Special Issue "Brain-Computer Interfaces: Current Trends and Novel Applications"

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

Deadline for manuscript submissions: closed (31 July 2016).

Special Issue Editor

Dr. Vaibhav Gandhi
E-Mail Website
Guest Editor
School of Science & Technology, Middlesex University, London, UK
Interests: brain-computer interfaces; machine learning; use-centric adaptive graphical user interfaces, human-machine interaction

Special Issue Information

Dear Colleagues,

Verbal or non-verbal information exchange is the basis of human communication. However, millions of people worldwide lose this fundamental ability of communication because of accidents or inherited neuromuscular disorders, leading to restrictions in even the basic communication needs. A Brain-Computer Interface (BCI), which is an electroencephalography (EEG) based communication, can enable such physically challenged people to achieve greater independence by making technology accessible. BCI emerged few decades ago, and is now maturing towards being more realistic and practically plausible. The main goal of this Special Issue is to show the latest advances in BCIs, including, but not limited to, neurotechnologies, tele-services, development of communication infrastructure to support practical BCIs, signal processing algorithms, graphical interfaces, and novel paradigms.

Vaibhav Gandhi
Guest Editor

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 1400 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

I invite potential contributors to this Special Issue to submit their original research and review articles on the topics which include, but are not limited to:

  • brain-computer interfaces (BCIs)
  • novel feedback paradigms
  • evolutionary computation
  • BCIs for independent living
  • clinical applications
  • communication infrastructure for practical BCIs
  • hybrid BCIs
  • collaborative BCIs
  • performance metrics in BCIs

Published Papers (4 papers)

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Research

Open AccessArticle
A Novel Hybrid Mental Spelling Application Based on Eye Tracking and SSVEP-Based BCI
Brain Sci. 2017, 7(4), 35; https://doi.org/10.3390/brainsci7040035 - 05 Apr 2017
Cited by 23
Abstract
Steady state visual evoked potentials (SSVEPs)-based Brain-Computer interfaces (BCIs), as well as eyetracking devices, provide a pathway for re-establishing communication for people with severe disabilities. We fused these control techniques into a novel eyetracking/SSVEP hybrid system, which utilizes eye tracking for initial rough [...] Read more.
Steady state visual evoked potentials (SSVEPs)-based Brain-Computer interfaces (BCIs), as well as eyetracking devices, provide a pathway for re-establishing communication for people with severe disabilities. We fused these control techniques into a novel eyetracking/SSVEP hybrid system, which utilizes eye tracking for initial rough selection and the SSVEP technology for fine target activation. Based on our previous studies, only four stimuli were used for the SSVEP aspect, granting sufficient control for most BCI users. As Eye tracking data is not used for activation of letters, false positives due to inappropriate dwell times are avoided. This novel approach combines the high speed of eye tracking systems and the high classification accuracies of low target SSVEP-based BCIs, leading to an optimal combination of both methods. We evaluated accuracy and speed of the proposed hybrid system with a 30-target spelling application implementing all three control approaches (pure eye tracking, SSVEP and the hybrid system) with 32 participants. Although the highest information transfer rates (ITRs) were achieved with pure eye tracking, a considerable amount of subjects was not able to gain sufficient control over the stand-alone eye-tracking device or the pure SSVEP system (78.13% and 75% of the participants reached reliable control, respectively). In this respect, the proposed hybrid was most universal (over 90% of users achieved reliable control), and outperformed the pure SSVEP system in terms of speed and user friendliness. The presented hybrid system might offer communication to a wider range of users in comparison to the standard techniques. Full article
(This article belongs to the Special Issue Brain-Computer Interfaces: Current Trends and Novel Applications)
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Open AccessArticle
A Genetic-Based Feature Selection Approach in the Identification of Left/Right Hand Motor Imagery for a Brain-Computer Interface
Brain Sci. 2017, 7(1), 12; https://doi.org/10.3390/brainsci7010012 - 23 Jan 2017
Cited by 9
Abstract
Electroencephalography is a non-invasive measure of the brain electrical activity generated by millions of neurons. Feature extraction in electroencephalography analysis is a core issue that may lead to accurate brain mental state classification. This paper presents a new feature selection method that improves [...] Read more.
Electroencephalography is a non-invasive measure of the brain electrical activity generated by millions of neurons. Feature extraction in electroencephalography analysis is a core issue that may lead to accurate brain mental state classification. This paper presents a new feature selection method that improves left/right hand movement identification of a motor imagery brain-computer interface, based on genetic algorithms and artificial neural networks used as classifiers. Raw electroencephalography signals are first preprocessed using appropriate filtering. Feature extraction is carried out afterwards, based on spectral and temporal signal components, and thus a feature vector is constructed. As various features might be inaccurate and mislead the classifier, thus degrading the overall system performance, the proposed approach identifies a subset of features from a large feature space, such that the classifier error rate is reduced. Experimental results show that the proposed method is able to reduce the number of features to as low as 0.5% (i.e., the number of ignored features can reach 99.5%) while improving the accuracy, sensitivity, specificity, and precision of the classifier. Full article
(This article belongs to the Special Issue Brain-Computer Interfaces: Current Trends and Novel Applications)
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Open AccessArticle
The Cluster Variation Method: A Primer for Neuroscientists
Brain Sci. 2016, 6(4), 44; https://doi.org/10.3390/brainsci6040044 - 30 Sep 2016
Cited by 2
Abstract
Effective Brain–Computer Interfaces (BCIs) require that the time-varying activation patterns of 2-D neural ensembles be modelled. The cluster variation method (CVM) offers a means for the characterization of 2-D local pattern distributions. This paper provides neuroscientists and BCI researchers with a CVM tutorial [...] Read more.
Effective Brain–Computer Interfaces (BCIs) require that the time-varying activation patterns of 2-D neural ensembles be modelled. The cluster variation method (CVM) offers a means for the characterization of 2-D local pattern distributions. This paper provides neuroscientists and BCI researchers with a CVM tutorial that will help them to understand how the CVM statistical thermodynamics formulation can model 2-D pattern distributions expressing structural and functional dynamics in the brain. The premise is that local-in-time free energy minimization works alongside neural connectivity adaptation, supporting the development and stabilization of consistent stimulus-specific responsive activation patterns. The equilibrium distribution of local patterns, or configuration variables, is defined in terms of a single interaction enthalpy parameter (h) for the case of an equiprobable distribution of bistate (neural/neural ensemble) units. Thus, either one enthalpy parameter (or two, for the case of non-equiprobable distribution) yields equilibrium configuration variable values. Modeling 2-D neural activation distribution patterns with the representational layer of a computational engine, we can thus correlate variational free energy minimization with specific configuration variable distributions. The CVM triplet configuration variables also map well to the notion of a M = 3 functional motif. This paper addresses the special case of an equiprobable unit distribution, for which an analytic solution can be found. Full article
(This article belongs to the Special Issue Brain-Computer Interfaces: Current Trends and Novel Applications)
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Open AccessArticle
Three-Class EEG-Based Motor Imagery Classification Using Phase-Space Reconstruction Technique
Brain Sci. 2016, 6(3), 36; https://doi.org/10.3390/brainsci6030036 - 23 Aug 2016
Cited by 20
Abstract
Over the last few decades, brain signals have been significantly exploited for brain-computer interface (BCI) applications. In this paper, we study the extraction of features using event-related desynchronization/synchronization techniques to improve the classification accuracy for three-class motor imagery (MI) BCI. The classification approach [...] Read more.
Over the last few decades, brain signals have been significantly exploited for brain-computer interface (BCI) applications. In this paper, we study the extraction of features using event-related desynchronization/synchronization techniques to improve the classification accuracy for three-class motor imagery (MI) BCI. The classification approach is based on combining the features of the phase and amplitude of the brain signals using fast Fourier transform (FFT) and autoregressive (AR) modeling of the reconstructed phase space as well as the modification of the BCI parameters (trial length, trial frequency band, classification method). We report interesting results compared with those present in the literature by utilizing sequential forward floating selection (SFFS) and a multi-class linear discriminant analysis (LDA), our findings showed superior classification results, a classification accuracy of 86.06% and 93% for two BCI competition datasets, with respect to results from previous studies. Full article
(This article belongs to the Special Issue Brain-Computer Interfaces: Current Trends and Novel Applications)
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