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Brain Sciences
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Advance Research of Neurodynamics to Enhance Noninvasive Brain-Computer Interface and...
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Advance Research of Neurodynamics to Enhance Noninvasive Brain-Computer Interface and Its Applications

A special issue of Brain Sciences (ISSN 2076-3425). This special issue belongs to the section "Neural Engineering, Neuroergonomics and Neurorobotics".

Deadline for manuscript submissions: closed (17 September 2021) | Viewed by 7954

Special Issue Editors

Dr. Camillo Porcaro

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Guest Editor
Institute of Cognitive Sciences and Technologies (ISTC) - National Research Council (CNR), Rome, Italy; Department of Information Engineering, Università Politecnica delle Marche, Ancona, Italy
Interests: brain–computer interface (BCI); blind source separation (BSS); fractal analysis; resting state networks (RSN); neuroimaging and electrophysiology
Dr. Sabrina Iarlori

E-Mail Website
Guest Editor
Department of Information Engineering, Università Politecnica delle Marche, Ancona, Italy
Interests: movement analysis and assessment; brain–computer interface; human–robot interaction and cooperation; assistive technologies; machine learning analysis
Dr. Francesco Ferracuti

E-Mail Website
Guest Editor
Department of Information Engineering, Università Politecnica delle Marche, Via Brecce Bianche, 60131 Ancona, Italy
Interests: fault detection and diagnosis; system identification; signal processing
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Andrea Monteriù

E-Mail Website
Guest Editor
Department of Information Engineering, Università Politecnica delle Marche, Via Brecce Bianche, 60131 Ancona, Italy
Interests: intelligent household; Internet of Things; interoperability; smart monitoring; smart living; safety; Security; Energy Management; home automation; smart sensors; wireless sensor networks; machine learning; home appliances and devices; digital twin; cyber-physical system; risk assessment; operators’ safety
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Brain–computer Interfaces (BCIs) are systems that establish a direct communication pathway between users’ brain activity and external effectors. The main goal of BCI is to replace or restore useful function to people disabled by neuromuscular disorders such as amyotrophic lateral sclerosis, cerebral palsy, stroke, or spinal cord injury. In recent years, BCI has also proven useful for rehabilitation after stroke and for other disorders. Despite the advancement obtained in the last decade, we still are far from a clear control of the machinery by means of brain intention. To achieve this goal, we need to progress on signal-acquisition hardware that is convenient, portable, safe, and able to function in all environments. Moreover, we need to increase efficiency in detecting brain signal intention and translate it into efficient commands for human–machine applications. In this respect, linear methods that are predominantly used in characterizing brain neurodynamics in both healthy and pathological conditions may not be suitable to describe the irregular and non-periodic patterns recorded by electrophysiological and neuroimaging techniques. Brain neurodynamics contain “hidden information” that might be captured by nonlinear methods, and thus, they may provide crucial and so far overlooked physiological information that may push BCI systems forward. 

The aim of the present Special Issue is to provide a general overview of recent linear and nonlinear signal processing and human–machine interaction supported by brain–computer interfaces. In particular, BCI systems benefit users if the decoded actions reflect the users’ intentions with an accuracy that enables them to efficiently interact with their environment. This interaction can also be related to advanced systems in an ambient assisted living scenario, when the human–system interaction requests user supervision to control autonomous systems.

Cutting-edge research topics can include: i) advanced linear and nonlinear signal processing; ii) deep neural networks; and iii) human–machine interaction or cooperation through BCI application.

This Special Issue will cover recent advances in –computer interface (BCI) and brain neurodynamics data analysis by linear and nonlinear methods and its application in human–machine interactions. We encourage submissions of original research and reviews with a focus on new approaches able to effectively enhance the quality of life of BCI users with both neurophysiological recordings (e.g., EEG and MEG) and neuroimaging techniques (e.g., fMRI and fNIRS).

Potential topics include but are not limited to the following:

  • EEG pattern data analysis;
  • EEG and evoked potential recognition;
  • Feature selection for EEG classification;
  • Linear and nonlinear classifier for EEG signals;
  • Brain–computer interface
  • Hybrid brain–computer interface;
  • Human–machine interaction;
  • BCI control applications.

Dr. Camillo Porcaro
Dr. Sabrina Iarlori
Dr. Francesco Ferracuti
Dr. Andrea Monteriù
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 submissions that pass pre-check are 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 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

  • Electroencephalography (EEG)
  • Magnetoencephalography (MEG)
  • Functional magnetic resonance imaging (fMRI)
  • Functional near-infrared spectroscopy (fNIRS)
  • Blind source separation (BSS)
  • Fractal dimension
  • neurological disorders
  • Brain–computer interface (BCI)
  • Hybrid brain–computer interface
  • Human–machine interaction
  • Artificial neural network (ANN)

Published Papers (3 papers)

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Research

16 pages, 2971 KiB  
Article
Comparing between Different Sets of Preprocessing, Classifiers, and Channels Selection Techniques to Optimise Motor Imagery Pattern Classification System from EEG Pattern Recognition
by Francesco Ferracuti, Sabrina Iarlori, Zahra Mansour, Andrea Monteriù and Camillo Porcaro
Brain Sci. 2022, 12(1), 57; https://doi.org/10.3390/brainsci12010057 - 31 Dec 2021
Cited by 7 | Viewed by 2467
Abstract
The ability to control external devices through thought is increasingly becoming a reality. Human beings can use the electrical signals of their brain to interact or change the surrounding environment and more. The development of this technology called brain-computer interface (BCI) will increasingly [...] Read more.
The ability to control external devices through thought is increasingly becoming a reality. Human beings can use the electrical signals of their brain to interact or change the surrounding environment and more. The development of this technology called brain-computer interface (BCI) will increasingly allow people with motor disabilities to communicate or use assistive devices to walk, manipulate objects and communicate. Using data from the PhysioNet database, this study implemented a pattern classification system for use in a BCI on 109 healthy volunteers during real movement activities and motor imagery recorded by 64-channels electroencephalography (EEG) system. Different classifiers such as Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Decision Trees (TREE) were applied on different combinations of EEG channels. Starting from two channels (C3, C4 and CP3 and CP4) positioned on the contralateral and ipsilateral sensorimotor cortex, the Region of Interest (RoI) centred on C3/Cp3 and C4/Cp4 and, finally, a data-driven automatic channels selection was tested to explore the best channel combination able to increase the classification accuracy. The results showed that the proposed automatic channels selection was able to significantly improve the performance of each classifier achieving 98% of accuracy for classification of real and imagined hand movement (sensitivity = 97%, specificity = 99%, AUC = 0.99) by SVM. While the accuracy of the classification between the imagery of hand and foot movements was 91% (sensitivity = 87%, specificity = 86%, AUC = 0.93) also with SVM. In the proposed approach, the data-driven automatic channels selection outperforms classical a priori channel selection models such as C3/C4, Cp3/Cp4, or RoIs around those channels with the utmost accuracy to help remove the boundaries of human communication and improve the quality of life of people with disabilities. Full article
(This article belongs to the Special Issue Advance Research of Neurodynamics to Enhance Noninvasive Brain-Computer Interface and Its Applications)
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18 pages, 3466 KiB  
Article
An Optimal Transport Based Transferable System for Detection of Erroneous Somato-Sensory Feedback from Neural Signals
by Saugat Bhattacharyya and Mitsuhiro Hayashibe
Brain Sci. 2021, 11(11), 1393; https://doi.org/10.3390/brainsci11111393 - 23 Oct 2021
Cited by 1 | Viewed by 1853
Abstract
This study is aimed at the detection of single-trial feedback, perceived as erroneous by the user, using a transferable classification system while conducting a motor imagery brain–computer interfacing (BCI) task. The feedback received by the users are relayed from a functional electrical stimulation [...] Read more.
This study is aimed at the detection of single-trial feedback, perceived as erroneous by the user, using a transferable classification system while conducting a motor imagery brain–computer interfacing (BCI) task. The feedback received by the users are relayed from a functional electrical stimulation (FES) device and hence are somato-sensory in nature. The BCI system designed for this study activates an electrical stimulator placed on the left hand, right hand, left foot, and right foot of the user. Trials containing erroneous feedback can be detected from the neural signals in form of the error related potential (ErrP). The inclusion of neuro-feedback during the experiments indicated the possibility that ErrP signals can be evoked when the participant perceives an error from the feedback. Hence, to detect such feedback using ErrP, a transferable (offline) decoder based on optimal transport theory is introduced herein. The offline system detects single-trial erroneous trials from the feedback period of an online neuro-feedback BCI system. The results of the FES-based feedback BCI system were compared to a similar visual-based (VIS) feedback system. Using our framework, the error detector systems for both the FES and VIS feedback paradigms achieved an F1-score of 92.66% and 83.10%, respectively, and are significantly superior to a comparative system where an optimal transport was not used. It is expected that this form of transferable and automated error detection system compounded with a motor imagery system will augment the performance of a BCI and provide a better BCI-based neuro-rehabilitation protocol that has an error control mechanism embedded into it. Full article
(This article belongs to the Special Issue Advance Research of Neurodynamics to Enhance Noninvasive Brain-Computer Interface and Its Applications)
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24 pages, 5747 KiB  
Article
A Densely Connected Multi-Branch 3D Convolutional Neural Network for Motor Imagery EEG Decoding
by Tianjun Liu and Deling Yang
Brain Sci. 2021, 11(2), 197; https://doi.org/10.3390/brainsci11020197 - 5 Feb 2021
Cited by 31 | Viewed by 2817
Abstract
Motor imagery (MI) is a classical method of brain–computer interaction (BCI), in which electroencephalogram (EEG) signal features evoked by imaginary body movements are recognized, and relevant information is extracted. Recently, various deep-learning methods are being focused on in finding an easy-to-use EEG representation [...] Read more.
Motor imagery (MI) is a classical method of brain–computer interaction (BCI), in which electroencephalogram (EEG) signal features evoked by imaginary body movements are recognized, and relevant information is extracted. Recently, various deep-learning methods are being focused on in finding an easy-to-use EEG representation method that can preserve both temporal information and spatial information. To further utilize the spatial and temporal features of EEG signals, an improved 3D representation of the EEG and a densely connected multi-branch 3D convolutional neural network (dense M3D CNN) for MI classification are introduced in this paper. Specifically, as compared to the original 3D representation, a new padding method is proposed to pad the points without electrodes with the mean of all the EEG signals. Based on this new 3D presentation, a densely connected multi-branch 3D CNN with a novel dense connectivity is proposed for extracting the EEG signal features. Experiments were carried out on the WAY-EEG-GAL and BCI competition IV 2a datasets to verify the performance of this proposed method. The experimental results show that the proposed framework achieves a state-of-the-art performance that significantly outperforms the multi-branch 3D CNN framework, with a 6.208% improvement in the average accuracy for the BCI competition IV 2a datasets and 6.281% improvement in the average accuracy for the WAY-EEG-GAL datasets, with a smaller standard deviation. The results also prove the effectiveness and robustness of the method, along with validating its use in MI-classification tasks. Full article
(This article belongs to the Special Issue Advance Research of Neurodynamics to Enhance Noninvasive Brain-Computer Interface and Its Applications)
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