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Embodied Minds: From Cognition to Artificial Intelligence

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

Deadline for manuscript submissions: closed (31 October 2021) | Viewed by 25021

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Special Issue Editors

Dr. Gianluca Borghini

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Guest Editor

Department of Molecular Medicine, Sapienza University of Rome, 00185 Rome, Italy
Interests: cognitive neuroscience; machine learning; neuroscience; signal processing
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Klaus Gramann

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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

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Guest Editor

Swartz Center for Computational Neuroscience, UCSD
Interests: real-world neuroimaging; real-time EEG analysis and modeling

Prof. Dr. Michelle Liou

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Guest Editor

Institute of Statistical Science, Academia Sinica
Interests: signal processing; functional neuroimaging; information theory; statistics

Dr. Hong-Hsiang Liu

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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

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Keywords

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

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Published Papers (6 papers)

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Research

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13 pages, 2515 KiB

Open AccessArticle

ECG Approximate Entropy in the Elderly during Cycling Exercise

by Jiun-Wei Liou, Po-Shan Wang, Yu-Te Wu, Sheng-Kai Lee, Shen-Da Chang and Michelle Liou

Sensors 2022, 22(14), 5255; https://doi.org/10.3390/s22145255 - 14 Jul 2022

Cited by 1 | Viewed by 2096

Abstract

Approximate entropy (ApEn) is used as a nonlinear measure of heart-rate variability (HRV) in the analysis of ECG time-series recordings. Previous studies have reported that HRV can differentiate between frail and pre-frail people. In this study, EEGs and ECGs were recorded from 38 elderly adults while performing a three-stage cycling routine. Before and after cycling stages, 5-min resting-state EEGs (rs-EEGs) and ECGs were also recorded under the eyes-open condition. Applying the K-mean classifier to pre-exercise rs-ECG ApEn values and body weights revealed nine females with EEG power which was far higher than that of the other subjects in all cycling stages. The breathing of those females was more rapid than that of other subjects and their average heart rate was faster. Those females also presented higher degrees of asymmetry in the alpha and theta bands (stronger power levels in the right frontal electrode), indicating stressful responses during the experiment. It appears that EEG delta activity could be used in conjunction with a very low ECG frequency power as a predictor of bursts in the heart rate to facilitate the monitoring of elderly adults at risk of heart failure. A resting ECG ApEn index in conjunction with the subject’s weight or BMI is recommended for screening high-risk candidates prior to exercise interventions. Full article

(This article belongs to the Special Issue Embodied Minds: From Cognition to Artificial Intelligence)

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15 pages, 6138 KiB

Open AccessArticle

Neural Dynamics of Target Detection via Wireless EEG in Embodied Cognition

by Congying He, Rupesh Kumar Chikara, Chia-Lung Yeh and Li-Wei Ko

Sensors 2021, 21(15), 5213; https://doi.org/10.3390/s21155213 - 31 Jul 2021

Cited by 15 | Viewed by 4499

Abstract

Embodied cognitive attention detection is important for many real-world applications, such as monitoring attention in daily driving and studying. Exploring how the brain and behavior are influenced by visual sensory inputs becomes a major challenge in the real world. The neural activity of embodied mind cognitive states can be understood through simple symbol experimental design. However, searching for a particular target in the real world is more complicated than during a simple symbol experiment in the laboratory setting. Hence, the development of realistic situations for investigating the neural dynamics of subjects during real-world environments is critical. This study designed a novel military-inspired target detection task for investigating the neural activities of performing embodied cognition tasks in the real-world setting. We adopted independent component analysis (ICA) and electroencephalogram (EEG) dipole source localization methods to study the participant’s event-related potentials (ERPs), event-related spectral perturbation (ERSP), and power spectral density (PSD) during the target detection task using a wireless EEG system, which is more convenient for real-life use. Behavioral results showed that the response time in the congruent condition (582 ms) was shorter than those in the incongruent (666 ms) and nontarget (863 ms) conditions. Regarding the EEG observation, we observed N200-P300 wave activation in the middle occipital lobe and P300-N500 wave activation in the right frontal lobe and left motor cortex, which are associated with attention ERPs. Furthermore, delta (1–4 Hz) and theta (4–7 Hz) band powers in the right frontal lobe, as well as alpha (8–12 Hz) and beta (13–30 Hz) band powers in the left motor cortex were suppressed, whereas the theta (4–7 Hz) band powers in the middle occipital lobe were increased considerably in the attention task. Experimental results showed that the embodied body function influences human mental states and psychological performance under cognition attention tasks. These neural markers will be also feasible to implement in the real-time brain computer interface. Novel findings in this study can be helpful for humans to further understand the interaction between the brain and behavior in multiple target detection conditions in real life. Full article

(This article belongs to the Special Issue Embodied Minds: From Cognition to Artificial Intelligence)

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22 pages, 532 KiB

Open AccessArticle

A Study on Seizure Detection of EEG Signals Represented in 2D

by Zhiwen Xiong, Huibin Wang, Lili Zhang, Tanghuai Fan, Jie Shen, Yue Zhao, Yang Liu and Qi Wu

Sensors 2021, 21(15), 5145; https://doi.org/10.3390/s21155145 - 29 Jul 2021

Cited by 14 | Viewed by 4129

Abstract

A seizure is a neurological disorder caused by abnormal neuronal discharges in the brain, which severely reduces the quality of life of patients and often endangers their lives. Automatic seizure detection is an important research area in the treatment of seizure and is a prerequisite for seizure intervention. Deep learning has been widely used for automatic detection of seizures, and many related research works decomposed the electroencephalogram (EEG) raw signal with a time window to obtain EEG signal slices, then performed feature extraction on the slices, and represented the obtained features as input data for neural networks. There are various methods for EEG signal decomposition, feature extraction, and representation, and most of the studies have been based on fixed hardware resources for the design of the scheme, which reduces the adaptability of the scheme in different application scenarios and makes it difficult to optimize the algorithms in the scheme. To address the above issues, this paper proposes a deep learning-based model for seizure detection, mainly characterized by the two-dimensional representation of EEG features and the scalability of neural networks. The model modularizes the main steps of seizure detection and improves the adaptability of the model to different hardware resource constraints, in order to increase the convenience of the algorithm optimization or the replacement of each module. The proposed model consists of five parts, and the model was tested using two epilepsy datasets separately. The experimental results showed that the proposed model has strong generality and good classification accuracy for seizure detection. Full article

(This article belongs to the Special Issue Embodied Minds: From Cognition to Artificial Intelligence)

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20 pages, 2591 KiB

Open AccessArticle

An EEG-Based Transfer Learning Method for Cross-Subject Fatigue Mental State Prediction

by Hong Zeng, Xiufeng Li, Gianluca Borghini, Yue Zhao, Pietro Aricò, Gianluca Di Flumeri, Nicolina Sciaraffa, Wael Zakaria, Wanzeng Kong and Fabio Babiloni

Sensors 2021, 21(7), 2369; https://doi.org/10.3390/s21072369 - 29 Mar 2021

Cited by 42 | Viewed by 5081

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 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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Review

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18 pages, 1590 KiB

Open AccessReview

Multisensory and Sensorimotor Integration in the Embodied Self: Relationship between Self-Body Recognition and the Mirror Neuron System

by Sotaro Shimada

Sensors 2022, 22(13), 5059; https://doi.org/10.3390/s22135059 - 5 Jul 2022

Cited by 6 | Viewed by 4025

Abstract

The embodied self is rooted in the self-body in the “here and now”. The senses of self-ownership and self-agency have been proposed as the basis of the sense of embodied self, and many experimental studies have been conducted on this subject. This review summarizes the experimental research on the embodied self that has been conducted over the past 20 years, mainly from the perspective of multisensory integration and sensorimotor integration regarding the self-body. Furthermore, the phenomenon of back projection, in which changes in an external object (e.g., a rubber hand) with which one has a sense of ownership have an inverse influence on the sensation and movement of one’s own body, is discussed. This postulates that the self-body illusion is not merely an illusion caused by multisensory and/or sensorimotor integration, but is the incorporation of an external object into the self-body representation in the brain. As an extension of this fact, we will also review research on the mirror neuron system, which is considered to be the neural basis of recognition of others, and discuss how the neural basis of self-body recognition and the mirror neuron system can be regarded as essentially the same. Full article

(This article belongs to the Special Issue Embodied Minds: From Cognition to Artificial Intelligence)

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Other

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17 pages, 459 KiB

Open AccessSystematic Review

A Systematic Review for Cognitive State-Based QoE/UX Evaluation

by Edgar Bañuelos-Lozoya, Gabriel González-Serna, Nimrod González-Franco, Olivia Fragoso-Diaz and Noé Castro-Sánchez

Sensors 2021, 21(10), 3439; https://doi.org/10.3390/s21103439 - 14 May 2021

Cited by 6 | Viewed by 3476

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 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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