Special Issue "Emergence of Novel Brain-Computer Interface Applications"

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

Deadline for manuscript submissions: closed (31 October 2013)

Special Issue Editors

Guest Editor
Dr. Francisco Sepulveda

School of Computer Science and Electronic Engineering, University of Essex, Wivenhoe Park, Colchester, Essex CO4 3SQ, UK
Website | E-Mail
Fax: +44 1206 872788
Interests: machine learning; human-machine interaction; affective computing; brain-computer interfaces; biomedical pattern recognition; computational neuroscience
Guest Editor
Dr. Damien Coyle

School of Computing and Intelligent Systems, Faculty of Computing and Engineering, University of Ulster, Northern Ireland BT48 J7L, UK
Website | E-Mail

Special Issue Information

Dear Colleagues,

Until the past decade, work in the burgeoning field of brain-computer interfaces (BCIs) was largely focused on feasibility studies, information transfer rate, and single user scenarios. As the field matures, it is growing to encompass various approaches and applications that transcend the original paradigms. BCIs have slowly but steadily moved outside strictly controlled laboratory conditions with P300, SSVEP, motor imagery — amongst other now well established methods — and into more realistic yet more complex areas such as neurorehabilitation and games.

This is an exciting time in BCI research, development and applications. As new areas and applications merge and emerge with BCIs at their core, some will inevitably be discarded while others will flourish. It is now timely to take stock of the most recent transitions in BCI applications and feedback paradigms and to provide the BCI research community with coverage of what might become more mainstream in the near and distant future. A special issue on the Emergence of Novel BCI Applications is therefore warranted. To this end, we invite authors to submit original research papers in BCIs and BCI-related areas, with an emphasis on novel and/or emerging techniques and applications.

Topics of interest include, but are not limited to:
• neurolinguistic BCIs
• clinical applications
• multidimensional control
• novel feedback paradigms
• exogenous/Endogenous Auditory BCI
• BCIs for independent living
• novel cognitive states
• long term BCI use
• BCIs and learning/plasticity
• machine-to-human BCIs
• collaborative (multi-user) BCIs


Dr. Francisco Sepulveda
Dr. Damien Coyle
Guest Editors

Submission

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. Papers will be published continuously (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as 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 refereed through a 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 quarterly 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 600 CHF (Swiss Francs). English correction and/or formatting fees of 250 CHF (Swiss Francs) will be charged in certain cases for those articles accepted for publication that require extensive additional formatting and/or English corrections.

Keywords

  • brain-computer interfaces
  • neurolinguistic BCIs
  • clinical applications
  • multidimensional control
  • novel feedback paradigms
  • exogenous/Endogenous Auditory BCI
  • BCIs for independent living
  • novel cognitive states
  • long term BCI use
  • BCIs and learning/plasticity
  • machine-to-human BCIs
  • collaborative BCIs

Published Papers (4 papers)

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Research

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Open AccessArticle Toward a New Application of Real-Time Electrophysiology: Online Optimization of Cognitive Neurosciences Hypothesis Testing
Brain Sci. 2014, 4(1), 49-72; doi:10.3390/brainsci4010049
Received: 1 November 2013 / Revised: 16 December 2013 / Accepted: 10 January 2014 / Published: 23 January 2014
Cited by 3 | PDF Full-text (1353 KB) | HTML Full-text | XML Full-text
Abstract
Brain-computer interfaces (BCIs) mostly rely on electrophysiological brain signals. Methodological and technical progress has largely solved the challenge of processing these signals online. The main issue that remains, however, is the identification of a reliable mapping between electrophysiological measures and relevant states of
[...] Read more.
Brain-computer interfaces (BCIs) mostly rely on electrophysiological brain signals. Methodological and technical progress has largely solved the challenge of processing these signals online. The main issue that remains, however, is the identification of a reliable mapping between electrophysiological measures and relevant states of mind. This is why BCIs are highly dependent upon advances in cognitive neuroscience and neuroimaging research. Recently, psychological theories became more biologically plausible, leading to more realistic generative models of psychophysiological observations. Such complex interpretations of empirical data call for efficient and robust computational approaches that can deal with statistical model comparison, such as approximate Bayesian inference schemes. Importantly, the latter enable the optimization of a model selection error rate with respect to experimental control variables, yielding maximally powerful designs. In this paper, we use a Bayesian decision theoretic approach to cast model comparison in an online adaptive design optimization procedure. We show how to maximize design efficiency for individual healthy subjects or patients. Using simulated data, we demonstrate the face- and construct-validity of this approach and illustrate its extension to electrophysiology and multiple hypothesis testing based on recent psychophysiological models of perception. Finally, we discuss its implications for basic neuroscience and BCI itself. Full article
(This article belongs to the Special Issue Emergence of Novel Brain-Computer Interface Applications)
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Open AccessArticle A Brain-Computer-Interface for the Detection and Modulation of Gamma Band Activity
Brain Sci. 2013, 3(4), 1569-1587; doi:10.3390/brainsci3041569
Received: 19 August 2013 / Revised: 6 November 2013 / Accepted: 8 November 2013 / Published: 18 November 2013
Cited by 2 | PDF Full-text (936 KB) | HTML Full-text | XML Full-text
Abstract
Gamma band oscillations in the human brain (around 40 Hz) play a functional role in information processing, and a real-time assessment of gamma band activity could be used to evaluate the functional relevance more directly. Therefore, we developed a source based Brain-Computer-Interface (BCI)
[...] Read more.
Gamma band oscillations in the human brain (around 40 Hz) play a functional role in information processing, and a real-time assessment of gamma band activity could be used to evaluate the functional relevance more directly. Therefore, we developed a source based Brain-Computer-Interface (BCI) with an online detection of gamma band activity in a selective brain region in the visual cortex. The BCI incorporates modules for online detection of various artifacts (including microsaccades) and the artifacts were continuously fed back to the volunteer. We examined the efficiency of the source-based BCI for Neurofeedback training of gamma- and alpha-band (8–12 Hz) oscillations and compared the specificity for the spatial and frequency domain. Our results demonstrated that volunteers learned to selectively switch between modulating alpha- or gamma-band oscillations and benefited from online artifact information. The analyses revealed a high level of accuracy with respect to frequency and topography for the gamma-band modulations. Thus, the developed BCI can be used to manipulate the fast oscillatory activity with a high level of specificity. These selective modulations can be used to assess the relevance of fast neural oscillations for information processing in a more direct way, i.e., by the adaptive presentation of stimuli within well-described brain states. Full article
(This article belongs to the Special Issue Emergence of Novel Brain-Computer Interface Applications)

Review

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Open AccessReview Towards Effective Non-Invasive Brain-Computer Interfaces Dedicated to Gait Rehabilitation Systems
Brain Sci. 2014, 4(1), 1-48; doi:10.3390/brainsci4010001
Received: 2 October 2013 / Revised: 5 November 2013 / Accepted: 12 December 2013 / Published: 31 December 2013
Cited by 6 | PDF Full-text (3571 KB) | HTML Full-text | XML Full-text
Abstract
In the last few years, significant progress has been made in the field of walk rehabilitation. Motor cortex signals in bipedal monkeys have been interpreted to predict walk kinematics. Epidural electrical stimulation in rats and in one young paraplegic has been realized to
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In the last few years, significant progress has been made in the field of walk rehabilitation. Motor cortex signals in bipedal monkeys have been interpreted to predict walk kinematics. Epidural electrical stimulation in rats and in one young paraplegic has been realized to partially restore motor control after spinal cord injury. However, these experimental trials are far from being applicable to all patients suffering from motor impairments. Therefore, it is thought that more simple rehabilitation systems are desirable in the meanwhile. The goal of this review is to describe and summarize the progress made in the development of non-invasive brain-computer interfaces dedicated to motor rehabilitation systems. In the first part, the main principles of human locomotion control are presented. The paper then focuses on the mechanisms of supra-spinal centers active during gait, including results from electroencephalography, functional brain imaging technologies [near-infrared spectroscopy (NIRS), functional magnetic resonance imaging (fMRI), positron-emission tomography (PET), single-photon emission-computed tomography (SPECT)] and invasive studies. The first brain-computer interface (BCI) applications to gait rehabilitation are then presented, with a discussion about the different strategies developed in the field. The challenges to raise for future systems are identified and discussed. Finally, we present some proposals to address these challenges, in order to contribute to the improvement of BCI for gait rehabilitation. Full article
(This article belongs to the Special Issue Emergence of Novel Brain-Computer Interface Applications)

Other

Jump to: Research, Review

Open AccessOpinion Patient Machine Interface for the Control of Mechanical Ventilation Devices
Brain Sci. 2013, 3(4), 1554-1568; doi:10.3390/brainsci3041554
Received: 19 August 2013 / Revised: 12 September 2013 / Accepted: 8 November 2013 / Published: 15 November 2013
PDF Full-text (432 KB) | HTML Full-text | XML Full-text
Abstract
The potential of Brain Computer Interfaces (BCIs) to translate brain activity into commands to control external devices during mechanical ventilation (MV) remains largely unexplored. This is surprising since the amount of patients that might benefit from such assistance is considerably larger than the
[...] Read more.
The potential of Brain Computer Interfaces (BCIs) to translate brain activity into commands to control external devices during mechanical ventilation (MV) remains largely unexplored. This is surprising since the amount of patients that might benefit from such assistance is considerably larger than the number of patients requiring BCI for motor control. Given the transient nature of MV (i.e., used mainly over night or during acute clinical conditions), precluding the use of invasive methods, and inspired by current research on BCIs, we argue that scalp recorded EEG (electroencephalography) signals can provide a non-invasive direct communication pathway between the brain and the ventilator. In this paper we propose a Patient Ventilator Interface (PVI) to control a ventilator during variable conscious states (i.e., wake, sleep, etc.). After a brief introduction on the neural control of breathing and the clinical conditions requiring the use of MV we discuss the conventional techniques used during MV. The schema of the PVI is presented followed by a description of the neural signals that can be used for the on-line control. To illustrate the full approach, we present data from a healthy subject, where the inspiration and expiration periods during voluntary breathing were discriminated with a 92% accuracy (10-fold cross-validation) from the scalp EEG data. The paper ends with a discussion on the advantages and obstacles that can be forecasted in this novel application of the concept of BCI. Full article
(This article belongs to the Special Issue Emergence of Novel Brain-Computer Interface Applications)
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