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Special Issue "Brain–Computer Interfaces: Advances and Challenges"

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Biomedical Sensors".

Deadline for manuscript submissions: 28 February 2021.

Special Issue Editors

Dr. Miguel Ángel López Gordo
Guest Editor
Brain-computer Interface Lab (CITIC-UGR), Smart Wireless Applications and Technologies Group (SWAT) , Department of Signal Theory, Telematics and Communications, University of Granada, 18014 Granada, Spain
Interests: BCI; neurometry; virtual reality; bio-signal processing; neuro-devices; affective computing; brain area networks
Dr. Christian A. Morillas Gutiérrez
Guest Editor
Brain-computer Interface Lab at Research Centre for Information and Communications Technologies (CITIC-UGR) Circuits and Systems for Information Processing Group (CASIP), Department of Computer Architecture and Computer Technology, University of Granada,18014 Granada, Spain
Interests: bio-signal acquisition devices; neurometry; bio-signal processing; neuro-devices

Special Issue Information

Dear Colleagues,

Brain–computer Interfaces emerged a few decades ago as a new communication technology to permit subjects with severe neuromuscular disorders to communicate and interact with the outer world. Recently, the rapid development of wireless technology opened the door for out-of-the-lab applications, such as in the field of entertainment, industry, marketing, and education. In the future, we foresee a new generation of smart neuro-devices for the cloud-computing of cerebral activity. This includes the deployment of large ensembles of fully-wireless-neural prostheses capable of performing complex tasks and bidirectional sensing. This ecosystem of implantable neuro-sensors constitutes a new framework of the IoT arena, referred to as brain area networks (BRAN). BRAN is a challenging and multidisciplinary paradigm that encompasses communication protocols, efficient wireless energy harvesting, and large-scale neuro-signal acquisition and processing.

This Special Issue will explore the advances, challenges, and future prospects of both invasive and non-invasive brain–computer interfaces (e.g., innovative multi bio-signals headsets, integrated stimulation-acquisition devices, dry electrodes, implantable neuro-technologies, and sensors), services and applications (e.g., applications in telemedicine and telerehabilitation, entertainment, cognitive workload assessment and emotion recognition, neuro-rehabilitation, ambient assisted living, biosecurity, clinical evaluation, neuro-marketing, and education, among others), scalable bio-signals processing algorithms for large numbers of neuro-sensors, including cloud-computing and closed-loop systems.

Dr. Miguel Ángel López Gordo
Dr. Christian A. Morillas Gutiérrez
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at 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. Sensors is an international peer-reviewed open access semimonthly 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 2000 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.


  • Neuro-signal processing, implantable
  • Brain-computer interfaces and applications
  • Neurometry and applications (education, marketing, industry, military, emotions recognition, telemedicine, rehabilitation, e-sports, and brain activity monitoring, among others)
  • Methods for neuro-signal processing and analysis
  • Communication techniques and power harvesting for brain area networks

Published Papers (1 paper)

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Open AccessArticle
Evaluation of Hyperparameter Optimization in Machine and Deep Learning Methods for Decoding Imagined Speech EEG
Sensors 2020, 20(16), 4629; - 17 Aug 2020
Classification of electroencephalography (EEG) signals corresponding to imagined speech production is important for the development of a direct-speech brain–computer interface (DS-BCI). Deep learning (DL) has been utilized with great success across several domains. However, it remains an open question whether DL methods provide [...] Read more.
Classification of electroencephalography (EEG) signals corresponding to imagined speech production is important for the development of a direct-speech brain–computer interface (DS-BCI). Deep learning (DL) has been utilized with great success across several domains. However, it remains an open question whether DL methods provide significant advances over traditional machine learning (ML) approaches for classification of imagined speech. Furthermore, hyperparameter (HP) optimization has been neglected in DL-EEG studies, resulting in the significance of its effects remaining uncertain. In this study, we aim to improve classification of imagined speech EEG by employing DL methods while also statistically evaluating the impact of HP optimization on classifier performance. We trained three distinct convolutional neural networks (CNN) on imagined speech EEG using a nested cross-validation approach to HP optimization. Each of the CNNs evaluated was designed specifically for EEG decoding. An imagined speech EEG dataset consisting of both words and vowels facilitated training on both sets independently. CNN results were compared with three benchmark ML methods: Support Vector Machine, Random Forest and regularized Linear Discriminant Analysis. Intra- and inter-subject methods of HP optimization were tested and the effects of HPs statistically analyzed. Accuracies obtained by the CNNs were significantly greater than the benchmark methods when trained on both datasets (words: 24.97%, p < 1 × 10–7, chance: 16.67%; vowels: 30.00%, p < 1 × 10–7, chance: 20%). The effects of varying HP values, and interactions between HPs and the CNNs were both statistically significant. The results of HP optimization demonstrate how critical it is for training CNNs to decode imagined speech. Full article
(This article belongs to the Special Issue Brain–Computer Interfaces: Advances and Challenges)
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