Special Issue "Brain Plasticity, Cognitive Training and Mental States Assessment"

A special issue of Brain Sciences (ISSN 2076-3425).

Deadline for manuscript submissions: closed (15 June 2020).

Special Issue Editors

Dr. Gianluca Borghini
E-Mail Website
Guest Editor
Department of Molecular Medicine, Sapienza Università di Roma, Rome, Italy
Interests: cognitive neuroscience; machine learning; neuroscience; signal processing
Special Issues and Collections in MDPI journals
Dr. Pietro Aricò
E-Mail Website
Guest Editor
Department of Molecular Medicine, “Sapienza” University of Rome, 00185 Rome, Italy
Interests: brain-computer interface; EEG; machine learning; neuroscience; mental states; human factors
Special Issues and Collections in MDPI journals
Dr. Alessandra Anzolin
E-Mail Website
Guest Editor
MGH/HST Athinoula A. Martinos Center for Biomedical Imaging, Boston, MA, USA
Interests: high-density EEG; hyperscanning; brain connectivity; brain–computer interface; graph theory; cognitive functions; chronic pain
Dr. Gianluca Di Flumeri
E-Mail Website
Guest Editor
Department of Molecular Medicine, Sapienza University of Rome, 00185 Rome, Italy
Interests: brain activity; cognitive neuroscience; EEG; signal processing; brain computer interface
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

In recent years, training programs in clinical, public, transport, and operative environments have obtained high importance due to their impacts on the efficiency of the operators themselves and most importantly on overall safety. The capacity of the brain to change itself is a key aspect in dealing with cognitive and physical training programs, and highly demanding or stressful situations. In this regard, being able to measure such brain capabilities and the related changes would be very useful. For example, the outcomes could be used to better assess both the trainee/patient learning process and the variation of mental states throughout the sessions, or to better tailor the training program itself at the single-subject level. In this regard, neuroimaging technology and methodology appear to be the most appropriate means to gather such information and directly employ it for the optimization of users and training schedules.

This Special Issue is dedicated to studies on methodologies to infer objective information about brain changes and user mental state variations during training/rehabilitation sessions. Studies on technologies to improve or accelerate learning processes are also welcome.

Dr. Gianluca Borghini
Dr. Pietro Aricò
Dr. Alessandra Anzolin
Dr. Gianluca Di Flumeri
Guest Editors

Manuscript Submission Information

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

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

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 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

  • brain plasticity
  • learning processing
  • cognitive training
  • mental states assessment
  • cognitive rehabilitation
  • human interaction
  • high-resolution EEG
  • machine learning
  • brain connectivity
  • cognitive control behavior

Published Papers (10 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

Article
Forefront Users’ Experience Evaluation by Employing Together Virtual Reality and Electroencephalography: A Case Study on Cognitive Effects of Scents
Brain Sci. 2021, 11(2), 256; https://doi.org/10.3390/brainsci11020256 - 18 Feb 2021
Cited by 1 | Viewed by 702
Abstract
Scents have the ability to affect peoples’ mental states and task performance with to different extents. It has been widely demonstrated that the lemon scent, included in most all-purpose cleaners, elicits stimulation and activation, while the lavender scent elicits relaxation and sedative effects. [...] Read more.
Scents have the ability to affect peoples’ mental states and task performance with to different extents. It has been widely demonstrated that the lemon scent, included in most all-purpose cleaners, elicits stimulation and activation, while the lavender scent elicits relaxation and sedative effects. The present study aimed at investigating and fostering a novel approach to evaluate users’ experience with respect to scents’ effects through the joint employment of Virtual Reality and users’ neurophysiological monitoring, in particular Electroencephalography. In particular, this study, involving 42 participants, aimed to compare the effects of lemon and lavender scents on the deployment of cognitive resources during a daily life experience consisting in a train journey carried out in virtual reality. Our findings showed a significant higher request of cognitive resources during the processing of an informative message for subjects exposed to the lavender scent with respect to the lemon exposure. No differences were found between lemon and lavender conditions on the self-reported items of pleasantness and involvement; as this study demonstrated, the employment of the lavender scent preserves the quality of the customer experience to the same extent as the more widely used lemon scent. Full article
(This article belongs to the Special Issue Brain Plasticity, Cognitive Training and Mental States Assessment)
Show Figures

Figure 1

Article
Regression Networks for Neurophysiological Indicator Evaluation in Practicing Motor Imagery Tasks
Brain Sci. 2020, 10(10), 707; https://doi.org/10.3390/brainsci10100707 - 04 Oct 2020
Cited by 1 | Viewed by 792
Abstract
Motor Imagery (MI) promotes motor learning in activities, like developing professional motor skills, sports gestures, and patient rehabilitation. However, up to 30% of users may not develop enough coordination skills after training sessions because of inter and intra-subject variability. Here, we develop a [...] Read more.
Motor Imagery (MI) promotes motor learning in activities, like developing professional motor skills, sports gestures, and patient rehabilitation. However, up to 30% of users may not develop enough coordination skills after training sessions because of inter and intra-subject variability. Here, we develop a data-driven estimator, termed Deep Regression Network (DRN), which jointly extracts and performs the regression analysis in order to assess the efficiency of the individual brain networks in practicing MI tasks. The proposed double-stage estimator initially learns a pool of deep patterns, extracted from the input data, in order to feed a neural regression model, allowing for infering the distinctiveness between subject assemblies having similar variability. The results, which were obtained on real-world MI data, prove that the DRN estimator fosters pre-training neural desynchronization and initial training synchronization to predict the bi-class accuracy response, thus providing a better understanding of the Brain–Computer Interface inefficiency of subjects. Full article
(This article belongs to the Special Issue Brain Plasticity, Cognitive Training and Mental States Assessment)
Show Figures

Figure 1

Article
A Novel Mutual Information Based Feature Set for Drivers’ Mental Workload Evaluation Using Machine Learning
Brain Sci. 2020, 10(8), 551; https://doi.org/10.3390/brainsci10080551 - 13 Aug 2020
Cited by 2 | Viewed by 1120
Abstract
Analysis of physiological signals, electroencephalography more specifically, is considered a very promising technique to obtain objective measures for mental workload evaluation, however, it requires a complex apparatus to record, and thus, with poor usability in monitoring in-vehicle drivers’ mental workload. This study proposes [...] Read more.
Analysis of physiological signals, electroencephalography more specifically, is considered a very promising technique to obtain objective measures for mental workload evaluation, however, it requires a complex apparatus to record, and thus, with poor usability in monitoring in-vehicle drivers’ mental workload. This study proposes a methodology of constructing a novel mutual information-based feature set from the fusion of electroencephalography and vehicular signals acquired through a real driving experiment and deployed in evaluating drivers’ mental workload. Mutual information of electroencephalography and vehicular signals were used as the prime factor for the fusion of features. In order to assess the reliability of the developed feature set mental workload score prediction, classification and event classification tasks were performed using different machine learning models. Moreover, features extracted from electroencephalography were used to compare the performance. In the prediction of mental workload score, expert-defined scores were used as the target values. For classification tasks, true labels were set from contextual information of the experiment. An extensive evaluation of every prediction tasks was carried out using different validation methods. In predicting the mental workload score from the proposed feature set lowest mean absolute error was 0.09 and for classifying mental workload highest accuracy was 94%. According to the outcome of the study, it can be stated that the novel mutual information based features developed through the proposed approach can be employed to classify and monitor in-vehicle drivers’ mental workload. Full article
(This article belongs to the Special Issue Brain Plasticity, Cognitive Training and Mental States Assessment)
Show Figures

Figure 1

Article
Towards Intelligent Data Analytics: A Case Study in Driver Cognitive Load Classification
Brain Sci. 2020, 10(8), 526; https://doi.org/10.3390/brainsci10080526 - 06 Aug 2020
Cited by 2 | Viewed by 1034
Abstract
One debatable issue in traffic safety research is that the cognitive load by secondary tasks reduces primary task performance, i.e., driving. In this paper, the study adopted a version of the n-back task as a cognitively loading secondary task on the primary task, [...] Read more.
One debatable issue in traffic safety research is that the cognitive load by secondary tasks reduces primary task performance, i.e., driving. In this paper, the study adopted a version of the n-back task as a cognitively loading secondary task on the primary task, i.e., driving; where drivers drove in three different simulated driving scenarios. This paper has taken a multimodal approach to perform ‘intelligent multivariate data analytics’ based on machine learning (ML). Here, the k-nearest neighbour (k-NN), support vector machine (SVM), and random forest (RF) are used for driver cognitive load classification. Moreover, physiological measures have proven to be sophisticated in cognitive load identification, yet it suffers from confounding factors and noise. Therefore, this work uses multi-component signals, i.e., physiological measures and vehicular features to overcome that problem. Both multiclass and binary classifications have been performed to distinguish normal driving from cognitive load tasks. To identify the optimal feature set, two feature selection algorithms, i.e., sequential forward floating selection (SFFS) and random forest have been applied where out of 323 features, a subset of 42 features has been selected as the best feature subset. For the classification, RF has shown better performance with F1-score of 0.75 and 0.80 than two other algorithms. Moreover, the result shows that using multicomponent features classifiers could classify better than using features from a single source. Full article
(This article belongs to the Special Issue Brain Plasticity, Cognitive Training and Mental States Assessment)
Show Figures

Figure 1

Article
Which Effects on Neuroanatomy and Path-Integration Survive? Results of a Randomized Controlled Study on Intensive Balance Training
Brain Sci. 2020, 10(4), 210; https://doi.org/10.3390/brainsci10040210 - 03 Apr 2020
Cited by 3 | Viewed by 1275
Abstract
Balancing is a complex task requiring the integration of visual, somatosensory and vestibular inputs. The vestibular system is linked to the hippocampus, a brain structure crucial for spatial orientation. Here we tested the immediate and sustained effects of a one-month-long slackline training program [...] Read more.
Balancing is a complex task requiring the integration of visual, somatosensory and vestibular inputs. The vestibular system is linked to the hippocampus, a brain structure crucial for spatial orientation. Here we tested the immediate and sustained effects of a one-month-long slackline training program on balancing and orientation abilities as well as on brain volumes in young adults without any prior experience in that skill. On the corrected level, we could not find any interaction effects for brain volumes, but the effect sizes were small to medium. A subsequent within-training-group analysis revealed volumetric increments within the somatosensory cortex and decrements within posterior insula, cerebellum and putamen remained stable over time. No significant interaction effects were observed on the clinical balance and the spatial orientation task two months after the training period (follow-up). We interpret these findings as a shift away from processes crucial for automatized motor output towards processes related to voluntarily controlled movements. The decrease in insular volume in the training group we propose to result from multisensory interaction of the vestibular with the visual and somatosensory systems. The discrepancy between sustained effects in the brain of the training group on the one hand and transient benefits in function on the other may indicate that for the latter to be retained a longer-term practice is required. Full article
(This article belongs to the Special Issue Brain Plasticity, Cognitive Training and Mental States Assessment)
Show Figures

Figure 1

Article
Multimodal Affective State Assessment Using fNIRS + EEG and Spontaneous Facial Expression
Brain Sci. 2020, 10(2), 85; https://doi.org/10.3390/brainsci10020085 - 06 Feb 2020
Cited by 4 | Viewed by 1633
Abstract
Human facial expressions are regarded as a vital indicator of one’s emotion and intention, and even reveal the state of health and wellbeing. Emotional states have been associated with information processing within and between subcortical and cortical areas of the brain, including the [...] Read more.
Human facial expressions are regarded as a vital indicator of one’s emotion and intention, and even reveal the state of health and wellbeing. Emotional states have been associated with information processing within and between subcortical and cortical areas of the brain, including the amygdala and prefrontal cortex. In this study, we evaluated the relationship between spontaneous human facial affective expressions and multi-modal brain activity measured via non-invasive and wearable sensors: functional near-infrared spectroscopy (fNIRS) and electroencephalography (EEG) signals. The affective states of twelve male participants detected via fNIRS, EEG, and spontaneous facial expressions were investigated in response to both image-content stimuli and video-content stimuli. We propose a method to jointly evaluate fNIRS and EEG signals for affective state detection (emotional valence as positive or negative). Experimental results reveal a strong correlation between spontaneous facial affective expressions and the perceived emotional valence. Moreover, the affective states were estimated by the fNIRS, EEG, and fNIRS + EEG brain activity measurements. We show that the proposed EEG + fNIRS hybrid method outperforms fNIRS-only and EEG-only approaches. Our findings indicate that the dynamic (video-content based) stimuli triggers a larger affective response than the static (image-content based) stimuli. These findings also suggest joint utilization of facial expression and wearable neuroimaging, fNIRS, and EEG, for improved emotional analysis and affective brain–computer interface applications. Full article
(This article belongs to the Special Issue Brain Plasticity, Cognitive Training and Mental States Assessment)
Show Figures

Figure 1

Article
Neurophysiological Vigilance Characterisation and Assessment: Laboratory and Realistic Validations Involving Professional Air Traffic Controllers
Brain Sci. 2020, 10(1), 48; https://doi.org/10.3390/brainsci10010048 - 15 Jan 2020
Cited by 7 | Viewed by 1349
Abstract
Vigilance degradation usually causes significant performance decrement. It is also considered the major factor causing the out-of-the-loop phenomenon (OOTL) occurrence. OOTL is strongly related to a high level of automation in operative contexts such as the Air Traffic Management (ATM), and it could [...] Read more.
Vigilance degradation usually causes significant performance decrement. It is also considered the major factor causing the out-of-the-loop phenomenon (OOTL) occurrence. OOTL is strongly related to a high level of automation in operative contexts such as the Air Traffic Management (ATM), and it could lead to a negative impact on the Air Traffic Controllers’ (ATCOs) engagement. As a consequence, being able to monitor the ATCOs’ vigilance would be very important to prevent risky situations. In this context, the present study aimed to characterise and assess the vigilance level by using electroencephalographic (EEG) measures. The first study, involving 13 participants in laboratory settings allowed to find out the neurophysiological features mostly related to vigilance decrements. Those results were also confirmed under realistic ATM settings recruiting 10 professional ATCOs. The results demonstrated that (i) there was a significant performance decrement related to vigilance reduction; (ii) there were no substantial differences between the identified neurophysiological features in controlled and ecological settings, and the EEG-channel configuration defined in laboratory was able to discriminate and classify vigilance changes in ATCOs’ vigilance with high accuracy (up to 84%); (iii) the derived two EEG-channel configuration was able to assess vigilance variations reporting only slight accuracy reduction. Full article
(This article belongs to the Special Issue Brain Plasticity, Cognitive Training and Mental States Assessment)
Show Figures

Figure 1

Article
On Assessing Driver Awareness of Situational Criticalities: Multi-modal Bio-Sensing and Vision-Based Analysis, Evaluations, and Insights
Brain Sci. 2020, 10(1), 46; https://doi.org/10.3390/brainsci10010046 - 15 Jan 2020
Viewed by 1182
Abstract
Automobiles for our roadways are increasingly using advanced driver assistance systems. The adoption of such new technologies requires us to develop novel perception systems not only for accurately understanding the situational context of these vehicles, but also to infer the driver’s awareness in [...] Read more.
Automobiles for our roadways are increasingly using advanced driver assistance systems. The adoption of such new technologies requires us to develop novel perception systems not only for accurately understanding the situational context of these vehicles, but also to infer the driver’s awareness in differentiating between safe and critical situations. This manuscript focuses on the specific problem of inferring driver awareness in the context of attention analysis and hazardous incident activity. Even after the development of wearable and compact multi-modal bio-sensing systems in recent years, their application in driver awareness context has been scarcely explored. The capability of simultaneously recording different kinds of bio-sensing data in addition to traditionally employed computer vision systems provides exciting opportunities to explore the limitations of these sensor modalities. In this work, we explore the applications of three different bio-sensing modalities namely electroencephalogram (EEG), photoplethysmogram (PPG) and galvanic skin response (GSR) along with a camera-based vision system in driver awareness context. We assess the information from these sensors independently and together using both signal processing- and deep learning-based tools. We show that our methods outperform previously reported studies to classify driver attention and detecting hazardous/non-hazardous situations for short time scales of two seconds. We use EEG and vision data for high resolution temporal classification (two seconds) while additionally also employing PPG and GSR over longer time periods. We evaluate our methods by collecting user data on twelve subjects for two real-world driving datasets among which one is publicly available (KITTI dataset) while the other was collected by us (LISA dataset) with the vehicle being driven in an autonomous mode. This work presents an exhaustive evaluation of multiple sensor modalities on two different datasets for attention monitoring and hazardous events classification. Full article
(This article belongs to the Special Issue Brain Plasticity, Cognitive Training and Mental States Assessment)
Show Figures

Figure 1

Article
A Hybrid Feature Pool-Based Emotional Stress State Detection Algorithm Using EEG Signals
Brain Sci. 2019, 9(12), 376; https://doi.org/10.3390/brainsci9120376 - 13 Dec 2019
Cited by 6 | Viewed by 1641
Abstract
Human stress analysis using electroencephalogram (EEG) signals requires a detailed and domain-specific information pool to develop an effective machine learning model. In this study, a multi-domain hybrid feature pool is designed to identify most of the important information from the signal. The hybrid [...] Read more.
Human stress analysis using electroencephalogram (EEG) signals requires a detailed and domain-specific information pool to develop an effective machine learning model. In this study, a multi-domain hybrid feature pool is designed to identify most of the important information from the signal. The hybrid feature pool contains features from two types of analysis: (a) statistical parametric analysis from the time domain, and (b) wavelet-based bandwidth specific feature analysis from the time-frequency domain. Then, a wrapper-based feature selector, Boruta, is applied for ranking all the relevant features from that feature pool instead of considering only the non-redundant features. Finally, the k-nearest neighbor (k-NN) algorithm is used for final classification. The proposed model yields an overall accuracy of 73.38% for the total considered dataset. To validate the performance of the proposed model and highlight the necessity of designing a hybrid feature pool, the model was compared to non-linear dimensionality reduction techniques, as well as those without feature ranking. Full article
(This article belongs to the Special Issue Brain Plasticity, Cognitive Training and Mental States Assessment)
Show Figures

Figure 1

Article
EEG Emotion Classification Using an Improved SincNet-Based Deep Learning Model
Brain Sci. 2019, 9(11), 326; https://doi.org/10.3390/brainsci9110326 - 14 Nov 2019
Cited by 10 | Viewed by 2222
Abstract
Deep learning (DL) methods have been used increasingly widely, such as in the fields of speech and image recognition. However, how to design an appropriate DL model to accurately and efficiently classify electroencephalogram (EEG) signals is still a challenge, mainly because EEG signals [...] Read more.
Deep learning (DL) methods have been used increasingly widely, such as in the fields of speech and image recognition. However, how to design an appropriate DL model to accurately and efficiently classify electroencephalogram (EEG) signals is still a challenge, mainly because EEG signals are characterized by significant differences between two different subjects or vary over time within a single subject, non-stability, strong randomness, low signal-to-noise ratio. SincNet is an efficient classifier for speaker recognition, but it has some drawbacks in dealing with EEG signals classification. In this paper, we improve and propose a SincNet-based classifier, SincNet-R, which consists of three convolutional layers, and three deep neural network (DNN) layers. We then make use of SincNet-R to test the classification accuracy and robustness by emotional EEG signals. The comparable results with original SincNet model and other traditional classifiers such as CNN, LSTM and SVM, show that our proposed SincNet-R model has higher classification accuracy and better algorithm robustness. Full article
(This article belongs to the Special Issue Brain Plasticity, Cognitive Training and Mental States Assessment)
Show Figures

Figure 1

Back to TopTop