Recurrent and convolutional neural networks in classification of EEG signal for guided imagery and mental workload detection
Recurrent and convolutional neural networks in classification of EEG signal for guided imagery and mental workload detection
StatusVoR
Alternative title
Authors
Postepski, Filip
Wójcik, Grzegorz M.
Wróbel, Krzysztof
Kawiak, Andrzej
Zemla, Katarzyna
Sędek, Grzegorz
Monograph
Monograph (alternative title)
Date
2025-03-27
Publisher
Journal title
Scientific Reports
Issue
1
Volume
15
Pages
Pages
1-18
ISSN
2045-2322
ISSN of series
Access date
2025-03-27
Abstract PL
Abstract EN
The Guided Imagery technique is reported to be used by therapists all over the world in order to increase the comfort of patients suffering from a variety of disorders from mental to oncology ones and proved to be successful in numerous of ways. Possible support for the therapists can be estimation of the time at which subject goes into deep relaxation. This paper presents the results of the investigations of a cohort of 26 students exposed to Guided Imagery relaxation technique and mental task workloads conducted with the use of dense array electroencephalographic amplifier. The research reported herein aimed at verification whether it is possible to detect differences between those two states and to classify them using deep learning methods and recurrent neural networks such as EEGNet, Long Short-Term Memory-based classifier, 1D Convolutional Neural Network and hybrid model of 1D Convolutional Neural Network and Long Short-Term Memory. The data processing pipeline was presented from the data acquisition, through the initial data cleaning, preprocessing and postprocessing. The classification was based on two datasets: one of them using 26 so-called cognitive electrodes and the other one using signal collected from 256 channels. So far there have not been such comparisons in the application being discussed. The classification results are presented by the validation metrics such as: accuracy, recall, precision, F1-score and loss for each case. It turned out that it is not necessary to collect signals from all electrodes as classification of the cognitive ones gives the results similar to those obtained for the full signal and extending input to 256 channels does not add much value. In Disscussion there were proposed an optimal classifier as well as some suggestions concerning the prospective development of the project.
Abstract other
Keywords PL
Keywords EN
Guided imagery
Mental workload
EEG
CNN
LSTM
Mental workload
EEG
CNN
LSTM
Keywords other
Exhibition title
Place of exhibition (institution)
Exhibition curator
Type
License type
Funder
Time range from
Time range to
Contact person name
Related publication
Related publication
Grant/project name
Collections
Views
4Acquisition Date3.04.2025
Downloads
2Acquisition Date3.04.2025
Altmetrics©
Dimensions
Google Scholar
