Anastasios Bezerianos | University of Patras (original) (raw)

Papers by Anastasios Bezerianos

Research paper thumbnail of Distinct spatio-temporal and spectral brain patterns for different thermal stimuli perception

Scientific Reports, Jan 18, 2022

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Research paper thumbnail of Chimera States in a Two-Population Network of Coupled Pendula

arXiv (Cornell University), Aug 26, 2013

More than a decade ago, a surprising coexistence of synchronized and asynchronous behavior called... more More than a decade ago, a surprising coexistence of synchronized and asynchronous behavior called the chimera state was discovered in networks of nonlocally coupled identical phase oscillators. In later years, chimeras were found to occur in a variety of theoretical and experimental studies of chemical and optical systems, as well as models of neuron dynamics. In this Letter, we study two coupled populations of pendula represented by phase oscillators with a second derivative term multiplied by a mass parameter mmm and treat the first order derivative terms as dissipation with parameter epsilon>0\epsilon>0epsilon>0. We first present numerical evidence showing that chimeras do exist in this system for small mass values 0<m<<10<m<<10<m<<1 and epsilon\epsilonepsilon greater than a threshold value epsilonthr(m)>0\epsilon_{thr}(m)>0epsilonthr(m)>0 that grows linearly with mmm. We then proceed to explain these states by reducing the coherent population to a single damped pendulum equation driven parametrically by oscillating averaged quantities related to the incoherent population.

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Research paper thumbnail of Sensory Feedback in Upper Limb Amputees Impacts Cortical Activity as Revealed by Multiscale Connectivity Analysis

2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)

Sensory feedback in upper limb amputees is crucial for improving movement decoding and also to en... more Sensory feedback in upper limb amputees is crucial for improving movement decoding and also to enhance embodiment of the prosthetic limb. Recently, an increasing number of invasive and noninvasive solutions for sensory stimulation have demonstrated the capability of providing a range of sensations to upper limb amputees. However, the cortical impact of restored sensation is not clearly understood. Particularly, understanding the cortical connectivity changes at multiple scales (nodal and modular) in response to sensory stimulation, can reveal crucial information on how amputees brain process the sensory stimuli. Using Electroencephalography (EEG) signals, we compared the cortical connectivity network in response to sensory feedback provided by targeted transcutaneous electrical nerve stimulation (tTENS) in an upper limb amputee during phantom upper limb movements. We focused our cortical connectivity analysis on four functional modules comprising of 20 brain regions that are primarily associated with a visually guided motor task (visual, motor, somatosensory and multisensory integration (MI)) used in this study. At the modular level, we observed that the hubness (a graph theoretic measure quantifying the importance of brain regions in integrating brain function) of the motor module decreases whereas that of the somatosensory module increases in presence of tTENS feedback. At the nodal level, similar observations were made for the visual and MI regions. This is the first work to reveal the impact of sensory feedback at multiple scales in the cortex of amputees in response to sensory stimulation.

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Research paper thumbnail of Olfactory-induced Positive Affect and Autonomic Response as a Function of Hedonic and Intensity Attributes of Fragrances

2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)

Olfactory perception is intrinsically tied to emotional processing, in both behavior and neurophy... more Olfactory perception is intrinsically tied to emotional processing, in both behavior and neurophysiology. Despite advances in olfactory-affective neuroscience, it is unclear how separate attributes of odor stimuli contribute to olfactoryinduced emotions, especially within the positive segment of the hedonic dimension to avoid potential cross-valence confounds. In this study, we examined how pleasantness and intensity of fragrances relate to different grades of positive affect. Our results show that greater odor pleasantness and intensity are independently associated with stronger positive affect. Pleasantness has a greater influence than intensity in evoking a positive vs. neutral affect, whereas intensity is more impactful than pleasantness in evoking an extreme positive vs. positive response. Autonomic response, as assessed by the galvanic skin response (GSR) was found to decrease with increasing pleasantness but not intensity. This clarifies how olfactory and affective processing induce significant downstream effects in peripheral physiology and self-reported affective experience, pertinent to the thriving field of olfactory neuromarkerting.

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Research paper thumbnail of Subtype Divergences of Trust in Autonomous Vehicles: Towards Optimisation of Driver–Vehicle Trust Management

2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC)

Trust determines public acceptance and uptake of autonomous vehicles (AV). Against popular assump... more Trust determines public acceptance and uptake of autonomous vehicles (AV). Against popular assumption, trustin-automation is not a unitary construct, but comprises trust subtypes that have different behavioural properties and implications. Understanding trust subtypes—specifically competence-based trust (CT) and integrity-based trust (IT)—is crucial for improving public communication about AVs, analysing trustdependent driver behaviours and designing trust-recovering interfaces. However, these issues have been overlooked in most past research. As a pioneering step, the goal of this research was to analyse how experience with AV failures affect CT and IT. After experience with AV driving errors, both trust subtypes were reduced, with CT showing greater reduction. Structural equation modelling revealed CT to be the primary contributor to acceptance for driving automation, with stronger subsequent impact on preference for fully autonomous (SAE L5) than on semi-autonomous driving (SAE L3). These findings inform that trust-repairing interface should target CT after driving errors, especially for higher automation levels where humans are further removed from the loop. Future directions concerning CT–IT interactions, and the impact of AV anthropomorphic design and connnected vehicle cyber-security on IT are discussed.

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Research paper thumbnail of Objective assessment of trait attentional control predicts driver response to emergency failures of vehicular automation

Accident Analysis & Prevention, 2022

With the advent of autonomous driving, the issue of human intervention during safety-critical eve... more With the advent of autonomous driving, the issue of human intervention during safety-critical events is an urgent topic of research. Supervisory monitoring, taking over vehicle control during automation failures and then bringing the vehicle to safety under time pressure are cognitively demanding tasks that pose varying difficulties across the driving population. This underpins a need to investigate individual differences (i.e., how people differ in their dispositional traits) in driver responses to automation system limits, so that autonomous vehicle design can be tailored to meet the safety-critical needs of higher-risk drivers. However, few studies thus far have examined individual differences, with self-report measures showing limited ability to predict driver takeover performance. To address this gap, the present study explored the utility of an established brain activity-based objective index of trait attentional control (frontal theta/beta ratio; TBR) in predicting driver interactions with conditional automation. Frontal TBR predicted drivers' average takeover reaction time, as well as the likelihood of accident after takeover. Moving towards practical applications, this study also demonstrated the utility of streamlined estimates of frontal TBR measured from the forehead electrodes and from a single crown electrode, with the latter showing better fidelity and predictive value. Overall, TBR is behaviourally relevant, measurable with minimal sensors and easily computable, rendering it a promising candidate for practical and objective assessment of drivers' neurocognitive traits that contribute to their AV driving readiness.

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Research paper thumbnail of Identification of gait-related brain activity using electroencephalographic signals

2017 8th International IEEE/EMBS Conference on Neural Engineering (NER), 2017

Restoring normal walking abilities following the loss of them is a challenge. Importantly, there ... more Restoring normal walking abilities following the loss of them is a challenge. Importantly, there is a growing need for a better understanding of brain plasticity and the neural involvements for the initiation and control of these abilities so as to develop better rehabilitation programmes and external support devices. In this paper, we attempt to identify gait-related neural activities by decoding neural signals obtained from electroencephalography (EEG) measurements while subjects performed three types of walking: without exoskeleton (free walking), and with exoskeleton support (zero force and assisting force). An average classification accuracy of 92.0% for training and 73.8% for testing sets was achieved using features extracted from mu and beta frequency bands. Furthermore, we found that mu band features contributed significantly to the classification accuracy and were localized mainly in sensorimotor regions that are associated with the control of the exoskeleton. These findings contribute meaningful insight on the neural dynamics associated with lower limb movements and provide useful information for future developments of orthotic devices and rehabilitation programs.

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Research paper thumbnail of Sensory Stimulation Enhances Functional Connectivity towards the Somatosensory Cortex in Upper Limb Amputation

2021 10th International IEEE/EMBS Conference on Neural Engineering (NER), 2021

Sensory stimulation elicits sensations in the phantom hand of individuals with upper limb amputat... more Sensory stimulation elicits sensations in the phantom hand of individuals with upper limb amputation. The reinstated sensory information is important to improve phantom limb perception and motor performance. In this work, we aimed to characterize the cortical impact of sensory stimulation on sensorimotor integration in upper limb amputees. To this goal, we investigated dynamic functional connectivity computed from electroencephalogram (EEG) recorded while amputees executed phantom hand movements with and without sensory stimulation. We focused on the dynamic functional connections to the somatosensory system and discovered that non-invasive sensory stimulation induced increased speed of information transfer, shown by decreased temporal distance, and increased number of connections from the motor, somatosensory, and multisensory processing systems. We show that the cortical impact of sensory stimulation is manifested not only through functional activities related to the primary somatosensory system, but also those involving the secondary somatosensory system.

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Research paper thumbnail of A new perspective of noise removal from EEG

2017 8th International IEEE/EMBS Conference on Neural Engineering (NER), 2017

Denoising, noise or interferences are removed from recorded signal to enhance the signal-to-noise... more Denoising, noise or interferences are removed from recorded signal to enhance the signal-to-noise ratio (SNR), is a crucial and ubiquitous step in the procedure of signal processing, especially for neurophysiological signal. This step facilitates following processing, such as feature extraction, classification, and data analyses. Conventional methods are based on the principle of separating noise components from the recorded signal and removing them, but these methods do not remove noise completely. In particular, conventional methods seems powerless to eliminate irregular and occasional noise bursts, which are caused by transient electrode contacting problem, head movements, or unpredictable factors. In this paper, we tackled the problem of noise removal from a new perspective, which is opposite to the conventional methods. Data portions that are contaminated by noise are entirely removed and then restored according to their relationships with the remaining signal. The rationale of this procedure is to purify the signal through addition rather than deduction that is normally executed in conventional methods. The results of both synthetic data and real EEG demonstrated that our idea is feasible and provides a new promising manner for noise removal.

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Research paper thumbnail of Differential Impact of Autonomous Vehicle Malfunctions on Human Trust

IEEE Transactions on Intelligent Transportation Systems, 2020

Trust in autonomous vehicles (AV) is of critical importance and demands comprehensive interdiscip... more Trust in autonomous vehicles (AV) is of critical importance and demands comprehensive interdisciplinary research. While most studies utilize subjective measures, we employ electroencephalography (EEG) to study in a more objective manner the cognitive states associated with trust during AV driving. Subjects drove a simulated AV in Conditional Automation Driving (SAE L3) and Full Automation Driving (SAE L5) modes. In the experimental design, malfunctions were induced at both automation levels. Self-reported trust in the AV was reduced immediately after Full Automation malfunctions, but not after Conditional Automation malfunctions when subjects were able to take over vehicle control to avoid danger. EEG analyses reveal that during Full Automation malfunctions, there was a significant enhancement in approach motivation (i.e. desire to re-engage) and a disruption of right frontal functional clustering that supports executive cognition (i.e. planning and decision-making). No neurocognitive disruptions were observed during Conditional Automation malfunctions. Our results demonstrate that it is not automation malfunctions per se (e.g. failure to decelerate) that deteriorate trust, but rather the inability for human drivers to adaptively mitigate the risk of negative outcomes (e.g. risk of crashing) resulting from those malfunctions. This is reflected in changes in brain activity associated with motivational state and action planning. Keeping the human driver on-the-loop protects against trust loss. Frontal alpha EEG is a neural correlate of trust-in-automation, with potential for trust monitoring using wearable technology to support driver-vehicle adaptivity.

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Research paper thumbnail of Driving Mental Fatigue Classification Based on Brain Functional Connectivity

Engineering Applications of Neural Networks, 2017

EEG techniques have been widely used for mental fatigue monitoring, which is an important factor ... more EEG techniques have been widely used for mental fatigue monitoring, which is an important factor for driving safety. In this work, we performed an experiment involving one hour driving simulation. Based on EEG recordings, we created brain functional networks in alpha power band with three different methods, partial directed coherence (PDC), direct transfer function (DTF) and phase lag index (PLI). Then, we performed feature selection and classification between alertness and fatigue states, using the functional connectivity as features. High accuracy (84.7%) was achieved, with 22 discriminative connections from PDC network. The selected features revealed alterations of the functional network due to mental fatigue and specifically reduction of information flow among areas. Finally, a feature ranking is provided, which can lead to electrode minimization for real-time fatigue monitoring applications.

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Research paper thumbnail of Sensory stimulation enhances phantom limb perception and movement decoding

Objective. A major challenge for controlling a prosthetic arm is communication between the device... more Objective. A major challenge for controlling a prosthetic arm is communication between the device and the user's phantom limb. We show the ability to enhance amputees' phantom limb perception and improve movement decoding through targeted transcutaneous electrical nerve stimulation (tTENS). Approach. Transcutaneous nerve stimulation experiments were performed with four amputee participants to map phantom limb perception. We measured myoelectric signals during phantom hand movements before and after amputees received sensory stimulation. Using electroencephalogram (EEG) monitoring, we measure the neural activity in sensorimotor regions during phantom movements and stimulation. In one participant, we also tracked sensory mapping over 2 years and movement decoding performance over 1 year. Main results. Results show improvements in the amputees' ability to perceive and move the phantom hand as a result of sensory stimulation, which leads to improved movement decoding. In the...

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Research paper thumbnail of Towards machine to brain interfaces: sensory stimulation enhances sensorimotor dynamic functional connectivity in upper limb amputees

Journal of Neural Engineering, 2020

Objective.Recent development of sensory stimulation techniques demonstrates the ability to elicit... more Objective.Recent development of sensory stimulation techniques demonstrates the ability to elicit touch-like phantom sensations in upper limb amputees. The cortical processing of this phantom sensation and the corresponding influences on sensorimotor functional connectivity have not been studied. We hypothesize that sensory stimulation has a profound impact on the sensorimotor cortical functional interactions, which will be uncovered by dynamic functional connectivity (dFC) analysis of amputees' electroencephalogram (EEG) recordings.Approach.We investigated dFC between cortical areas associated with somatosensory, motor, visual, and multisensory processing functions using EEG signals. We applied dFC to the EEG of two amputees performing hand movements with and without sensory stimulation and compared the results with those from three able-bodied subjects. We quantified the changes due to sensory stimulation using dFC metrics, namely temporal distance, number of connection paths, temporal global and local efficiencies, and clustering coefficient.Main results.We show a significant effect of sensory stimulation on functional connectivity in the amputee brains, with notable impact on multisensory processing between regions involved in sensorimotor processing. The dFC metrics reveal that sensory stimulation enhances the speed of information transfer (shown by decreases in temporal distance) and number of connection paths between the brain systems involved in sensorimotor processing, including primary somatosensory (S1) and motor, and higher order processing regions.Significance.This is the first work to reveal dynamic communication between somatosensory, motor, and higher order processing regions in the cortex of amputees in response to sensory stimulation. We believe that our work provides crucial insights on the cortical impact of sensory stimulation in amputees, which may lead to the design of personalized brain-informed sensory feedback paradigms. This in turn may lead to building novel Machine to Brain Interfaces involving sensory feedback and the resultant enhanced motor performance.

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Research paper thumbnail of Classification of brain signal (EEG) induced by shape-analogous letter perception

Advanced Engineering Informatics, 2019

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Research paper thumbnail of What Are Spectral and Spatial Distributions of EEG-EMG Correlations in Overground Walking? An Exploratory Study

IEEE Access, 2019

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Research paper thumbnail of Mental Workload Drives Different Reorganizations of Functional Cortical Connectivity Between 2D and 3D Simulated Flight Experiments

IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2019

Despite the apparent usefulness of efficient mental workload assessment in various real-world sit... more Despite the apparent usefulness of efficient mental workload assessment in various real-world situations, the underlying neural mechanism remains largely unknown, and studies of the mental workload are limited to well-controlled cognitive tasks using a 2D computer screen. In this paper, we investigated functional brain network alterations in a simulated flight experiment with three mental workload levels and compared the reorganization pattern between computer screen (2D) and virtual reality (3D) interfaces. We constructed multiband functional networks in electroencephalogram (EEG) source space, which were further assessed in terms of network efficiency and workload classification performances. We found that increased alpha band efficiencies and beta band local efficiency were associated with elevated mental workload levels, while beta band global efficiency exhibited distinct development trends between 2D and 3D interfaces. Furthermore, using a small subset of connectivity features, we achieved a satisfactory multi-level workload classification accuracy in both interfaces (82% for both 2D and 3D). Further inspection of these discriminative connectivity subsets, we found predominant alpha band connectivity features followed by beta and theta band features with different topological patterns between 2D and 3D interfaces. These findings allow for a more comprehensive interpretation of the neural mechanisms of mental workload in relation to real-world assessment.

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Research paper thumbnail of Neural Mechanisms of Mental Fatigue Revisited: New Insights from the Brain Connectome

Engineering, 2019

Abstract Maintaining sustained attention during a prolonged cognitive task often comes at a cost:... more Abstract Maintaining sustained attention during a prolonged cognitive task often comes at a cost: high levels of mental fatigue. Heuristically, mental fatigue refers to a feeling of tiredness or exhaustion, and a disengagement from the task at hand; it manifests as impaired cognitive and behavioral performance. In order to effectively reduce the undesirable yet preventable consequences of mental fatigue in many real-world workspaces, a better understanding of the underlying neural mechanisms is needed, and continuous efforts have been devoted to this topic. In comparison with conventional univariate approaches, which are widely utilized in fatigue studies, convergent evidence has shown that multivariate functional connectivity analysis may lead to richer information about mental fatigue. In fact, mental fatigue is increasingly thought to be related to the deviated reorganization of functional connectivity among brain regions in recent studies. In addition, graph theoretical analysis has shed new light on quantitatively assessing the reorganization of the brain functional networks that are modulated by mental fatigue. This review article begins with a brief introduction to neuroimaging studies on mental fatigue and the brain connectome, followed by a thorough overview of connectome studies on mental fatigue. Although only a limited number of studies have been published thus far, it is believed that the brain connectome can be a useful approach not only for the elucidation of underlying neural mechanisms in the nascent field of neuroergonomics, but also for the automatic detection and classification of mental fatigue in order to address the prevention of fatigue-related human error in the near future.

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Research paper thumbnail of Functional Connectivity Analysis of Mental Fatigue Reveals Different Network Topological Alterations Between Driving and Vigilance Tasks

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society, 2018

Despite the apparent importance of mental fatigue detection, a reliable application is hindered d... more Despite the apparent importance of mental fatigue detection, a reliable application is hindered due to the incomprehensive understanding of the neural mechanisms of mental fatigue. In this paper, we investigated the topological alterations of functional brain networks in the theta band (4 - 7 Hz) of electroencephalography (EEG) data from 40 male subjects undergoing two distinct fatigue-inducing tasks: a low-intensity one-hour simulated driving and a high-demanding half-hour sustained attention task [psychomotor vigilance task (PVT)]. Behaviorally, subjects demonstrated a robust mental fatigue effect, as reflected by significantly declined performances in cognitive tasks prior and post these two tasks. Furthermore, characteristic path length presented a positive correlation with task duration, which led to a significant increase between the first and the last five minutes of both tasks, indicating a fatigue-related disruption in information processing efficiency. However, significant...

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Research paper thumbnail of Spatio-spectral Representation Learning for Electroencephalographic Gait-Pattern Classification

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society, Jan 7, 2018

The brain plays a pivotal role in locomotion by coordinating muscles through interconnections tha... more The brain plays a pivotal role in locomotion by coordinating muscles through interconnections that get established by the peripheral nervous system. To date, many attempts have been made to reveal the underlying mechanisms of humans' gait. However, decoding cortical processes associated with different walking conditions using EEG signals for gait-pattern classification is a less-explored research area. In this paper, we design an EEG-based experiment with four walking conditions (i.e., free walking, and exoskeleton-assisted walking at zero, low and high assistive forces by the use of a unilateral exoskeleton to right lower limb). We proposed spatio-spectral representation learning (SSRL), a deep neural network topology with shared weights to learn the spatial and spectral representations of multi-channel EEG signals during walking. Adoption of weight sharing reduces the number of free parameters, while learning spatial and spectral equivariant features. SSRL outperformed state-o...

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Research paper thumbnail of A mental fatigue index based on regression using mulitband EEG features with application in simulated driving

Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference, Jul 1, 2017

Development of accurate fatigue level prediction models is of great importance for driving safety... more Development of accurate fatigue level prediction models is of great importance for driving safety. In parallel, a limited number of sensors is a prerequisite for development of applicable wearable devices. Several EEG-based studies so far have performed classification in two or few levels, while others have proposed indices based on power ratios. Here, we utilized a regression Random Forest model in order to provide more accurate continuous fatigue level prediction. In detail, multiband power features were extracted from EEG data recorded from one hour simulated driving task. Next, cross-subject regression was performed to obtain common fatigue-related discriminative features. We achieved satisfactory prediction accuracy and simultaneously we minimized required electrodes, proposing to use a set of 3 electrodes.

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Research paper thumbnail of Distinct spatio-temporal and spectral brain patterns for different thermal stimuli perception

Scientific Reports, Jan 18, 2022

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Research paper thumbnail of Chimera States in a Two-Population Network of Coupled Pendula

arXiv (Cornell University), Aug 26, 2013

More than a decade ago, a surprising coexistence of synchronized and asynchronous behavior called... more More than a decade ago, a surprising coexistence of synchronized and asynchronous behavior called the chimera state was discovered in networks of nonlocally coupled identical phase oscillators. In later years, chimeras were found to occur in a variety of theoretical and experimental studies of chemical and optical systems, as well as models of neuron dynamics. In this Letter, we study two coupled populations of pendula represented by phase oscillators with a second derivative term multiplied by a mass parameter mmm and treat the first order derivative terms as dissipation with parameter epsilon>0\epsilon>0epsilon>0. We first present numerical evidence showing that chimeras do exist in this system for small mass values 0<m<<10<m<<10<m<<1 and epsilon\epsilonepsilon greater than a threshold value epsilonthr(m)>0\epsilon_{thr}(m)>0epsilonthr(m)>0 that grows linearly with mmm. We then proceed to explain these states by reducing the coherent population to a single damped pendulum equation driven parametrically by oscillating averaged quantities related to the incoherent population.

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Research paper thumbnail of Sensory Feedback in Upper Limb Amputees Impacts Cortical Activity as Revealed by Multiscale Connectivity Analysis

2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)

Sensory feedback in upper limb amputees is crucial for improving movement decoding and also to en... more Sensory feedback in upper limb amputees is crucial for improving movement decoding and also to enhance embodiment of the prosthetic limb. Recently, an increasing number of invasive and noninvasive solutions for sensory stimulation have demonstrated the capability of providing a range of sensations to upper limb amputees. However, the cortical impact of restored sensation is not clearly understood. Particularly, understanding the cortical connectivity changes at multiple scales (nodal and modular) in response to sensory stimulation, can reveal crucial information on how amputees brain process the sensory stimuli. Using Electroencephalography (EEG) signals, we compared the cortical connectivity network in response to sensory feedback provided by targeted transcutaneous electrical nerve stimulation (tTENS) in an upper limb amputee during phantom upper limb movements. We focused our cortical connectivity analysis on four functional modules comprising of 20 brain regions that are primarily associated with a visually guided motor task (visual, motor, somatosensory and multisensory integration (MI)) used in this study. At the modular level, we observed that the hubness (a graph theoretic measure quantifying the importance of brain regions in integrating brain function) of the motor module decreases whereas that of the somatosensory module increases in presence of tTENS feedback. At the nodal level, similar observations were made for the visual and MI regions. This is the first work to reveal the impact of sensory feedback at multiple scales in the cortex of amputees in response to sensory stimulation.

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Research paper thumbnail of Olfactory-induced Positive Affect and Autonomic Response as a Function of Hedonic and Intensity Attributes of Fragrances

2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)

Olfactory perception is intrinsically tied to emotional processing, in both behavior and neurophy... more Olfactory perception is intrinsically tied to emotional processing, in both behavior and neurophysiology. Despite advances in olfactory-affective neuroscience, it is unclear how separate attributes of odor stimuli contribute to olfactoryinduced emotions, especially within the positive segment of the hedonic dimension to avoid potential cross-valence confounds. In this study, we examined how pleasantness and intensity of fragrances relate to different grades of positive affect. Our results show that greater odor pleasantness and intensity are independently associated with stronger positive affect. Pleasantness has a greater influence than intensity in evoking a positive vs. neutral affect, whereas intensity is more impactful than pleasantness in evoking an extreme positive vs. positive response. Autonomic response, as assessed by the galvanic skin response (GSR) was found to decrease with increasing pleasantness but not intensity. This clarifies how olfactory and affective processing induce significant downstream effects in peripheral physiology and self-reported affective experience, pertinent to the thriving field of olfactory neuromarkerting.

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Research paper thumbnail of Subtype Divergences of Trust in Autonomous Vehicles: Towards Optimisation of Driver–Vehicle Trust Management

2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC)

Trust determines public acceptance and uptake of autonomous vehicles (AV). Against popular assump... more Trust determines public acceptance and uptake of autonomous vehicles (AV). Against popular assumption, trustin-automation is not a unitary construct, but comprises trust subtypes that have different behavioural properties and implications. Understanding trust subtypes—specifically competence-based trust (CT) and integrity-based trust (IT)—is crucial for improving public communication about AVs, analysing trustdependent driver behaviours and designing trust-recovering interfaces. However, these issues have been overlooked in most past research. As a pioneering step, the goal of this research was to analyse how experience with AV failures affect CT and IT. After experience with AV driving errors, both trust subtypes were reduced, with CT showing greater reduction. Structural equation modelling revealed CT to be the primary contributor to acceptance for driving automation, with stronger subsequent impact on preference for fully autonomous (SAE L5) than on semi-autonomous driving (SAE L3). These findings inform that trust-repairing interface should target CT after driving errors, especially for higher automation levels where humans are further removed from the loop. Future directions concerning CT–IT interactions, and the impact of AV anthropomorphic design and connnected vehicle cyber-security on IT are discussed.

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Research paper thumbnail of Objective assessment of trait attentional control predicts driver response to emergency failures of vehicular automation

Accident Analysis & Prevention, 2022

With the advent of autonomous driving, the issue of human intervention during safety-critical eve... more With the advent of autonomous driving, the issue of human intervention during safety-critical events is an urgent topic of research. Supervisory monitoring, taking over vehicle control during automation failures and then bringing the vehicle to safety under time pressure are cognitively demanding tasks that pose varying difficulties across the driving population. This underpins a need to investigate individual differences (i.e., how people differ in their dispositional traits) in driver responses to automation system limits, so that autonomous vehicle design can be tailored to meet the safety-critical needs of higher-risk drivers. However, few studies thus far have examined individual differences, with self-report measures showing limited ability to predict driver takeover performance. To address this gap, the present study explored the utility of an established brain activity-based objective index of trait attentional control (frontal theta/beta ratio; TBR) in predicting driver interactions with conditional automation. Frontal TBR predicted drivers' average takeover reaction time, as well as the likelihood of accident after takeover. Moving towards practical applications, this study also demonstrated the utility of streamlined estimates of frontal TBR measured from the forehead electrodes and from a single crown electrode, with the latter showing better fidelity and predictive value. Overall, TBR is behaviourally relevant, measurable with minimal sensors and easily computable, rendering it a promising candidate for practical and objective assessment of drivers' neurocognitive traits that contribute to their AV driving readiness.

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Research paper thumbnail of Identification of gait-related brain activity using electroencephalographic signals

2017 8th International IEEE/EMBS Conference on Neural Engineering (NER), 2017

Restoring normal walking abilities following the loss of them is a challenge. Importantly, there ... more Restoring normal walking abilities following the loss of them is a challenge. Importantly, there is a growing need for a better understanding of brain plasticity and the neural involvements for the initiation and control of these abilities so as to develop better rehabilitation programmes and external support devices. In this paper, we attempt to identify gait-related neural activities by decoding neural signals obtained from electroencephalography (EEG) measurements while subjects performed three types of walking: without exoskeleton (free walking), and with exoskeleton support (zero force and assisting force). An average classification accuracy of 92.0% for training and 73.8% for testing sets was achieved using features extracted from mu and beta frequency bands. Furthermore, we found that mu band features contributed significantly to the classification accuracy and were localized mainly in sensorimotor regions that are associated with the control of the exoskeleton. These findings contribute meaningful insight on the neural dynamics associated with lower limb movements and provide useful information for future developments of orthotic devices and rehabilitation programs.

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Research paper thumbnail of Sensory Stimulation Enhances Functional Connectivity towards the Somatosensory Cortex in Upper Limb Amputation

2021 10th International IEEE/EMBS Conference on Neural Engineering (NER), 2021

Sensory stimulation elicits sensations in the phantom hand of individuals with upper limb amputat... more Sensory stimulation elicits sensations in the phantom hand of individuals with upper limb amputation. The reinstated sensory information is important to improve phantom limb perception and motor performance. In this work, we aimed to characterize the cortical impact of sensory stimulation on sensorimotor integration in upper limb amputees. To this goal, we investigated dynamic functional connectivity computed from electroencephalogram (EEG) recorded while amputees executed phantom hand movements with and without sensory stimulation. We focused on the dynamic functional connections to the somatosensory system and discovered that non-invasive sensory stimulation induced increased speed of information transfer, shown by decreased temporal distance, and increased number of connections from the motor, somatosensory, and multisensory processing systems. We show that the cortical impact of sensory stimulation is manifested not only through functional activities related to the primary somatosensory system, but also those involving the secondary somatosensory system.

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Research paper thumbnail of A new perspective of noise removal from EEG

2017 8th International IEEE/EMBS Conference on Neural Engineering (NER), 2017

Denoising, noise or interferences are removed from recorded signal to enhance the signal-to-noise... more Denoising, noise or interferences are removed from recorded signal to enhance the signal-to-noise ratio (SNR), is a crucial and ubiquitous step in the procedure of signal processing, especially for neurophysiological signal. This step facilitates following processing, such as feature extraction, classification, and data analyses. Conventional methods are based on the principle of separating noise components from the recorded signal and removing them, but these methods do not remove noise completely. In particular, conventional methods seems powerless to eliminate irregular and occasional noise bursts, which are caused by transient electrode contacting problem, head movements, or unpredictable factors. In this paper, we tackled the problem of noise removal from a new perspective, which is opposite to the conventional methods. Data portions that are contaminated by noise are entirely removed and then restored according to their relationships with the remaining signal. The rationale of this procedure is to purify the signal through addition rather than deduction that is normally executed in conventional methods. The results of both synthetic data and real EEG demonstrated that our idea is feasible and provides a new promising manner for noise removal.

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Research paper thumbnail of Differential Impact of Autonomous Vehicle Malfunctions on Human Trust

IEEE Transactions on Intelligent Transportation Systems, 2020

Trust in autonomous vehicles (AV) is of critical importance and demands comprehensive interdiscip... more Trust in autonomous vehicles (AV) is of critical importance and demands comprehensive interdisciplinary research. While most studies utilize subjective measures, we employ electroencephalography (EEG) to study in a more objective manner the cognitive states associated with trust during AV driving. Subjects drove a simulated AV in Conditional Automation Driving (SAE L3) and Full Automation Driving (SAE L5) modes. In the experimental design, malfunctions were induced at both automation levels. Self-reported trust in the AV was reduced immediately after Full Automation malfunctions, but not after Conditional Automation malfunctions when subjects were able to take over vehicle control to avoid danger. EEG analyses reveal that during Full Automation malfunctions, there was a significant enhancement in approach motivation (i.e. desire to re-engage) and a disruption of right frontal functional clustering that supports executive cognition (i.e. planning and decision-making). No neurocognitive disruptions were observed during Conditional Automation malfunctions. Our results demonstrate that it is not automation malfunctions per se (e.g. failure to decelerate) that deteriorate trust, but rather the inability for human drivers to adaptively mitigate the risk of negative outcomes (e.g. risk of crashing) resulting from those malfunctions. This is reflected in changes in brain activity associated with motivational state and action planning. Keeping the human driver on-the-loop protects against trust loss. Frontal alpha EEG is a neural correlate of trust-in-automation, with potential for trust monitoring using wearable technology to support driver-vehicle adaptivity.

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Research paper thumbnail of Driving Mental Fatigue Classification Based on Brain Functional Connectivity

Engineering Applications of Neural Networks, 2017

EEG techniques have been widely used for mental fatigue monitoring, which is an important factor ... more EEG techniques have been widely used for mental fatigue monitoring, which is an important factor for driving safety. In this work, we performed an experiment involving one hour driving simulation. Based on EEG recordings, we created brain functional networks in alpha power band with three different methods, partial directed coherence (PDC), direct transfer function (DTF) and phase lag index (PLI). Then, we performed feature selection and classification between alertness and fatigue states, using the functional connectivity as features. High accuracy (84.7%) was achieved, with 22 discriminative connections from PDC network. The selected features revealed alterations of the functional network due to mental fatigue and specifically reduction of information flow among areas. Finally, a feature ranking is provided, which can lead to electrode minimization for real-time fatigue monitoring applications.

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Research paper thumbnail of Sensory stimulation enhances phantom limb perception and movement decoding

Objective. A major challenge for controlling a prosthetic arm is communication between the device... more Objective. A major challenge for controlling a prosthetic arm is communication between the device and the user's phantom limb. We show the ability to enhance amputees' phantom limb perception and improve movement decoding through targeted transcutaneous electrical nerve stimulation (tTENS). Approach. Transcutaneous nerve stimulation experiments were performed with four amputee participants to map phantom limb perception. We measured myoelectric signals during phantom hand movements before and after amputees received sensory stimulation. Using electroencephalogram (EEG) monitoring, we measure the neural activity in sensorimotor regions during phantom movements and stimulation. In one participant, we also tracked sensory mapping over 2 years and movement decoding performance over 1 year. Main results. Results show improvements in the amputees' ability to perceive and move the phantom hand as a result of sensory stimulation, which leads to improved movement decoding. In the...

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Research paper thumbnail of Towards machine to brain interfaces: sensory stimulation enhances sensorimotor dynamic functional connectivity in upper limb amputees

Journal of Neural Engineering, 2020

Objective.Recent development of sensory stimulation techniques demonstrates the ability to elicit... more Objective.Recent development of sensory stimulation techniques demonstrates the ability to elicit touch-like phantom sensations in upper limb amputees. The cortical processing of this phantom sensation and the corresponding influences on sensorimotor functional connectivity have not been studied. We hypothesize that sensory stimulation has a profound impact on the sensorimotor cortical functional interactions, which will be uncovered by dynamic functional connectivity (dFC) analysis of amputees' electroencephalogram (EEG) recordings.Approach.We investigated dFC between cortical areas associated with somatosensory, motor, visual, and multisensory processing functions using EEG signals. We applied dFC to the EEG of two amputees performing hand movements with and without sensory stimulation and compared the results with those from three able-bodied subjects. We quantified the changes due to sensory stimulation using dFC metrics, namely temporal distance, number of connection paths, temporal global and local efficiencies, and clustering coefficient.Main results.We show a significant effect of sensory stimulation on functional connectivity in the amputee brains, with notable impact on multisensory processing between regions involved in sensorimotor processing. The dFC metrics reveal that sensory stimulation enhances the speed of information transfer (shown by decreases in temporal distance) and number of connection paths between the brain systems involved in sensorimotor processing, including primary somatosensory (S1) and motor, and higher order processing regions.Significance.This is the first work to reveal dynamic communication between somatosensory, motor, and higher order processing regions in the cortex of amputees in response to sensory stimulation. We believe that our work provides crucial insights on the cortical impact of sensory stimulation in amputees, which may lead to the design of personalized brain-informed sensory feedback paradigms. This in turn may lead to building novel Machine to Brain Interfaces involving sensory feedback and the resultant enhanced motor performance.

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Research paper thumbnail of Classification of brain signal (EEG) induced by shape-analogous letter perception

Advanced Engineering Informatics, 2019

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Research paper thumbnail of What Are Spectral and Spatial Distributions of EEG-EMG Correlations in Overground Walking? An Exploratory Study

IEEE Access, 2019

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Research paper thumbnail of Mental Workload Drives Different Reorganizations of Functional Cortical Connectivity Between 2D and 3D Simulated Flight Experiments

IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2019

Despite the apparent usefulness of efficient mental workload assessment in various real-world sit... more Despite the apparent usefulness of efficient mental workload assessment in various real-world situations, the underlying neural mechanism remains largely unknown, and studies of the mental workload are limited to well-controlled cognitive tasks using a 2D computer screen. In this paper, we investigated functional brain network alterations in a simulated flight experiment with three mental workload levels and compared the reorganization pattern between computer screen (2D) and virtual reality (3D) interfaces. We constructed multiband functional networks in electroencephalogram (EEG) source space, which were further assessed in terms of network efficiency and workload classification performances. We found that increased alpha band efficiencies and beta band local efficiency were associated with elevated mental workload levels, while beta band global efficiency exhibited distinct development trends between 2D and 3D interfaces. Furthermore, using a small subset of connectivity features, we achieved a satisfactory multi-level workload classification accuracy in both interfaces (82% for both 2D and 3D). Further inspection of these discriminative connectivity subsets, we found predominant alpha band connectivity features followed by beta and theta band features with different topological patterns between 2D and 3D interfaces. These findings allow for a more comprehensive interpretation of the neural mechanisms of mental workload in relation to real-world assessment.

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Research paper thumbnail of Neural Mechanisms of Mental Fatigue Revisited: New Insights from the Brain Connectome

Engineering, 2019

Abstract Maintaining sustained attention during a prolonged cognitive task often comes at a cost:... more Abstract Maintaining sustained attention during a prolonged cognitive task often comes at a cost: high levels of mental fatigue. Heuristically, mental fatigue refers to a feeling of tiredness or exhaustion, and a disengagement from the task at hand; it manifests as impaired cognitive and behavioral performance. In order to effectively reduce the undesirable yet preventable consequences of mental fatigue in many real-world workspaces, a better understanding of the underlying neural mechanisms is needed, and continuous efforts have been devoted to this topic. In comparison with conventional univariate approaches, which are widely utilized in fatigue studies, convergent evidence has shown that multivariate functional connectivity analysis may lead to richer information about mental fatigue. In fact, mental fatigue is increasingly thought to be related to the deviated reorganization of functional connectivity among brain regions in recent studies. In addition, graph theoretical analysis has shed new light on quantitatively assessing the reorganization of the brain functional networks that are modulated by mental fatigue. This review article begins with a brief introduction to neuroimaging studies on mental fatigue and the brain connectome, followed by a thorough overview of connectome studies on mental fatigue. Although only a limited number of studies have been published thus far, it is believed that the brain connectome can be a useful approach not only for the elucidation of underlying neural mechanisms in the nascent field of neuroergonomics, but also for the automatic detection and classification of mental fatigue in order to address the prevention of fatigue-related human error in the near future.

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Research paper thumbnail of Functional Connectivity Analysis of Mental Fatigue Reveals Different Network Topological Alterations Between Driving and Vigilance Tasks

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society, 2018

Despite the apparent importance of mental fatigue detection, a reliable application is hindered d... more Despite the apparent importance of mental fatigue detection, a reliable application is hindered due to the incomprehensive understanding of the neural mechanisms of mental fatigue. In this paper, we investigated the topological alterations of functional brain networks in the theta band (4 - 7 Hz) of electroencephalography (EEG) data from 40 male subjects undergoing two distinct fatigue-inducing tasks: a low-intensity one-hour simulated driving and a high-demanding half-hour sustained attention task [psychomotor vigilance task (PVT)]. Behaviorally, subjects demonstrated a robust mental fatigue effect, as reflected by significantly declined performances in cognitive tasks prior and post these two tasks. Furthermore, characteristic path length presented a positive correlation with task duration, which led to a significant increase between the first and the last five minutes of both tasks, indicating a fatigue-related disruption in information processing efficiency. However, significant...

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Research paper thumbnail of Spatio-spectral Representation Learning for Electroencephalographic Gait-Pattern Classification

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society, Jan 7, 2018

The brain plays a pivotal role in locomotion by coordinating muscles through interconnections tha... more The brain plays a pivotal role in locomotion by coordinating muscles through interconnections that get established by the peripheral nervous system. To date, many attempts have been made to reveal the underlying mechanisms of humans' gait. However, decoding cortical processes associated with different walking conditions using EEG signals for gait-pattern classification is a less-explored research area. In this paper, we design an EEG-based experiment with four walking conditions (i.e., free walking, and exoskeleton-assisted walking at zero, low and high assistive forces by the use of a unilateral exoskeleton to right lower limb). We proposed spatio-spectral representation learning (SSRL), a deep neural network topology with shared weights to learn the spatial and spectral representations of multi-channel EEG signals during walking. Adoption of weight sharing reduces the number of free parameters, while learning spatial and spectral equivariant features. SSRL outperformed state-o...

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Research paper thumbnail of A mental fatigue index based on regression using mulitband EEG features with application in simulated driving

Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference, Jul 1, 2017

Development of accurate fatigue level prediction models is of great importance for driving safety... more Development of accurate fatigue level prediction models is of great importance for driving safety. In parallel, a limited number of sensors is a prerequisite for development of applicable wearable devices. Several EEG-based studies so far have performed classification in two or few levels, while others have proposed indices based on power ratios. Here, we utilized a regression Random Forest model in order to provide more accurate continuous fatigue level prediction. In detail, multiband power features were extracted from EEG data recorded from one hour simulated driving task. Next, cross-subject regression was performed to obtain common fatigue-related discriminative features. We achieved satisfactory prediction accuracy and simultaneously we minimized required electrodes, proposing to use a set of 3 electrodes.

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