Special Issue "Brain-Computer Interfaces for Human Augmentation"

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

Deadline for manuscript submissions: closed (30 September 2018)

Special Issue Editors

Guest Editor
Prof. Riccardo Poli

Brain-Computer Interfaces and Neural Engineering Laboratory, School of Computer Science and Electonic Engineering, University of Essex, Wivenhoe Park, Colchester, CO4 3SQ, UK
Website | E-Mail
Interests: brain–computer interfaces; evolutionary algorithms; machine learning; artificial intelligence
Guest Editor
Dr. Davide Valeriani

Brain-Computer Interfaces and Neural Engineering Laboratory, School of Computer Science and Electonic Engineering, University of Essex, Wivenhoe Park, Colchester, CO4 3SQ, UK
Website | E-Mail
Interests: brain–computer interfaces; decision making; machine learning; artificial intelligence
Guest Editor
Dr. Caterina Cinel

Brain-Computer Interfaces and Neural Engineering Laboratory, School of Computer Science and Electonic Engineering, University of Essex, Wivenhoe Park, Colchester, CO4 3SQ, UK
Website 1 | Website 2 | E-Mail
Interests: multisensory integration; visual feature integration; attention; EEG; brain-computer interfaces; decision making; transcranial current stimulation; autobiographical memory

Special Issue Information

Dear Colleagues,

The field of Brain–Computer Interfaces (BCIs) has grown rapidly in the last few decades, allowing the development of faster and more reliable assistive technologies based on direct links between the brain and an external device. Novel applications of BCIs have also been proposed, especially in the area of human augmentation, i.e., enabling people to go beyond human limitations in sensory, cognitive and motor tasks. Brain-imaging techniques, such as electroencephalography, have been used to extract neural correlates of various brain processes and transform them, via machine learning, into commands for external devices. Brain stimulation technology has allowed to trigger the activation of specific brain areas to enhance the cognitive processes associated to the task at hand, hence improving performance. BCIs have therefore extended their scope from assistive technologies for people with disabilities to neuro-tools for human enhancement. This Special Issue aims at showing the recent advances in BCIs for human augmentation, highlighting new results on both traditional and novel applications. These include, but are not limited to, control of external devices, communication, cognitive enhancement, decision making and entertainment. We look forward to receiving your contribution to this Special Issue.

Prof. Riccardo Poli
Dr. Davide Valeriani
Dr. Caterina Cinel
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 650 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-Computer Interfaces (BCIs)
  • Collaborative BCIs
  • Hybrid BCIs
  • Neuroscience
  • Brain stimulation
  • Human augmentation

Published Papers (5 papers)

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Research

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Open AccessArticle EEG Waveform Analysis of P300 ERP with Applications to Brain Computer Interfaces
Brain Sci. 2018, 8(11), 199; https://doi.org/10.3390/brainsci8110199
Received: 27 September 2018 / Revised: 9 November 2018 / Accepted: 13 November 2018 / Published: 16 November 2018
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Abstract
The Electroencephalography (EEG) is not just a mere clinical tool anymore. It has become the de-facto mobile, portable, non-invasive brain imaging sensor to harness brain information in real time. It is now being used to translate or decode brain signals, to diagnose diseases
[...] Read more.
The Electroencephalography (EEG) is not just a mere clinical tool anymore. It has become the de-facto mobile, portable, non-invasive brain imaging sensor to harness brain information in real time. It is now being used to translate or decode brain signals, to diagnose diseases or to implement Brain Computer Interface (BCI) devices. The automatic decoding is mainly implemented by using quantitative algorithms to detect the cloaked information buried in the signal. However, clinical EEG is based intensively on waveforms and the structure of signal plots. Hence, the purpose of this work is to establish a bridge to fill this gap by reviewing and describing the procedures that have been used to detect patterns in the electroencephalographic waveforms, benchmarking them on a controlled pseudo-real dataset of a P300-Based BCI Speller and verifying their performance on a public dataset of a BCI Competition. Full article
(This article belongs to the Special Issue Brain-Computer Interfaces for Human Augmentation)
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Open AccessArticle A Motivational Model of BCI-Controlled Heuristic Search
Brain Sci. 2018, 8(9), 166; https://doi.org/10.3390/brainsci8090166
Received: 11 June 2018 / Revised: 12 August 2018 / Accepted: 17 August 2018 / Published: 31 August 2018
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Abstract
Several researchers have proposed a new application for human augmentation, which is to provide human supervision to autonomous artificial intelligence (AI) systems. In this paper, we introduce a framework to implement this proposal, which consists of using Brain–Computer Interfaces (BCI) to influence AI
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Several researchers have proposed a new application for human augmentation, which is to provide human supervision to autonomous artificial intelligence (AI) systems. In this paper, we introduce a framework to implement this proposal, which consists of using Brain–Computer Interfaces (BCI) to influence AI computation via some of their core algorithmic components, such as heuristic search. Our framework is based on a joint analysis of philosophical proposals characterising the behaviour of autonomous AI systems and recent research in cognitive neuroscience that support the design of appropriate BCI. Our framework is defined as a motivational approach, which, on the AI side, influences the shape of the solution produced by heuristic search using a BCI motivational signal reflecting the user’s disposition towards the anticipated result. The actual mapping is based on a measure of prefrontal asymmetry, which is translated into a non-admissible variant of the heuristic function. Finally, we discuss results from a proof-of-concept experiment using functional near-infrared spectroscopy (fNIRS) to capture prefrontal asymmetry and control the progression of AI computation of traditional heuristic search problems. Full article
(This article belongs to the Special Issue Brain-Computer Interfaces for Human Augmentation)
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Open AccessArticle A 20-Questions-Based Binary Spelling Interface for Communication Systems
Brain Sci. 2018, 8(7), 126; https://doi.org/10.3390/brainsci8070126
Received: 11 June 2018 / Revised: 28 June 2018 / Accepted: 30 June 2018 / Published: 2 July 2018
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Abstract
Brain computer interfaces (BCIs) enables people with motor impairments to communicate using their brain signals by selecting letters and words from a screen. However, these spellers do not work for people in a complete locked-in state (CLIS). For these patients, a near infrared
[...] Read more.
Brain computer interfaces (BCIs) enables people with motor impairments to communicate using their brain signals by selecting letters and words from a screen. However, these spellers do not work for people in a complete locked-in state (CLIS). For these patients, a near infrared spectroscopy-based BCI has been developed, allowing them to reply to “yes”/”no” questions with a classification accuracy of 70%. Because of the non-optimal accuracy, a usual character-based speller for selecting letters or words cannot be used. In this paper, a novel spelling interface based on the popular 20-questions-game has been presented, which will allow patients to communicate using only “yes”/”no” answers, even in the presence of poor classification accuracy. The communication system is based on an artificial neural network (ANN) that estimates a statement thought by the patient asking less than 20 questions. The ANN has been tested in a web-based version with healthy participants and in offline simulations. Both results indicate that the proposed system can estimate a patient’s imagined sentence with an accuracy that varies from 40%, in the case of a “yes”/”no” classification accuracy of 70%, and up to 100% in the best case. These results show that the proposed spelling interface could allow patients in CLIS to express their own thoughts, instead of only answer to “yes”/”no” questions. Full article
(This article belongs to the Special Issue Brain-Computer Interfaces for Human Augmentation)
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Open AccessArticle Prediction of Human Performance Using Electroencephalography under Different Indoor Room Temperatures
Brain Sci. 2018, 8(4), 74; https://doi.org/10.3390/brainsci8040074
Received: 13 April 2018 / Revised: 19 April 2018 / Accepted: 19 April 2018 / Published: 23 April 2018
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Abstract
Varying indoor environmental conditions is known to affect office worker’s performance; wherein past research studies have reported the effects of unfavorable indoor temperature and air quality causing sick building syndrome (SBS) among office workers. Thus, investigating factors that can predict performance in changing
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Varying indoor environmental conditions is known to affect office worker’s performance; wherein past research studies have reported the effects of unfavorable indoor temperature and air quality causing sick building syndrome (SBS) among office workers. Thus, investigating factors that can predict performance in changing indoor environments have become a highly important research topic bearing significant impact in our society. While past research studies have attempted to determine predictors for performance, they do not provide satisfactory prediction ability. Therefore, in this preliminary study, we attempt to predict performance during office-work tasks triggered by different indoor room temperatures (22.2 °C and 30 °C) from human brain signals recorded using electroencephalography (EEG). Seven participants were recruited, from whom EEG, skin temperature, heart rate and thermal survey questionnaires were collected. Regression analyses were carried out to investigate the effectiveness of using EEG power spectral densities (PSD) as predictors of performance. Our results indicate EEG PSDs as predictors provide the highest R2 (> 0.70), that is 17 times higher than using other physiological signals as predictors and is more robust. Finally, the paper provides insight on the selected predictors based on brain activity patterns for low- and high-performance levels under different indoor-temperatures. Full article
(This article belongs to the Special Issue Brain-Computer Interfaces for Human Augmentation)
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Review

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Open AccessReview Brain–Computer Interface Spellers: A Review
Brain Sci. 2018, 8(4), 57; https://doi.org/10.3390/brainsci8040057
Received: 21 February 2018 / Revised: 16 March 2018 / Accepted: 27 March 2018 / Published: 30 March 2018
Cited by 2 | PDF Full-text (5085 KB) | HTML Full-text | XML Full-text
Abstract
A Brain–Computer Interface (BCI) provides a novel non-muscular communication method via brain signals. A BCI-speller can be considered as one of the first published BCI applications and has opened the gate for many advances in the field. Although many BCI-spellers have been developed
[...] Read more.
A Brain–Computer Interface (BCI) provides a novel non-muscular communication method via brain signals. A BCI-speller can be considered as one of the first published BCI applications and has opened the gate for many advances in the field. Although many BCI-spellers have been developed during the last few decades, to our knowledge, no reviews have described the different spellers proposed and studied in this vital field. The presented speller systems are categorized according to major BCI paradigms: P300, steady-state visual evoked potential (SSVEP), and motor imagery (MI). Different BCI paradigms require specific electroencephalogram (EEG) signal features and lead to the development of appropriate Graphical User Interfaces (GUIs). The purpose of this review is to consolidate the most successful BCI-spellers published since 2010, while mentioning some other older systems which were built explicitly for spelling purposes. We aim to assist researchers and concerned individuals in the field by illustrating the highlights of different spellers and presenting them in one review. It is almost impossible to carry out an objective comparison between different spellers, as each has its variables, parameters, and conditions. However, the gathered information and the provided taxonomy about different BCI-spellers can be helpful, as it could identify suitable systems for first-hand users, as well as opportunities of development and learning from previous studies for BCI researchers. Full article
(This article belongs to the Special Issue Brain-Computer Interfaces for Human Augmentation)
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