Brain Plasticity, Cognitive Training and Mental States Assessment: Series II

A special issue of Brain Sciences (ISSN 2076-3425). This special issue belongs to the section "Neuropsychology".

Deadline for manuscript submissions: closed (15 March 2022) | Viewed by 11578

Special Issue Editors


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Department of Computer, Control, and Management Engineering "Antonio Ruberti", “Sapienza” University of Rome, 00185 Rome, Italy
Interests: brain-computer interface; neuroscience; signal-processing; machine learning
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Spaulding Rehabilitation Hospital, Department of Physical Medicine and Rehabilitation, Harvard Medical School, Boston, MA 02115, USA
Interests: brain networks; hyperscanning; signal-processing
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Department of Molecular Medicine, Sapienza University of Rome, 00185 Rome, Italy
Interests: cognitive neuroscience; behavioural neuroscience; neuropsychology, biosignals processing; brain-computer interface; human-machine interaction; human factor; road safety
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Following the success of the first release, this Special Issue of Brain Sciences titled “Brain Plasticity, Cognitive Training and Mental States Assessment: Series II” aims to collect studies detailing the most recent advancements in the field of brain plasticity and learning research. Authors are invited to submit cutting-edge research and review articles that address a broad range of topics related to the evaluation of the capacity of the brain to change itself under clinical (e.g., patients going through rehab sessions) or working contexts (e.g., users learning new tasks). 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 the therapist–patient empathy, patients’ learning progress, variations of mental states throughout sessions, or to better tailor the training program on the basis of each patient attitude. In this regard, neuroimaging technology like EEG, fMRI, fNIRS, MEG, wearable sensors, VR, and robotic research appear to be the most appropriate means of gathering such information and directly employing it for the optimization of users and training schedules.

This Special Issue is also dedicated to new methodologies and technologies to infer objective information about brain changes and user mental state variations during training/rehabilitation sessions. Studies that may have a significant translational effect on the field of clinical services and research 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

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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. 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 2200 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • 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 (2 papers)

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20 pages, 3897 KiB  
Article
Validation of a Light EEG-Based Measure for Real-Time Stress Monitoring during Realistic Driving
by Nicolina Sciaraffa, Gianluca Di Flumeri, Daniele Germano, Andrea Giorgi, Antonio Di Florio, Gianluca Borghini, Alessia Vozzi, Vincenzo Ronca, Rodrigo Varga, Marteyn van Gasteren, Fabio Babiloni and Pietro Aricò
Brain Sci. 2022, 12(3), 304; https://doi.org/10.3390/brainsci12030304 - 24 Feb 2022
Cited by 31 | Viewed by 3589
Abstract
Driver’s stress affects decision-making and the probability of risk occurrence, and it is therefore a key factor in road safety. This suggests the need for continuous stress monitoring. This work aims at validating a stress neurophysiological measure—a Neurometric—for out-of-the-lab use obtained from lightweight [...] Read more.
Driver’s stress affects decision-making and the probability of risk occurrence, and it is therefore a key factor in road safety. This suggests the need for continuous stress monitoring. This work aims at validating a stress neurophysiological measure—a Neurometric—for out-of-the-lab use obtained from lightweight EEG relying on two wet sensors, in real-time, and without calibration. The Neurometric was tested during a multitasking experiment and validated with a realistic driving simulator. Twenty subjects participated in the experiment, and the resulting stress Neurometric was compared with the Random Forest (RF) model, calibrated by using EEG features and both intra-subject and cross-task approaches. The Neurometric was also compared with a measure based on skin conductance level (SCL), representing one of the physiological parameters investigated in the literature mostly correlated with stress variations. We found that during both multitasking and realistic driving experiments, the Neurometric was able to discriminate between low and high levels of stress with an average Area Under Curve (AUC) value higher than 0.9. Furthermore, the stress Neurometric showed higher AUC and stability than both the SCL measure and the RF calibrated with a cross-task approach. In conclusion, the Neurometric proposed in this work proved to be suitable for out-of-the-lab monitoring of stress levels. Full article
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14 pages, 1536 KiB  
Article
Cognitive Training with Neurofeedback Using NIRS Improved Cognitive Functions in Young Adults: Evidence from a Randomized Controlled Trial
by Rui Nouchi, Haruka Nouchi, Jerome Dinet and Ryuta Kawashima
Brain Sci. 2022, 12(1), 5; https://doi.org/10.3390/brainsci12010005 - 21 Dec 2021
Cited by 14 | Viewed by 7116
Abstract
(1) Background: A previous study has shown that cognitive training with neurofeedback (CT-NF) using down-regulation improves cognitive functions in young adults. Neurofeedback has two strategies for manipulating brain activity (down-regulation and upregulation). However, the benefit of CT-NF with the upregulation of cognitive functions [...] Read more.
(1) Background: A previous study has shown that cognitive training with neurofeedback (CT-NF) using down-regulation improves cognitive functions in young adults. Neurofeedback has two strategies for manipulating brain activity (down-regulation and upregulation). However, the benefit of CT-NF with the upregulation of cognitive functions is still unknown. In this study, we investigated whether the upregulation of CT-NF improves a wide range of cognitive functions compared to cognitive training alone. (2) Methods: In this double-blinded randomized control trial (RCT), 60 young adults were randomly assigned to one of three groups: CT-NF group, CT alone group, and an active control (ACT) group who played a puzzle game. Participants in the three groups used the same device (tablet PC and 2ch NIRS (near-infrared spectroscopy)) and performed the training game for 20 min every day for four weeks. We measured brain activity during training in all groups, but only CT-NFs received NF. We also measured a wide range of cognitive functions before and after the intervention period. (3) Results: The CT-NF groups showed superior beneficial effects on episodic memory, working memory, and attention compared to the CT alone and ACT groups. In addition, the CT-NF group showed an increase in brain activity during CT, which was associated with improvements in cognitive function. (4) Discussion: This study first demonstrated that the CT-NF using the upregulation strategy has beneficial effects on cognitive functions compared to the CT alone. Our results suggest that greater brain activities during CT would enhance a benefit from CT. Full article
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