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Electronics
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Advanced Technologies and Challenges in Brain Machine Interface
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Advanced Technologies and Challenges in Brain Machine Interface

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Bioelectronics".

Deadline for manuscript submissions: closed (30 October 2021) | Viewed by 13418

Special Issue Editor

Prof. Dr. Dong-Joo Kim

E-Mail Website
Guest Editor
Department of Brain and Cognitive Engineering, Korea University, Seoul 136-701, Republic of Korea
Interests: digital therapeutics; personalized medicine; artificial intelligence; brain-computer interface; neurophysiological monitoring; medical image analysis; neurorehabilitation; neuropsychiatric disorders; acquired brain injury

Special Issue Information

Dear Colleagues,

The concept of the brain–machine interface (BMI) has existed for decades. Indeed, there is little doubt that BMI, if matured, could be used in every conceivable aspect of our daily life. With the rapid advancements in machine learning techniques, there has been a growing interest in further facilitating the utility of BMI outside the laboratory environments. Nevertheless, most of the existing BMI methods heavily rely on the use of electroencephalography (EEG), which involves several technological difficulties—namely, adequate placing and type of the electrodes, signal quality control for the acquired EEG, and proper, real-time interpretation of the EEG. These well-known yet still prevalent problems have been significantly hindering the active implementation of the BMI in industrial fields.

This Special Issue calls for original research papers that address the aforementioned issues of EEG, and further, studies that propose novel methods for non-EEG-based or multimodal BMI.  We are also interested in review articles focusing on recent advancements in BMI applications and/or the use of machine learning techniques in the development of BMI.

Prof. Dr. Dong-Joo Kim
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 submissions that pass pre-check are 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. Electronics 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 2400 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

  • Artificial intelligence
  • Brain–computer (machine) interface
  • Dry electrode
  • Electroencephalogram
  • Feature extraction
  • Signal processing
  • Machine learning
  • Multimodal neuromonitoring
  • Neurorehabilitation

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Published Papers (3 papers)

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Research

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12 pages, 2128 KiB  
Article
The Human—Unmanned Aerial Vehicle System Based on SSVEP—Brain Computer Interface
by Ming-An Chung, Chia-Wei Lin and Chih-Tsung Chang
Electronics 2021, 10(23), 3025; https://doi.org/10.3390/electronics10233025 - 3 Dec 2021
Cited by 8 | Viewed by 2988
Abstract
The brain–computer interface (BCI) is a mechanism for extracting information from the brain, with this information used for various applications. This study proposes a method to control an unmanned aerial vehicle (UAV) flying through a BCI system using the steady-state visual evoked potential [...] Read more.
The brain–computer interface (BCI) is a mechanism for extracting information from the brain, with this information used for various applications. This study proposes a method to control an unmanned aerial vehicle (UAV) flying through a BCI system using the steady-state visual evoked potential (SSVEP) approach. The UAV’s screen emits three frequencies for visual stimulation: 15, 23, and 31 Hz for the UAV’s left-turn, forward-flight, and right-turn functions. Due to the requirement of immediate response to the UAV flight, this paper proposes a method to improve the accuracy rate and reduce the time required to correct instruction errors in the resolution of brainwave signals received by UAVs. This study tested ten subjects and verified that the proposed method has a 10% improvement inaccuracy. While the traditional method can take 8 s to correct an error, the proposed method requires only 1 s, making it more suitable for practical applications in UAVs. Furthermore, such a BCI application for UAV systems can achieve the same experience of using the remote control for physically challenged patients. Full article
(This article belongs to the Special Issue Advanced Technologies and Challenges in Brain Machine Interface)
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20 pages, 3914 KiB  
Article
Explainable Convolutional Neural Network to Investigate Age-Related Changes in Multi-Order Functional Connectivity
by Sunghee Dong, Yan Jin, SuJin Bak, Bumchul Yoon and Jichai Jeong
Electronics 2021, 10(23), 3020; https://doi.org/10.3390/electronics10233020 - 3 Dec 2021
Cited by 4 | Viewed by 2801
Abstract
Functional connectivity (FC) is a potential candidate that can increase the performance of brain-computer interfaces (BCIs) in the elderly because of its compensatory role in neural circuits. However, it is difficult to decode FC by the current machine learning techniques because of a [...] Read more.
Functional connectivity (FC) is a potential candidate that can increase the performance of brain-computer interfaces (BCIs) in the elderly because of its compensatory role in neural circuits. However, it is difficult to decode FC by the current machine learning techniques because of a lack of physiological understanding. To investigate the suitability of FC in BCIs for the elderly, we propose the decoding of lower- and higher-order FC using a convolutional neural network (CNN) in six cognitive-motor tasks. The layer-wise relevance propagation (LRP) method describes how age-related changes in FCs impact BCI applications for the elderly compared to younger adults. A total of 17 young adults 24.5±2.7 years and 12 older 72.5±3.2 years adults were recruited to perform tasks related to hand-force control with or without mental calculation. The CNN yielded a six-class classification accuracy of 75.3% in the elderly, exceeding the 70.7% accuracy for the younger adults. In the elderly, the proposed method increased the classification accuracy by 88.3% compared to the filter-bank common spatial pattern. The LRP results revealed that both lower- and higher-order FCs were dominantly overactivated in the prefrontal lobe, depending on the task type. These findings suggest a promising application of multi-order FC with deep learning on BCI systems for the elderly. Full article
(This article belongs to the Special Issue Advanced Technologies and Challenges in Brain Machine Interface)
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Review

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26 pages, 403 KiB  
Review
Wireless Sensors for Brain Activity—A Survey
by Mahyar TajDini, Volodymyr Sokolov, Ievgeniia Kuzminykh, Stavros Shiaeles and Bogdan Ghita
Electronics 2020, 9(12), 2092; https://doi.org/10.3390/electronics9122092 - 8 Dec 2020
Cited by 28 | Viewed by 6899
Abstract
Over the last decade, the area of electroencephalography (EEG) witnessed a progressive move from high-end large measurement devices, relying on accurate construction and providing high sensitivity, to miniature hardware, more specifically wireless wearable EEG devices. While accurate, traditional EEG systems need a complex [...] Read more.
Over the last decade, the area of electroencephalography (EEG) witnessed a progressive move from high-end large measurement devices, relying on accurate construction and providing high sensitivity, to miniature hardware, more specifically wireless wearable EEG devices. While accurate, traditional EEG systems need a complex structure and long periods of application time, unwittingly causing discomfort and distress on the users. Given their size and price, aside from their lower sensitivity and narrower spectrum band(s), wearable EEG devices may be used regularly by individuals for continuous collection of user data from non-medical environments. This allows their usage for diverse, nontraditional, non-medical applications, including cognition, BCI, education, and gaming. Given the reduced need for standardization or accuracy, the area remains a rather incipient one, mostly driven by the emergence of new devices that represent the critical link of the innovation chain. In this context, the aim of this study is to provide a holistic assessment of the consumer-grade EEG devices for cognition, BCI, education, and gaming, based on the existing products, the success of their underlying technologies, as benchmarked by the undertaken studies, and their integration with current applications across the four areas. Beyond establishing a reference point, this review also provides the critical and necessary systematic guidance for non-medical EEG research and development efforts at the start of their investigation. Full article
(This article belongs to the Special Issue Advanced Technologies and Challenges in Brain Machine Interface)
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