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Special Issue "Embodied Minds: From Cognition to Artificial Intelligence"

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Biomedical Sensors".

Deadline for manuscript submissions: 30 June 2021.

Special Issue Editors

Dr. Gianluca Borghini
E-Mail Website
Guest Editor
Department of Molecular Medicine, Sapienza Università di Roma, Rome, Italy
Interests: cognitive neuroscience; machine learning; neuroscience; signal processing
Special Issues and Collections in MDPI journals
Prof. Dr. Klaus Gramann
E-Mail Website
Guest Editor
Institute of Psychology and Ergonomics, School of Mechanical Engineering and Transport Systems, Technische Universitaet Berlin
Interests: mobile brain imaging; spatial navigation; visual attention
Prof. Dr. Tzyy-Ping Jung
E-Mail Website
Guest Editor
Swartz Center for Computational Neuroscience, UCSD
Interests: real-world neuroimaging; real-time EEG analysis and modeling
Prof. Dr. Michelle Liou
E-Mail Website
Guest Editor
Institute of Statistical Science, Academia Sinica
Interests: signal processing; functional neuroimaging; information theory; statistics
Dr. Hong-Hsiang Liu
E-Mail Website
Supporting Guest Editor
Institute of Statistical Science, Academia Sinica
Interests: collective decision making; reinforcement learning; affective processing; neuropsychiatric disorders; EEG hyperscanning

Special Issue Information

Dear Colleagues,

Scientists in the field of human cognition have conventionally viewed the mind as a computer distinct from the body; however, mental processes and bodily states are actually intertwined. As asserted by Lakoff and Johnson in their seminal work Philosophy in the Flesh (1999), reason is shaped by internal sensations from our bodies and external experiences via the neural structure of our brains. There is a growing body of compelling evidence supporting the claim that the human body is indeed a component of consciousness rather than a servant of the mind. Nonetheless, a comprehensive understanding of mental functioning in real life will require a highly detailed analysis of the interactions between the mind and body. This Special Issue of Sensors focuses on the mind–body interactions elicited by exposing subjects to natural stimulation or prompting subjects to perform sensory-guided movements. Innovative work from the perspectives of cognition or methodology is acceptable. Preferably, analysis is based on signals from at least two devices, such as EEG in conjunction with ECG and respiration, or fNIRS in conjunction with GSR. Topics that are applicable to clinical, sport, education, or health settings are of particular interest.

Dr. Gianluca Borghini
Prof. Dr. Klaus Gramann
Prof. Dr. Tzyy-Ping Jung
Prof. Dr. Michelle Liou
Guest Editors

Dr. Hong-Hsiang Liu
Supporting 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 papers will be 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. Sensors is an international peer-reviewed open access semimonthly 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 2200 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

  • Embodied cognition
  • Brain-computer interface
  • Signal processing
  • Convolutional neural networks
  • Natural stimulation
  • Simultaneous recording

Published Papers (2 papers)

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Article
An EEG-Based Transfer Learning Method for Cross-Subject Fatigue Mental State Prediction
Sensors 2021, 21(7), 2369; https://doi.org/10.3390/s21072369 - 29 Mar 2021
Viewed by 557
Abstract
Fatigued driving is one of the main causes of traffic accidents. The electroencephalogram (EEG)-based mental state analysis method is an effective and objective way of detecting fatigue. However, as EEG shows significant differences across subjects, effectively “transfering” the EEG analysis model of the [...] Read more.
Fatigued driving is one of the main causes of traffic accidents. The electroencephalogram (EEG)-based mental state analysis method is an effective and objective way of detecting fatigue. However, as EEG shows significant differences across subjects, effectively “transfering” the EEG analysis model of the existing subjects to the EEG signals of other subjects is still a challenge. Domain-Adversarial Neural Network (DANN) has excellent performance in transfer learning, especially in the fields of document analysis and image recognition, but has not been applied directly in EEG-based cross-subject fatigue detection. In this paper, we present a DANN-based model, Generative-DANN (GDANN), which combines Generative Adversarial Networks (GAN) to enhance the ability by addressing the issue of different distribution of EEG across subjects. The comparative results show that in the analysis of cross-subject tasks, GDANN has a higher average accuracy of 91.63% in fatigue detection across subjects than those of traditional classification models, which is expected to have much broader application prospects in practical brain–computer interaction (BCI). Full article
(This article belongs to the Special Issue Embodied Minds: From Cognition to Artificial Intelligence)
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Systematic Review
A Systematic Review for Cognitive State-Based QoE/UX Evaluation
Sensors 2021, 21(10), 3439; https://doi.org/10.3390/s21103439 - 14 May 2021
Viewed by 481
Abstract
Traditional evaluation of user experience is subjective by nature, for what is sought is to use data from physiological and behavioral sensors to interpret the relationship that the user’s cognitive states have with the elements of a graphical interface and interaction mechanisms. This [...] Read more.
Traditional evaluation of user experience is subjective by nature, for what is sought is to use data from physiological and behavioral sensors to interpret the relationship that the user’s cognitive states have with the elements of a graphical interface and interaction mechanisms. This study presents the systematic review that was developed to determine the cognitive states that are being investigated in the context of Quality of Experience (QoE)/User Experience (UX) evaluation, as well as the signals and characteristics obtained, machine learning models used, evaluation architectures proposed, and the results achieved. Twenty-nine papers published in 2014–2019 were selected from eight online sources of information, of which 24% were related to the classification of cognitive states, 17% described evaluation architectures, and 41% presented correlations between different signals, cognitive states, and QoE/UX metrics, among others. The amount of identified studies was low in comparison with cognitive state research in other contexts, such as driving or other critical activities; however, this provides a starting point to analyze and interpret states such as mental workload, confusion, and mental stress from various human signals and propose more robust QoE/UX evaluation architectures. Full article
(This article belongs to the Special Issue Embodied Minds: From Cognition to Artificial Intelligence)
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