Recurrent and Convolutional Neural Networks in Classification of EEG Signal for Guided Imagery and Mental Workload Detection

• URL: http://arxiv.org/abs/2405.16901v2
• Date: Tue, 28 May 2024 11:29:06 GMT
• Title: Recurrent and Convolutional Neural Networks in Classification of EEG Signal for Guided Imagery and Mental Workload Detection
• Authors: Filip Postepski, Grzegorz M. Wojcik, Krzysztof Wrobel, Andrzej Kawiak, Katarzyna Zemla, Grzegorz Sedek,
• Abstract summary: 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.
• Score: 0.9895793818721335
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• Deep Learning-based Classification of Dementia using Image Representation of Subcortical Signals [4.17085180769512]
  Alzheimer's disease (AD) and Frontotemporal dementia (FTD) are the common forms of dementia, each with distinct progression patterns. This study aims to develop a deep learning-based classification system for dementia by analyzing scout time-series signals from deep brain regions.
  arXiv  Detail & Related papers  (2024-08-20T13:11:43Z)
• Graph Neural Networks with Trainable Adjacency Matrices for Fault Diagnosis on Multivariate Sensor Data [69.25738064847175]
  It is necessary to consider the behavior of the signals in each sensor separately, to take into account their correlation and hidden relationships with each other. The graph nodes can be represented as data from the different sensors, and the edges can display the influence of these data on each other. It was proposed to construct a graph during the training of graph neural network. This allows to train models on data where the dependencies between the sensors are not known in advance.
  arXiv  Detail & Related papers  (2022-10-20T11:03:21Z)
• Towards better Interpretable and Generalizable AD detection using Collective Artificial Intelligence [0.0]
  Deep learning methods have been proposed to automate diagnosis and prognosis of Alzheimer's disease. These methods often suffer from a lack of interpretability and generalization. We propose a novel deep framework designed to overcome these limitations.
  arXiv  Detail & Related papers  (2022-06-07T13:02:53Z)
• A Novel Capsule Neural Network Based Model for Drowsiness Detection Using Electroencephalography Signals [0.0]
  Capsule Neural Networks are a brand-new Deep Learning algorithm proposed for work with reduced amounts of data. This paper presents a Deep Learning-based method for drowsiness detection with CapsNet by using a concatenation of spectrogram images of the electroencephalography signals channels.
  arXiv  Detail & Related papers  (2022-04-04T17:23:53Z)
• Ensemble of Convolution Neural Networks on Heterogeneous Signals for Sleep Stage Scoring [63.30661835412352]
  This paper explores and compares the convenience of using additional signals apart from electroencephalograms. The best overall model, an ensemble of Depth-wise Separational Convolutional Neural Networks, has achieved an accuracy of 86.06%.
  arXiv  Detail & Related papers  (2021-07-23T06:37:38Z)
• Convolutional Neural Networks for Sleep Stage Scoring on a Two-Channel EEG Signal [63.18666008322476]
  Sleep problems are one of the major diseases all over the world. Basic tool used by specialists is the Polysomnogram, which is a collection of different signals recorded during sleep. Specialists have to score the different signals according to one of the standard guidelines.
  arXiv  Detail & Related papers  (2021-03-30T09:59:56Z)
• Raspberry Pi Based Intelligent Robot that Recognizes and Places Puzzle Objects [1.1470070927586016]
  Non-linear secondorder difference plot (SODP) is used to diagnose congestive heart failure (CHF) patients. It is shown that using neural network classifier with features obtained from SODP, the constructed system could distinguish normal and CHF patients with 100% accuracy rate.
  arXiv  Detail & Related papers  (2021-01-20T18:58:59Z)
• Emotional EEG Classification using Connectivity Features and Convolutional Neural Networks [81.74442855155843]
  We introduce a new classification system that utilizes brain connectivity with a CNN and validate its effectiveness via the emotional video classification. The level of concentration of the brain connectivity related to the emotional property of the target video is correlated with classification performance.
  arXiv  Detail & Related papers  (2021-01-18T13:28:08Z)
• An Uncertainty-Driven GCN Refinement Strategy for Organ Segmentation [53.425900196763756]
  We propose a segmentation refinement method based on uncertainty analysis and graph convolutional networks. We employ the uncertainty levels of the convolutional network in a particular input volume to formulate a semi-supervised graph learning problem. We show that our method outperforms the state-of-the-art CRF refinement method by improving the dice score by 1% for the pancreas and 2% for spleen.
  arXiv  Detail & Related papers  (2020-12-06T18:55:07Z)
• Automatic Recognition of the Supraspinatus Tendinopathy from Ultrasound Images using Convolutional Neural Networks [1.021325814813899]
  An automatic tendinopathy recognition framework based on convolutional neural networks has been proposed. Tendon segmentation is done through a novel network, NASUNet. A general classification pipeline has been proposed for tendinopathy recognition.
  arXiv  Detail & Related papers  (2020-11-23T22:41:41Z)
• Training Binary Neural Networks through Learning with Noisy Supervision [76.26677550127656]
  This paper formalizes the binarization operations over neural networks from a learning perspective. Experimental results on benchmark datasets indicate that the proposed binarization technique attains consistent improvements over baselines.
  arXiv  Detail & Related papers  (2020-10-10T01:59:39Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.