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Applied Sciences
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Electroencephalography (EEG) in Assessment of Engagement and Workload
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Electroencephalography (EEG) in Assessment of Engagement and Workload

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Biomedical Engineering".

Deadline for manuscript submissions: closed (20 November 2024) | Viewed by 3198

Special Issue Editors

Dr. Yelena Tonoyan

E-Mail Website
Guest Editor
Rehab Technologies Lab, Istituto Italiano di Tecnologia, Via Morego 30, 16163 Genoa, Italy
Interests: computational neuroscience; human–robot interaction; cognitive neuroscience
Prof. Dr. Giacinto Barresi

E-Mail Website
Guest Editor
Bristol Robotics Laboratory, University of the West of England, Bristol BS16 1QY, UK
Interests: neuroergonomics; biomedical robotics; human–robot interaction; human augmentation; rehabilitation technology; assistive technology; prosthetics; extended reality; digital health; gamification
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Honglin Hu

E-Mail Website
Guest Editor
University of Chinese Academy of Sciences, Beijing, China
Interests: signal and communication systems; mobile communication systems and wireless networks; brain computer interface technology

Special Issue Information

Dear Colleagues,

Being an agent in a variety of complex systems, a human puts mental and attentional efforts to providing efficiency to the operational environment, as occurs in risky professional activities like surgeries. This also occurs when an individual with motor impairments controls assistive or rehabilitative devices based on brain–computer interfaces (BCIs) or robotic systems (like exoskeletons and prosthetic limbs). In such contexts, it becomes crucially important to investigate how humans perceive the difficulty of performing demanding tasks. Within this domain, the concepts of engagement and mental workload play a fundamental role in understanding the user experience and the human–system performance, as pondered in several studies of cognitive ergonomics and neuroergonomics. Furthermore, electroencephalography (EEG) can offer impactful indices and biomarkers for the assessment of engagement and mental workload during the task performance. This way, it would be possible to improve the design of interaction technologies even by enriching them with neuroadaptive features that demonstrate the potential of BCIs for the elicitation of engagement and the mitigation of mental workload in several contexts of human assistance and augmentation.

Dr. Yelena Tonoyan
Dr. Giacinto Barresi
Prof. Dr. Honglin Hu
Guest Editors

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Keywords

  • electroencephalography (EEG)
  • mental workload
  • engagement
  • brain–computer interface (BCI)

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Published Papers (2 papers)

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Research

15 pages, 477 KiB  
Article
Engagement Recognition Using a Multi-Domain Feature Extraction Method Based on Correlation-Based Common Spatial Patterns
by Guiying Xu, Zhenyu Wang, Tianheng Xu, Ting Zhou and Honglin Hu
Appl. Sci. 2023, 13(21), 11924; https://doi.org/10.3390/app132111924 - 31 Oct 2023
Viewed by 1104
Abstract
Engagement ability plays a fundamental role in allocating attentional resources and helps us perform daily tasks efficiently. Therefore, it is of great importance to recognize engagement level. Electroencephalography is frequently employed to recognize engagement for its objective and harmless nature. To fully exploit [...] Read more.
Engagement ability plays a fundamental role in allocating attentional resources and helps us perform daily tasks efficiently. Therefore, it is of great importance to recognize engagement level. Electroencephalography is frequently employed to recognize engagement for its objective and harmless nature. To fully exploit the information contained in EEG signals, an engagement recognition method integrating multi-domain information is proposed. The proposed method extracts frequency information by a filter bank. In order to utilize spatial information, the correlation-based common spatial patterns method is introduced and extended into three versions by replacing different correlation coefficients. In addition, the Hilbert transform helps to obtain both amplitude and phase information. Finally, features in three domains are combined and fed into a support vector machine to realize engagement recognition. The proposed method is experimentally validated on an open dataset composed of 29 subjects. In the comparison with six existing methods, it achieves the best accuracy of 87.74±5.98% in binary engagement recognition with an improvement of 4.03%, which proves its efficiency in the engagement recognition field. Full article
(This article belongs to the Special Issue Electroencephalography (EEG) in Assessment of Engagement and Workload)
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14 pages, 1500 KiB  
Article
Comparing EEG-Based Epilepsy Diagnosis Using Neural Networks and Wavelet Transform
by Mohammad Reza Yousefi, Amin Dehghani, Saina Golnejad and Melika Mohammad Hosseini
Appl. Sci. 2023, 13(18), 10412; https://doi.org/10.3390/app131810412 - 18 Sep 2023
Cited by 5 | Viewed by 1504
Abstract
Epilepsy is a common neurological disorder characterized by the recurrence of seizures, which can significantly impact the lives of patients. Electroencephalography (EEG) can provide important physiological information on human brain activity which can be useful to diagnose epilepsy. However, manual analysis and visual [...] Read more.
Epilepsy is a common neurological disorder characterized by the recurrence of seizures, which can significantly impact the lives of patients. Electroencephalography (EEG) can provide important physiological information on human brain activity which can be useful to diagnose epilepsy. However, manual analysis and visual inspection of many EEG signals can be time-consuming and may lead to contradictory diagnoses by doctors. EEG signals play an important role in the diagnosis of epilepsy, as the quantification of cerebral signal anomalies may indicate the condition and the pathology of the cerebral signal. In this study, we attempted to develop a two-step process for the automated diagnosis of epilepsy using EEG signals. In the first step, we applied a low-pass filter and designed three intermediate filters for different frequency bands, employing multi-layer neural networks. In the second step, we used a wavelet transform method to process the data. The characteristics of the local brain are the distribution of epileptic EEG activity in the wavelet model across the whole brain surface. We also evaluated the use of two different classifiers, an artificial neural network (ANN) and a support vector machine (SVM), for the diagnosis of epilepsy. These classifiers were trained on normal and epileptic data and were able to accurately distinguish between normal and epilepsy as well as other conditions. We also found that the use of the wavelet transform did not significantly affect the classification performance but using a multi-layer neural network provided better precision. In this study, we developed a two-step automated process; incorporating low-pass filters, intermediate filters, multi-layer neural networks, and wavelet transform led to an accurate and efficient diagnosis of epilepsy. The results of this paper show high accuracy rates for both the artificial neural network (92.38%) and the support vector machine (95.5%) classifiers. Moreover, the study highlighted the effectiveness of utilizing a multi-layer neural network for improved precision. These findings contribute to the ongoing efforts in developing automated methods for epilepsy diagnosis, offering the potential for faster and more reliable detection techniques that can enhance patient care and outcomes. Full article
(This article belongs to the Special Issue Electroencephalography (EEG) in Assessment of Engagement and Workload)
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