Deep Neural Network for Visual Stimulus-Based Reaction Time Estimation Using the Periodogram of Single-Trial EEG Article Swipe
Mohammad Samin Nur Chowdhury
,
Arindam Dutta
,
Matthew K. Robison
,
Chris Blais
,
Gene A. Brewer
,
Daniel W. Bliss
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.3390/s20216090
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.3390/s20216090
Multiplexed deep neural networks (DNN) have engendered high-performance predictive models gaining popularity for decoding brain waves, extensively collected in the form of electroencephalogram (EEG) signals. In this paper, to the best of our knowledge, we introduce a first-ever DNN-based generalized approach to estimate reaction time (RT) using the periodogram representation of single-trial EEG in a visual stimulus-response experiment with 48 participants. We have designed a Fully Connected Neural Network (FCNN) and a Convolutional Neural Network (CNN) to predict and classify RTs for each trial. Though deep neural networks are widely known for classification applications, cascading FCNN/CNN with the Random Forest model, we designed a robust regression-based estimator to predict RT. With the FCNN model, the accuracies obtained for binary and 3-class classification were 93% and 76%, respectively, which further improved with the use of CNN (94% and 78%, respectively). The regression-based approach predicted RTs with correlation coefficients (CC) of 0.78 and 0.80 for FCNN and CNN, respectively. Investigating further, we found that the left central as well as parietal and occipital lobes were crucial for predicting RT, with significant activities in the theta and alpha frequency bands.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/s20216090
- https://www.mdpi.com/1424-8220/20/21/6090/pdf?version=1603766461
- OA Status
- gold
- Cited By
- 16
- References
- 52
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3096084006
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- OpenAlex ID
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https://openalex.org/W3096084006Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/s20216090Digital Object Identifier
- Title
-
Deep Neural Network for Visual Stimulus-Based Reaction Time Estimation Using the Periodogram of Single-Trial EEGWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2020Year of publication
- Publication date
-
2020-10-27Full publication date if available
- Authors
-
Mohammad Samin Nur Chowdhury, Arindam Dutta, Matthew K. Robison, Chris Blais, Gene A. Brewer, Daniel W. BlissList of authors in order
- Landing page
-
https://doi.org/10.3390/s20216090Publisher landing page
- PDF URL
-
https://www.mdpi.com/1424-8220/20/21/6090/pdf?version=1603766461Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.mdpi.com/1424-8220/20/21/6090/pdf?version=1603766461Direct OA link when available
- Concepts
-
Electroencephalography, Convolutional neural network, Artificial intelligence, Pattern recognition (psychology), Estimator, Computer science, Artificial neural network, Decoding methods, Speech recognition, Regression, Mathematics, Algorithm, Psychology, Statistics, NeuroscienceTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
16Total citation count in OpenAlex
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2025: 3, 2024: 2, 2023: 5, 2022: 3, 2021: 3Per-year citation counts (last 5 years)
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52Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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