Classification Mental Workload Levels from EEG Signals with 1D Convolutional Neural Network

Author:

Baydemir RecepORCID,Latifoğlu FatmaORCID,Orhanbulucu FıratORCID

Abstract

Mental workload (MWL) can be estimated according to the state of cognitive capacity after an activity. In this study, it is aimed to classify MWL levels from Electroencephalogram (EEG) signals recorded from a task moment. Using the proposed one-dimensional convolutional neural network (1D-CNN) model in the study, low (L) and high (H) level WL states were classified. The classification process was carried out in two stages. EEG signals passed through the preprocessing stage were classified with 1D-CNN in the first stage. In the second step, these signals were decomposed into subbands by applying Empirical Mode Decomposition (EMD) and classified with 1D-CNN. As a result of the classification process, accuracy (Acc), sensitivity (Sens), and specificity (Spe) values were obtained and evaluated in this study. As a result of the evaluation, the most successful Acc rate was 98.4%, Sens rate 97.62%, and Spe rate 98.94%

Publisher

Orclever Science and Research Group

Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. A novel approach for Parkinson’s disease detection using Vold-Kalman order filtering and machine learning algorithms;Neural Computing and Applications;2024-02-27

2. Attention based 1D-CNN for Mental Workload Classification using EEG;Proceedings of the 16th International Conference on PErvasive Technologies Related to Assistive Environments;2023-07-05

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