Mental Workload Estimation Using EEG

Title

Mental Workload Estimation Using EEG

Creator

Pandey V.; Choudhary D.K.; Verma V.; Sharma G.; Singh R.; Chandra S.

Description

Mental workload contributes considerably to the outcome or the performance of any task. The concern of human workload increases during a human-machine collaboration task or in a multitasking environment. This paper presents a comparative study of machine learning algorithms used to estimate workload using Electroencephalography (EEG) data. An open-access EEG dataset acquired during a 'simultaneous capacity (SIMKAP) experiment' and 'no task' is used to create and validate models for binary classification of workload as present and absent respectively. The paper presents an implementation of various classification models that use EEG data to predict the workload. In this paper, implementation for KNN classifier (57.3%), Random Forest classifier (57.19%), MLP network classifier (58.2%), CNN+ LSTM network classifier (58.68%), and LSTM network classifier (61.08%) has been reported. The paper can be further extended to study operator workload in real-time using a brain-computer interface paradigm for any kind of task in a real-world application. The workload classification can be further used in human-machine tasks to decide task allocation between the system to achieve optimal performance in a complex critical system. 2020 IEEE.

Source

Proceedings - 2020 5th International Conference on Research in Computational Intelligence and Communication Networks, ICRCICN 2020, pp. 83-86.

Date

2020-01-01

Publisher

Institute of Electrical and Electronics Engineers Inc.

Subject

Deep Learning; Electroencephalography; Machine Learning; Mental Workload

Coverage

Pandey V., Institute of Nuclear Medicine and Allied Sciences, DRDO, Department of Biomedical Engineering, Delhi, India; Choudhary D.K., Christ University, Department of Computer Science, Bangalore, India; Verma V., Vivekanand Institute of Professional Studies, Department of Computer Science, Delhi, India; Sharma G., Institute of Nuclear Medicine and Allied Sciences, DRDO, Department of Biomedical Engineering, Delhi, India; Singh R., Institute of Nuclear Medicine and Allied Sciences, DRDO, Department of Biomedical Engineering, Delhi, India; Chandra S., Institute of Nuclear Medicine and Allied Sciences, DRDO, Department of Biomedical Engineering, Delhi, India

Rights

Restricted Access

Relation

ISBN: 978-172818818-8

Format

Online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1109/ICRCICN50933.2020.9296150

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85099196415&doi=10.1109%2fICRCICN50933.2020.9296150&partnerID=40&md5=5a9fb33a5d505c3120f5a47047049c5c