Deep Neural Network for Visual Stimulus-Based Reaction Time Estimation Using the Periodogram of Single-Trial EEG

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...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2020-10, Vol.20 (21), p.6090
Main Authors: Chowdhury, Mohammad Samin Nur, Dutta, Arindam, Robison, Matthew Kyle, Blais, Chris, Brewer, Gene Arnold, Bliss, Daniel Wesley
Format: Article
Language:English
Subjects:
Algorithms
brain–computer interface
Classification
Convolutional Neural Network (CNN)
Correlation coefficients
deep learning
EEG signal processing
Electroencephalography
Experiments
Field programmable gate arrays
Frequencies
Fully Connected Neural Network (FCNN)
Human performance
Humans
Mean square errors
Model accuracy
Neural networks
Neural Networks, Computer
Occipital lobes
periodogram
Phenomenology
Prediction models
Principal components analysis
Reaction Time
Regression analysis
Signal processing
Software
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Summary: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 and frequency bands.
ISSN:1424-8220
1424-8220
DOI:10.3390/s20216090