-level mental states such as stress and cognitive load. Our proposed feature extraction method uses recently developed spectral-based multi-graph tools and applies them to the time series of graphs implied by the statistical dependence structure (e.g., correlation) amongst the multiple sensors. We study the features in the context of two datasets each consisting of at least 30 participants and recorded using multi-channel EEG systems. We compare the classification performance of a classifier trained on the proposed features to a classifier trained on the traditional band power-based features in three settings and find that the two feature sets offer complementary predictive information. We conclude by showing that the importance of particular channels and pairs of channels for classification when using the proposed features is neuroscientifically valid.

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Section: Introductionmentioning

confidence: 99%

Mental State Classification Using Multi-Graph Features

Chen

Helm

Lytvynets

et al. 2022

Front. Hum. Neurosci.

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Don’t Think Twice, It’s All Right? – An Examination of Commonly Used EEG Indices and Their Sensitivity to Mental Workload

Hamann

Carstengerdes

2023

Lecture Notes in Computer Science

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Method for Selecting an Engagement Index for a Specific Type of Game Using Cognitive Neuroscience

Biercewicz

Borawski

Duda

2020

International Journal of Computer Games Technology

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The popularity of video games means that methods are needed to assess their content in terms of player satisfaction right from the production stage. For this purpose, the indicators used in EEG studies can be used. This publication presents a method that has been developed to determine whether a person likes an arcade game. To this end, six different indicators to measure consumer involvement in a video game using the EEG were compared, among others. The study was conducted using several different games created in Unity based on the observation (n=31) of the respondents. EEG has been used to select the most suitable indices studied.

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Eeg Indices To Time-On-Task Effects And To A Workload Manipulation (Cueing)

Cited by 5 publications

References 0 publications

Mental State Classification Using Multi-Graph Features

Mental State Classification Using Multi-Graph Features

Don’t Think Twice, It’s All Right? – An Examination of Commonly Used EEG Indices and Their Sensitivity to Mental Workload

Method for Selecting an Engagement Index for a Specific Type of Game Using Cognitive Neuroscience

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