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).

Printed Edition Available!
A printed edition of this Special Issue is available here.

Special Issue Editors

Prof. Riccardo Poli
E-Mail Website
Guest Editor
Brain-Computer Interfaces and Neural Engineering Laboratory, School of Computer Science and Electonic Engineering, University of Essex, Wivenhoe Park, Colchester, CO4 3SQ, UK
Interests: brain–computer interfaces; evolutionary algorithms; machine learning; artificial intelligence
Dr. Davide Valeriani
E-Mail Website
Guest Editor
Brain-Computer Interfaces and Neural Engineering Laboratory, School of Computer Science and Electonic Engineering, University of Essex, Wivenhoe Park, Colchester, CO4 3SQ, UK
Interests: brain–computer interfaces; decision making; machine learning; artificial intelligence
Dr. Caterina Cinel
E-Mail Website1 Website2
Guest Editor
Brain-Computer Interfaces and Neural Engineering Laboratory, School of Computer Science and Electonic Engineering, University of Essex, Wivenhoe Park, Colchester, CO4 3SQ, UK
Interests: multisensory integration; visual feature integration; attention; EEG; brain-computer interfaces; decision making; transcranial current stimulation; autobiographical memory
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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

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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.

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Keywords

  • Brain-Computer Interfaces (BCIs)
  • Collaborative BCIs
  • Hybrid BCIs
  • Neuroscience
  • Brain stimulation
  • Human augmentation

Published Papers (6 papers)

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Editorial

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Open AccessEditorial
Brain–Computer Interfaces for Human Augmentation
Brain Sci. 2019, 9(2), 22; https://doi.org/10.3390/brainsci9020022 - 24 Jan 2019
Cited by 1
Abstract
The field of brain–computer interfaces (BCIs) has grown rapidly in the last few decades, allowing the development of ever faster and more reliable assistive technologies for converting brain activity into control signals for external devices for people with severe disabilities [...] Full article

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 - 16 Nov 2018
Cited by 4
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
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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 - 31 Aug 2018
Cited by 3
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 [...] Read more.
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
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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 - 02 Jul 2018
Cited by 2
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
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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 - 23 Apr 2018
Cited by 3
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 [...] Read more.
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
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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 - 30 Mar 2018
Cited by 45
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
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