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Entropy
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Application of Entropy Analysis to Electroencephalographic Data
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Application of Entropy Analysis to Electroencephalographic Data

A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Entropy and Biology".

Deadline for manuscript submissions: closed (20 April 2023) | Viewed by 3627

Special Issue Editor

Prof. Dr. Fabrizio Vecchio

E-Mail Website
Guest Editor
1. Department of Theoretical and Applied Sciences, eCampus University, 22060 Como, Italy
2. Brain Connectivity Laboratory, Department of Neuroscience and Neurorehabilitation, IRCCS San Raffaele Roma, 00163 Rome, Italy
Interests: EEG; neuroscience; functional coupling; brain complexity; graph theory; entropy; cognition; dementia
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Human brain is an extremely complex network of interconnected neurons, and its functioning is essentially based on nonlinearities. Electroencephalography (EEG) has become a widely used technique to understand the chaotic behavior underlying brain dynamical properties. EEG complexity has been evaluated by means of several computational approaches throughout the years; one such approach is the entropy. Entropy is a concept addressing randomness and predictability, with greater entropy often associated with more randomness.

Brain complexity can change during both physiological and pathological ageing, but also during cognitive or motor tasks. Entropy can be used to monitor the brain complexity in several conditions thanks to simple EEG recordings. Contributions addressing any of these issues are welcome.

This Special Issue aims to be a forum for the presentation of new and improved techniques of information theory for complex systems. In particular, the analysis and interpretation of brain complex systems with the help of statistical tools based on information theory and complexity fall within the scope of this Special Issue. This Special Issue will collect new ideas and describe promising methods arising from the field of analysis and modeling of complex nonlinear dynamical systems.

Prof. Dr. Fabrizio Vecchio
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Entropy is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • EEG
  • neuroscience
  • resting state
  • cognitive/motor task
  • brain complexity
  • entropy
  • cognition
  • dementia

Published Papers (2 papers)

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Research

16 pages, 4608 KiB  
Article
A Multi-Scale Temporal Convolutional Network with Attention Mechanism for Force Level Classification during Motor Imagery of Unilateral Upper-Limb Movements
by Junpeng Sheng, Jialin Xu, Han Li, Zhen Liu, Huilin Zhou, Yimeng You, Tao Song and Guokun Zuo
Entropy 2023, 25(3), 464; https://doi.org/10.3390/e25030464 - 7 Mar 2023
Cited by 1 | Viewed by 1509
Abstract
In motor imagery (MI) brain–computer interface (BCI) research, some researchers have designed MI paradigms of force under a unilateral upper-limb static state. It is difficult to apply these paradigms to the dynamic force interaction process between the robot and the patient in a [...] Read more.
In motor imagery (MI) brain–computer interface (BCI) research, some researchers have designed MI paradigms of force under a unilateral upper-limb static state. It is difficult to apply these paradigms to the dynamic force interaction process between the robot and the patient in a brain-controlled rehabilitation robot system, which needs to induce thinking states of the patient’s demand for assistance. Therefore, in our research, according to the movement of wiping the table in human daily life, we designed a three-level-force MI paradigm under a unilateral upper-limb dynamic state. Based on the event-related de-synchronization (ERD) feature analysis of the electroencephalography (EEG) signals generated by the brain’s force change motor imagination, we proposed a multi-scale temporal convolutional network with attention mechanism (MSTCN-AM) algorithm to recognize ERD features of MI-EEG signals. Aiming at the slight feature differences of single-trial MI-EEG signals among different levels of force, the MSTCN module was designed to extract fine-grained features of different dimensions in the time–frequency domain. The spatial convolution module was then used to learn the area differences of space domain features. Finally, the attention mechanism dynamically weighted the time–frequency–space domain features to improve the algorithm’s sensitivity. The results showed that the accuracy of the algorithm was 86.4 ± 14.0% for the three-level-force MI-EEG data collected experimentally. Compared with the baseline algorithms (OVR-CSP+SVM (77.6 ± 14.5%), Deep ConvNet (75.3 ± 12.3%), Shallow ConvNet (77.6 ± 11.8%), EEGNet (82.3 ± 13.8%), and SCNN-BiLSTM (69.1 ± 16.8%)), our algorithm had higher classification accuracy with significant differences and better fitting performance. Full article
(This article belongs to the Special Issue Application of Entropy Analysis to Electroencephalographic Data)
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19 pages, 5363 KiB  
Article
Driving Fatigue Detection with Three Non-Hair-Bearing EEG Channels and Modified Transformer Model
by Jie Wang, Yanting Xu, Jinghong Tian, Huayun Li, Weidong Jiao, Yu Sun and Gang Li
Entropy 2022, 24(12), 1715; https://doi.org/10.3390/e24121715 - 24 Nov 2022
Cited by 7 | Viewed by 1725
Abstract
Driving fatigue is the main cause of traffic accidents, which seriously affects people’s life and property safety. Many researchers have applied electroencephalogram (EEG) signals for driving fatigue detection to reduce negative effects. The main challenges are the practicality and accuracy of the EEG-based [...] Read more.
Driving fatigue is the main cause of traffic accidents, which seriously affects people’s life and property safety. Many researchers have applied electroencephalogram (EEG) signals for driving fatigue detection to reduce negative effects. The main challenges are the practicality and accuracy of the EEG-based driving fatigue detection method when it is applied on the real road. In our previous study, we attempted to improve the practicality of fatigue detection based on the proposed non-hair-bearing (NHB) montage with fewer EEG channels, but the recognition accuracy was only 76.47% with the random forest (RF) model. In order to improve the accuracy with NHB montage, this study proposed an improved transformer architecture for one-dimensional feature vector classification based on introducing the Gated Linear Unit (GLU) in the Attention sub-block and Feed-Forward Networks (FFN) sub-block of a transformer, called GLU-Oneformer. Moreover, we constructed an NHB-EEG-based feature set, including the same EEG features (power ratio, approximate entropy, and mutual information (MI)) in our previous study, and the lateralization features of the power ratio and approximate entropy based on the strategy of brain lateralization. The results indicated that our GLU-Oneformer method significantly improved the recognition performance and achieved an accuracy of 86.97%. Our framework demonstrated that the combination of the NHB montage and the proposed GLU-Oneformer model could well support driving fatigue detection. Full article
(This article belongs to the Special Issue Application of Entropy Analysis to Electroencephalographic Data)
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