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

Deadline for manuscript submissions: closed (30 July 2023) | Viewed by 40536

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Dr. Andrea Bizzego

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Department of Psychology and Cognitive Science, University of Trento, 38068 Trento, Italy
Interests: physiological signal processing; statistical neuroimaging; artificial intelligence; reproducibility
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Prof. Dr. Annabel Chen

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1. Director of the Centre for Research & Development in Learning (CRADLE@NTU), National Institute of Education, Nanyang Technological University, Singapore
2. Director (NTU) of the Centre for Lifelong Learning and Individualised Cognition (CLIC) in Collaboration with Cambridge University (an NRF-CREATE Programme) LKCMedicine, National Institute of Education, Nanyang Technological University, Singapore
Interests: fMRI; cognitive neuroscience
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Prof. Dr. Gianluca Esposito

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Department of Psychology and Cognitive Sciences, University of Trento, 38100 Trento, Italy
Interests: social neuroscience; clinical psychology
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Special Issue Information

Dear Colleagues,

In the past few decades, the emergence of remarkable novel sensing technologies and methodological approaches has contributed to advancing research on the human brain.

One side, a new generation of sensors, facilitated the collection of central and peripheral nervous system signals, allowing studies within new contexts and experimental settings (e.g.., hyperscanning, real-life).

On the other side, novel analysis techniques (e.g., data fusion, artificial intelligence) have enabled a more efficient and robust extraction of brain activity indicators.

This Special Issue aims to report high-quality theoretical, analytical, and experimental investigations, including proof-of-concept, modeling, and practical-oriented studies related to new hardware and software applications aimed at monitoring and measuring brain activity.

This Special Issue will accept high-quality papers containing original research results and survey articles of excellent merit in (but not limited to) the following fields:

Functional magnetic resonance imaging (fMRI);
Functional near infrared spectroscopy (fNIRS);
Magnetic resonance spectroscopy (MRS);
Encephalography (EEG);
Real-life brain monitoring;
Signal processing and validation.

Dr. Andrea Bizzego
Prof. Dr. Annabel Chen
Dr. Gianluca Esposito
Guest Editors

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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 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

functional magnetic resonance imaging (fMRI)
functional near infrared spectroscopy (fNIRS)
magnetic resonance spectroscopy (MRS)
encephalography (EEG)
real-life brain monitoring
signal processing and validation

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Brain Activity Monitoring and Measurement (2nd Edition) in Sensors

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Research

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18 pages, 2385 KiB

Open AccessArticle

A Supervised Machine Learning Approach to Classify Brain Morphology of Professional Visual Artists versus Non-Artists

by Alessandro Grecucci, Clara Rastelli, Francesca Bacci, David Melcher and Nicola De Pisapia

Sensors 2023, 23(9), 4199; https://doi.org/10.3390/s23094199 - 22 Apr 2023

Cited by 1 | Viewed by 2841

Abstract

This study aimed to investigate whether there are structural differences in the brains of professional artists who received formal training in the visual arts and non-artists who did not have any formal training or professional experience in the visual arts, and whether these differences can be used to accurately classify individuals as being an artist or not. Previous research using functional MRI has suggested that general creativity involves a balance between the default mode network and the executive control network. However, it is not known whether there are structural differences between the brains of artists and non-artists. In this study, a machine learning method called Multi-Kernel Learning (MKL) was applied to gray matter images of 12 artists and 12 non-artists matched for age and gender. The results showed that the predictive model was able to correctly classify artists from non-artists with an accuracy of 79.17% (AUC 88%), and had the ability to predict new cases with an accuracy of 81.82%. The brain regions most important for this classification were the Heschl area, amygdala, cingulate, thalamus, and parts of the parietal and occipital lobes as well as the temporal pole. These regions may be related to the enhanced emotional and visuospatial abilities that professional artists possess compared to non-artists. Additionally, the reliability of this circuit was assessed using two different classifiers, which confirmed the findings. There was also a trend towards significance between the circuit and a measure of vividness of imagery, further supporting the idea that these brain regions may be related to the imagery abilities involved in the artistic process. Full article

(This article belongs to the Special Issue Brain Activity Monitoring and Measurement)

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18 pages, 9478 KiB

Open AccessArticle

by Makoto Miyakoshi, Josephine Astrid Archer, Chiao-Yi Wu, Toshiharu Nakai and Shen-Hsing Annabel Chen

Sensors 2023, 23(8), 4107; https://doi.org/10.3390/s23084107 - 19 Apr 2023

Cited by 1 | Viewed by 1575

Abstract

The posterior-to-anterior shift in aging (PASA) effect is seen as a compensatory model that enables older adults to meet increased cognitive demands to perform comparably as their young counterparts. However, empirical support for the PASA effect investigating age-related changes in the inferior frontal gyrus (IFG), hippocampus, and parahippocampus has yet to be established. 33 older adults and 48 young adults were administered tasks sensitive to novelty and relational processing of indoor/outdoor scenes in a 3-Tesla MRI scanner. Functional activation and connectivity analyses were applied to examine the age-related changes on the IFG, hippocampus, and parahippocampus among low/high-performing older adults and young adults. Significant parahippocampal activation was generally found in both older (high-performing) and young adults for novelty and relational processing of scenes. Younger adults had significantly greater IFG and parahippocampal activation than older adults, and greater parahippocampal activation compared to low-performing older adults for relational processing—providing partial support for the PASA model. Observations of significant functional connectivity within the medial temporal lobe and greater negative left IFG-right hippocampus/parahippocampus functional connectivity for young compared to low-performing older adults for relational processing also supports the PASA effect partially. Full article

(This article belongs to the Special Issue Brain Activity Monitoring and Measurement)

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15 pages, 543 KiB

Open AccessArticle

Exposure to Multicultural Context Affects Neural Response to Out-Group Faces: A Functional Magnetic Resonance Imaging Study

by Alessandro Carollo, Paola Rigo, Andrea Bizzego, Albert Lee, Peipei Setoh and Gianluca Esposito

Sensors 2023, 23(8), 4030; https://doi.org/10.3390/s23084030 - 16 Apr 2023

Cited by 1 | Viewed by 2284

Abstract

Recent migration and globalization trends have led to the emergence of ethnically, religiously, and linguistically diverse countries. Understanding the unfolding of social dynamics in multicultural contexts becomes a matter of common interest to promote national harmony and social cohesion among groups. The current functional magnetic resonance imaging (fMRI) study aimed to (i) explore the neural signature of the in-group bias in the multicultural context; and (ii) assess the relationship between the brain activity and people’s system-justifying ideologies. A sample of 43 (22 females) Chinese Singaporeans (M = 23.36; SD = 1.41) was recruited. All participants completed the Right Wing Authoritarianism Scale and Social Dominance Orientation Scale to assess their system-justifying ideologies. Subsequently, four types of visual stimuli were presented in an fMRI task: Chinese (in-group), Indian (typical out-group), Arabic (non-typical out-group), and Caucasian (non-typical out-group) faces. The right middle occipital gyrus and the right postcentral gyrus showed enhanced activity when participants were exposed to in-group (Chinese) rather than out-group (Arabic, Indian, and Caucasian) faces. Regions having a role in mentalization, empathetic resonance, and social cognition showed enhanced activity to Chinese (in-group) rather than Indian (typical out-group) faces. Similarly, regions typically involved in socioemotional and reward-related processing showed increased activation when participants were shown Chinese (in-group) rather than Arabic (non-typical out-group) faces. The neural activations in the right postcentral gyrus for in-group rather than out-group faces and in the right caudate in response to Chinese rather than Arabic faces were in a significant positive correlation with participants’ Right Wing Authoritarianism scores (p < 0.05). Furthermore, the activity in the right middle occipital gyrus for Chinese rather than out-group faces was in a significant negative correlation with participants’ Social Dominance Orientation scores (p < 0.05). Results are discussed by considering the typical role played by the activated brain regions in socioemotional processes as well as the role of familiarity to out-group faces. Full article

(This article belongs to the Special Issue Brain Activity Monitoring and Measurement)

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21 pages, 9340 KiB

Open AccessArticle

An Integrated Machine Learning-Based Brain Computer Interface to Classify Diverse Limb Motor Tasks: Explainable Model

by Hend A. Hashem, Yousry Abdulazeem, Labib M. Labib, Mostafa A. Elhosseini and Mohamed Shehata

Sensors 2023, 23(6), 3171; https://doi.org/10.3390/s23063171 - 16 Mar 2023

Cited by 2 | Viewed by 3002

Abstract

Terminal neurological conditions can affect millions of people worldwide and hinder them from doing their daily tasks and movements normally. Brain computer interface (BCI) is the best hope for many individuals with motor deficiencies. It will help many patients interact with the outside world and handle their daily tasks without assistance. Therefore, machine learning-based BCI systems have emerged as non-invasive techniques for reading out signals from the brain and interpreting them into commands to help those people to perform diverse limb motor tasks. This paper proposes an innovative and improved machine learning-based BCI system that analyzes EEG signals obtained from motor imagery to distinguish among various limb motor tasks based on BCI competition III dataset IVa. The proposed framework pipeline for EEG signal processing performs the following major steps. The first step uses a meta-heuristic optimization technique, called the whale optimization algorithm (WOA), to select the optimal features for discriminating between neural activity patterns. The pipeline then uses machine learning models such as LDA, k-NN, DT, RF, and LR to analyze the chosen features to enhance the precision of EEG signal analysis. The proposed BCI system, which merges the WOA as a feature selection method and the optimized k-NN classification model, demonstrated an overall accuracy of 98.6%, outperforming other machine learning models and previous techniques on the BCI competition III dataset IVa. Additionally, the EEG feature contribution in the ML classification model is reported using Explainable AI (XAI) tools, which provide insights into the individual contributions of the features in the predictions made by the model. By incorporating XAI techniques, the results of this study offer greater transparency and understanding of the relationship between the EEG features and the model’s predictions. The proposed method shows potential levels for better use in controlling diverse limb motor tasks to help people with limb impairments and support them while enhancing their quality of life. Full article

(This article belongs to the Special Issue Brain Activity Monitoring and Measurement)

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21 pages, 5853 KiB

Open AccessArticle

Abnormal Brain Circuits Characterize Borderline Personality and Mediate the Relationship between Childhood Traumas and Symptoms: A mCCA+jICA and Random Forest Approach

by Alessandro Grecucci, Harold Dadomo, Gerardo Salvato, Gaia Lapomarda, Sara Sorella and Irene Messina

Sensors 2023, 23(5), 2862; https://doi.org/10.3390/s23052862 - 6 Mar 2023

Cited by 8 | Viewed by 3611

Abstract

Borderline personality disorder (BPD) is a severe personality disorder whose neural bases are still unclear. Indeed, previous studies reported inconsistent findings concerning alterations in cortical and subcortical areas. In the present study, we applied for the first time a combination of an unsupervised machine learning approach known as multimodal canonical correlation analysis plus joint independent component analysis (mCCA+jICA), in combination with a supervised machine learning approach known as random forest, to possibly find covarying gray matter and white matter (GM-WM) circuits that separate BPD from controls and that are also predictive of this diagnosis. The first analysis was used to decompose the brain into independent circuits of covarying grey and white matter concentrations. The second method was used to develop a predictive model able to correctly classify new unobserved BPD cases based on one or more circuits derived from the first analysis. To this aim, we analyzed the structural images of patients with BPD and matched healthy controls (HCs). The results showed that two GM-WM covarying circuits, including basal ganglia, amygdala, and portions of the temporal lobes and of the orbitofrontal cortex, correctly classified BPD against HC. Notably, these circuits are affected by specific child traumatic experiences (emotional and physical neglect, and physical abuse) and predict symptoms severity in the interpersonal and impulsivity domains. These results support that BPD is characterized by anomalies in both GM and WM circuits related to early traumatic experiences and specific symptoms. Full article

(This article belongs to the Special Issue Brain Activity Monitoring and Measurement)

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16 pages, 2706 KiB

Open AccessArticle

Classification of Motor Imagery EEG Signals Based on Data Augmentation and Convolutional Neural Networks

by Yu Xie and Stefan Oniga

Sensors 2023, 23(4), 1932; https://doi.org/10.3390/s23041932 - 9 Feb 2023

Cited by 14 | Viewed by 4683

Abstract

In brain–computer interface (BCI) systems, motor imagery electroencephalography (MI-EEG) signals are commonly used to detect participant intent. Many factors, including low signal-to-noise ratios and few high-quality samples, make MI classification difficult. In order for BCI systems to function, MI-EEG signals must be studied. In pattern recognition and other fields, deep learning approaches have recently been successfully applied. In contrast, few effective deep learning algorithms have been applied to BCI systems, especially MI-based systems. In this paper, we address these problems from two aspects based on the characteristics of EEG signals: first, we proposed a combined time–frequency domain data enhancement method. This method guarantees that the size of the training data is effectively increased while maintaining the intrinsic composition of the data. Second, our design consists of a parallel CNN that takes both raw EEG images and images transformed through continuous wavelet transform (CWT) as inputs. We conducted classification experiments on a public data set to verify the effectiveness of the algorithm. According to experimental results based on the BCI Competition IV Dataset2a, the average classification accuracy is 97.61%. A comparison of the proposed algorithm with other algorithms shows that it performs better in classification. The algorithm can be used to improve the classification performance of MI-based BCIs and BCI systems created for people with disabilities. Full article

(This article belongs to the Special Issue Brain Activity Monitoring and Measurement)

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20 pages, 1631 KiB

Open AccessArticle

Anxious Brains: A Combined Data Fusion Machine Learning Approach to Predict Trait Anxiety from Morphometric Features

by Teresa Baggio, Alessandro Grecucci, Federica Meconi and Irene Messina

Sensors 2023, 23(2), 610; https://doi.org/10.3390/s23020610 - 5 Jan 2023

Cited by 12 | Viewed by 3919

Abstract

Trait anxiety relates to the steady propensity to experience and report negative emotions and thoughts such as fear and worries across different situations, along with a stable perception of the environment as characterized by threatening stimuli. Previous studies have tried to investigate neuroanatomical features related to anxiety mostly using univariate analyses and thus giving rise to contrasting results. The aim of this study is to build a predictive model of individual differences in trait anxiety from brain morphometric features, by taking advantage of a combined data fusion machine learning approach to allow generalization to new cases. Additionally, we aimed to perform a network analysis to test the hypothesis that anxiety-related networks have a central role in modulating other networks not strictly associated with anxiety. Finally, we wanted to test the hypothesis that trait anxiety was associated with specific cognitive emotion regulation strategies, and whether anxiety may decrease with ageing. Structural brain images of 158 participants were first decomposed into independent covarying gray and white matter networks with a data fusion unsupervised machine learning approach (Parallel ICA). Then, supervised machine learning (decision tree) and backward regression were used to extract and test the generalizability of a predictive model of trait anxiety. Two covarying gray and white matter independent networks successfully predicted trait anxiety. The first network included mainly parietal and temporal regions such as the postcentral gyrus, the precuneus, and the middle and superior temporal gyrus, while the second network included frontal and parietal regions such as the superior and middle temporal gyrus, the anterior cingulate, and the precuneus. We also found that trait anxiety was positively associated with catastrophizing, rumination, other- and self-blame, and negatively associated with positive refocusing and reappraisal. Moreover, trait anxiety was negatively associated with age. This paper provides new insights regarding the prediction of individual differences in trait anxiety from brain and psychological features and can pave the way for future diagnostic predictive models of anxiety. Full article

(This article belongs to the Special Issue Brain Activity Monitoring and Measurement)

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13 pages, 1207 KiB

Open AccessArticle

Estimation of ANT-DBS Electrodes on Target Positioning Based on a New Percept^TM PC LFP Signal Analysis

by Elodie Múrias Lopes, Ricardo Rego, Manuel Rito, Clara Chamadoira, Duarte Dias and João Paulo Silva Cunha

Sensors 2022, 22(17), 6601; https://doi.org/10.3390/s22176601 - 1 Sep 2022

Cited by 6 | Viewed by 2801

Abstract

Deep brain stimulation of the Anterior Nucleus of the Thalamus (ANT-DBS) is an effective therapy in epilepsy. Poorer surgical outcomes are related to deviations of the lead from the ANT-target. The target identification relies on the visualization of anatomical structures by medical imaging, which presents some disadvantages. This study aims to research whether ANT-LFPs recorded with the Percept^TM PC neurostimulator can be an asset in the identification of the DBS-target. For this purpose, 17 features were extracted from LFPs recorded from a single patient, who stayed at an Epilepsy Monitoring Unit for a 5-day period. Features were then integrated into two machine learning (ML)-based methodologies, according to different LFP bipolar montages: Pass1 (nonadjacent channels) and Pass2 (adjacent channels). We obtained an accuracy of 76.6% for the Pass1-classifier and 83.33% for the Pass2-classifier in distinguishing locations completely inserted in the target and completely outside. Then, both classifiers were used to predict the target percentage of all combinations, and we found that contacts 3 (left hemisphere) and 2 and 3 (right hemisphere) presented higher signatures of the ANT-target, which agreed with the medical images. This result opens a new window of opportunity for the use of LFPs in the guidance of DBS target identification. Full article

(This article belongs to the Special Issue Brain Activity Monitoring and Measurement)

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21 pages, 3096 KiB

Open AccessArticle

Signal Quality Investigation of a New Wearable Frontal Lobe EEG Device

by Zhilin Gao, Xingran Cui, Wang Wan, Zeguang Qin and Zhongze Gu

Sensors 2022, 22(5), 1898; https://doi.org/10.3390/s22051898 - 28 Feb 2022

Cited by 12 | Viewed by 3791

Abstract

The demand for non-laboratory and long-term EEG acquisition in scientific and clinical applications has put forward new requirements for wearable EEG devices. In this paper, a new wearable frontal EEG device called Mindeep was proposed. A signal quality study was then conducted, which included simulated signal tests and signal quality comparison experiments. Simulated signals with different frequencies and amplitudes were used to test the stability of Mindeep’s circuit, and the high correlation coefficients (>0.9) proved that Mindeep has a stable and reliable hardware circuit. The signal quality comparison experiment, between Mindeep and the gold standard device, Neuroscan, included three tasks: (1) resting; (2) auditory oddball; and (3) attention. In the resting state, the average normalized cross-correlation coefficients between EEG signals recorded by the two devices was around 0.72 ± 0.02, Berger effect was observed (p < 0.01), and the comparison results in the time and frequency domain illustrated the ability of Mindeep to record high-quality EEG signals. The significant differences between high tone and low tone in auditory event-related potential collected by Mindeep was observed in N2 and P2. The attention recognition accuracy of Mindeep achieved 71.12% and 74.76% based on EEG features and the XGBoost model in the two attention tasks, respectively, which were higher than that of Neuroscan (70.19% and 72.80%). The results validated the performance of Mindeep as a prefrontal EEG recording device, which has a wide range of potential applications in audiology, cognitive neuroscience, and daily requirements. Full article

(This article belongs to the Special Issue Brain Activity Monitoring and Measurement)

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Review

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15 pages, 431 KiB

Open AccessReview

The Role of Artificial Intelligence in Decoding Speech from EEG Signals: A Scoping Review

by Uzair Shah, Mahmood Alzubaidi, Farida Mohsen, Alaa Abd-Alrazaq, Tanvir Alam and Mowafa Househ

Sensors 2022, 22(18), 6975; https://doi.org/10.3390/s22186975 - 15 Sep 2022

Cited by 11 | Viewed by 5223

Abstract

Background: Brain traumas, mental disorders, and vocal abuse can result in permanent or temporary speech impairment, significantly impairing one’s quality of life and occasionally resulting in social isolation. Brain–computer interfaces (BCI) can support people who have issues with their speech or who have been paralyzed to communicate with their surroundings via brain signals. Therefore, EEG signal-based BCI has received significant attention in the last two decades for multiple reasons: (i) clinical research has capitulated detailed knowledge of EEG signals, (ii) inexpensive EEG devices, and (iii) its application in medical and social fields. Objective: This study explores the existing literature and summarizes EEG data acquisition, feature extraction, and artificial intelligence (AI) techniques for decoding speech from brain signals. Method: We followed the PRISMA-ScR guidelines to conduct this scoping review. We searched six electronic databases: PubMed, IEEE Xplore, the ACM Digital Library, Scopus, arXiv, and Google Scholar. We carefully selected search terms based on target intervention (i.e., imagined speech and AI) and target data (EEG signals), and some of the search terms were derived from previous reviews. The study selection process was carried out in three phases: study identification, study selection, and data extraction. Two reviewers independently carried out study selection and data extraction. A narrative approach was adopted to synthesize the extracted data. Results: A total of 263 studies were evaluated; however, 34 met the eligibility criteria for inclusion in this review. We found 64-electrode EEG signal devices to be the most widely used in the included studies. The most common signal normalization and feature extractions in the included studies were the bandpass filter and wavelet-based feature extraction. We categorized the studies based on AI techniques, such as machine learning and deep learning. The most prominent ML algorithm was a support vector machine, and the DL algorithm was a convolutional neural network. Conclusions: EEG signal-based BCI is a viable technology that can enable people with severe or temporal voice impairment to communicate to the world directly from their brain. However, the development of BCI technology is still in its infancy. Full article

(This article belongs to the Special Issue Brain Activity Monitoring and Measurement)

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36 pages, 2665 KiB

Open AccessReview

A Comprehensive Review of Endogenous EEG-Based BCIs for Dynamic Device Control

by Natasha Padfield, Kenneth Camilleri, Tracey Camilleri, Simon Fabri and Marvin Bugeja

Sensors 2022, 22(15), 5802; https://doi.org/10.3390/s22155802 - 3 Aug 2022

Cited by 26 | Viewed by 4938

Abstract

Electroencephalogram (EEG)-based brain–computer interfaces (BCIs) provide a novel approach for controlling external devices. BCI technologies can be important enabling technologies for people with severe mobility impairment. Endogenous paradigms, which depend on user-generated commands and do not need external stimuli, can provide intuitive control of external devices. This paper discusses BCIs to control various physical devices such as exoskeletons, wheelchairs, mobile robots, and robotic arms. These technologies must be able to navigate complex environments or execute fine motor movements. Brain control of these devices presents an intricate research problem that merges signal processing and classification techniques with control theory. In particular, obtaining strong classification performance for endogenous BCIs is challenging, and EEG decoder output signals can be unstable. These issues present myriad research questions that are discussed in this review paper. This review covers papers published until the end of 2021 that presented BCI-controlled dynamic devices. It discusses the devices controlled, EEG paradigms, shared control, stabilization of the EEG signal, traditional machine learning and deep learning techniques, and user experience. The paper concludes with a discussion of open questions and avenues for future work. Full article

(This article belongs to the Special Issue Brain Activity Monitoring and Measurement)

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