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Real-Life Wearable EEG-Based BCI: Open Challenges
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Real-Life Wearable EEG-Based BCI: Open Challenges

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

Deadline for manuscript submissions: closed (31 May 2023) | Viewed by 15770

Special Issue Editors

Dr. Silvia Corchs

E-Mail Website
Guest Editor
Department of Theoretical and Applied Sciences, University of Insubria, Via Ravasi, 2, 21100 Varese, Italy
Interests: cognitive computing; computational neuroscience; human-computer interaction
Special Issues, Collections and Topics in MDPI journals
Dr. Francesca Gasparini

E-Mail Website
Guest Editor
Department of Informatics, Systems and Communication, University of Milano-Bicocca, 20126 Milano, Italy
Interests: multimedia signal processing; image quality assessment; image complexity; affective signal processing
Special Issues, Collections and Topics in MDPI journals
Dr. Aurora Saibene

E-Mail Website
Guest Editor
Department of Informatics, Systems and Communication, University of Milano-Bicocca, 20126 Milan, Italy
Interests: brain-computer interfacing; electroencephalography; human-machine interaction; motor imagery; signal processing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The evolution from clinical EEG devices to wearable ones has opened a new field of brain–computer interface (BCI) applications in real-life scenarios that require multidisciplinary competencies. Recent advances in wearable sensing technology are enabling new methods for data acquisition and collection, moving BCIs from research labs to home and making them more pervasive in real-life scenarios. 

This Special Issue aims to highlight the current challenges and future perspectives that arise in the field of wearable EEG-based BCIs, especially when considering in-home and real-life healthcare and wellbeing applications.

Possible topics include but are not limited to:

  • Innovative wearable devices in terms of technological solutions (e.g., peculiar caps, new sensors, efficient communication devices);
  • Efficient, reliable, and secure communication protocols and data protection tools (e.g., cloud-based data collection, Bluetooth);
  • Multimodal approaches to support wearable EEG-based BCIs (e.g., multiple sensor data acquisition, integration with IoT devices);
  • Real-time data processing (e.g., signal processing, feature engineering, machine learning methodologies);
  • Signal quality in terms of reliability and accuracy of the collected data (e.g., electrode number and typology, artifacts, comparisons between signals obtained through clinical devices and consumer-grade sensors);
  • Human–machine interaction (e.g., ergonomics, user acceptance, protocols);
  • Application fields closely related to healthcare and wellbeing, especially in real-life and in-home scenarios (e.g., applications that guarantee a continuous assistance in patients’ follow-up treatment, patient-self-managed treatment at home, general wellbeing monitoring);
  • Neurosecurity and neuroethics (e.g., data protection, possible exploits, data usage).

Dr. Silvia Elena Corchs
Dr. Francesca Gasparini
Dr. Aurora Saibene
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 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. 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 2600 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

  • BCI
  • wearable
  • EEG
  • real-time processing
  • AI
  • sensor technology
  • communication
  • human interaction
  • wearable sensor fusion
  • neurosecurity

Published Papers (5 papers)

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Research

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24 pages, 9678 KiB  
Article
A Novel OpenBCI Framework for EEG-Based Neurophysiological Experiments
by Yeison Nolberto Cardona-Álvarez, Andrés Marino Álvarez-Meza, David Augusto Cárdenas-Peña, Germán Albeiro Castaño-Duque and German Castellanos-Dominguez
Sensors 2023, 23(7), 3763; https://doi.org/10.3390/s23073763 - 06 Apr 2023
Cited by 4 | Viewed by 3374
Abstract
An Open Brain–Computer Interface (OpenBCI) provides unparalleled freedom and flexibility through open-source hardware and firmware at a low-cost implementation. It exploits robust hardware platforms and powerful software development kits to create customized drivers with advanced capabilities. Still, several restrictions may significantly reduce the [...] Read more.
An Open Brain–Computer Interface (OpenBCI) provides unparalleled freedom and flexibility through open-source hardware and firmware at a low-cost implementation. It exploits robust hardware platforms and powerful software development kits to create customized drivers with advanced capabilities. Still, several restrictions may significantly reduce the performance of OpenBCI. These limitations include the need for more effective communication between computers and peripheral devices and more flexibility for fast settings under specific protocols for neurophysiological data. This paper describes a flexible and scalable OpenBCI framework for electroencephalographic (EEG) data experiments using the Cyton acquisition board with updated drivers to maximize the hardware benefits of ADS1299 platforms. The framework handles distributed computing tasks and supports multiple sampling rates, communication protocols, free electrode placement, and single marker synchronization. As a result, the OpenBCI system delivers real-time feedback and controlled execution of EEG-based clinical protocols for implementing the steps of neural recording, decoding, stimulation, and real-time analysis. In addition, the system incorporates automatic background configuration and user-friendly widgets for stimuli delivery. Motor imagery tests the closed-loop BCI designed to enable real-time streaming within the required latency and jitter ranges. Therefore, the presented framework offers a promising solution for tailored neurophysiological data processing. Full article
(This article belongs to the Special Issue Real-Life Wearable EEG-Based BCI: Open Challenges)
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14 pages, 3703 KiB  
Article
Investigating User Proficiency of Motor Imagery for EEG-Based BCI System to Control Simulated Wheelchair
by Theerat Saichoo, Poonpong Boonbrahm and Yunyong Punsawad
Sensors 2022, 22(24), 9788; https://doi.org/10.3390/s22249788 - 13 Dec 2022
Cited by 2 | Viewed by 2033
Abstract
The research on the electroencephalography (EEG)-based brain–computer interface (BCI) is widely utilized for wheelchair control. The ability of the user is one factor of BCI efficiency. Therefore, we focused on BCI tasks and protocols to yield high efficiency from the robust EEG features [...] Read more.
The research on the electroencephalography (EEG)-based brain–computer interface (BCI) is widely utilized for wheelchair control. The ability of the user is one factor of BCI efficiency. Therefore, we focused on BCI tasks and protocols to yield high efficiency from the robust EEG features of individual users. This study proposes a task-based brain activity to gain the power of the alpha band, which included eyes closed for alpha response at the occipital area, attention to an upward arrow for alpha response at the frontal area, and an imagined left/right motor for alpha event-related desynchronization at the left/right motor cortex. An EPOC X neuroheadset was used to acquire the EEG signals. We also proposed user proficiency in motor imagery sessions with limb movement paradigms by recommending motor imagination tasks. Using the proposed system, we verified the feature extraction algorithms and command translation. Twelve volunteers participated in the experiment, and the conventional paradigm of motor imagery was used to compare the efficiencies. With utilized user proficiency in motor imagery, an average accuracy of 83.7% across the left and right commands was achieved. The recommended MI paradigm via user proficiency achieved an approximately 4% higher accuracy than the conventional MI paradigm. Moreover, the real-time control results of a simulated wheelchair revealed a high efficiency based on the time condition. The time results for the same task as the joystick-based control were still approximately three times longer. We suggest that user proficiency be used to recommend an individual MI paradigm for beginners. Furthermore, the proposed BCI system can be used for electric wheelchair control by people with severe disabilities. Full article
(This article belongs to the Special Issue Real-Life Wearable EEG-Based BCI: Open Challenges)
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11 pages, 2223 KiB  
Article
The Dominance of Anticipatory Prefrontal Activity in Uncued Sensory–Motor Tasks
by Merve Aydin, Anna Laura Carpenelli, Stefania Lucia and Francesco Di Russo
Sensors 2022, 22(17), 6559; https://doi.org/10.3390/s22176559 - 31 Aug 2022
Cited by 3 | Viewed by 1523
Abstract
Anticipatory event-related potentials (ERPs) precede upcoming events such as stimuli or actions. These ERPs are usually obtained in cued sensory–motor tasks employing a warning stimulus that precedes a probe stimulus as in the contingent negative variation (CNV) paradigms. The CNV wave has been [...] Read more.
Anticipatory event-related potentials (ERPs) precede upcoming events such as stimuli or actions. These ERPs are usually obtained in cued sensory–motor tasks employing a warning stimulus that precedes a probe stimulus as in the contingent negative variation (CNV) paradigms. The CNV wave has been widely studied, from clinical to brain–computer interface (BCI) applications, and has been shown to emerge in medial frontoparietal areas, localized in the cingulate and supplementary motor areas. Several dated studies also suggest the existence of a prefrontal CNV, although this component was not confirmed by later studies due to the contamination of ocular artifacts. Another lesser-known anticipatory ERP is the prefrontal negativity (pN) that precedes the uncued probe stimuli in discriminative response tasks and has been localized in the inferior frontal gyrus. This study aimed to characterize the pN by comparing it with the CNV in cued and uncued tasks and test if the pN could be associated with event preparation, temporal preparation, or both. To achieve these aims, high-density electroencephalographic recording and advanced ERP analysis controlling for ocular activity were obtained in 25 volunteers who performed 4 different visuomotor tasks. Our results showed that the pN amplitude was largest in the condition requiring both time and event preparation, medium in the condition requiring event preparation only, and smallest in the condition requiring temporal preparation only. We concluded that the prefrontal CNV could be associated with the pN, and this activity emerges in complex tasks requiring the anticipation of both the category and timing of the upcoming stimulus. The proposed method can be useful in BCI studies investigating the endogenous neural signatures triggered by different sensorimotor paradigms. Full article
(This article belongs to the Special Issue Real-Life Wearable EEG-Based BCI: Open Challenges)
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23 pages, 7666 KiB  
Article
Cross-Platform Implementation of an SSVEP-Based BCI for the Control of a 6-DOF Robotic Arm
by Eduardo Quiles, Javier Dadone, Nayibe Chio and Emilio García
Sensors 2022, 22(13), 5000; https://doi.org/10.3390/s22135000 - 02 Jul 2022
Cited by 11 | Viewed by 3141
Abstract
Robotics has been successfully applied in the design of collaborative robots for assistance to people with motor disabilities. However, man-machine interaction is difficult for those who suffer severe motor disabilities. The aim of this study was to test the feasibility of a low-cost [...] Read more.
Robotics has been successfully applied in the design of collaborative robots for assistance to people with motor disabilities. However, man-machine interaction is difficult for those who suffer severe motor disabilities. The aim of this study was to test the feasibility of a low-cost robotic arm control system with an EEG-based brain-computer interface (BCI). The BCI system relays on the Steady State Visually Evoked Potentials (SSVEP) paradigm. A cross-platform application was obtained in C++. This C++ platform, together with the open-source software Openvibe was used to control a Stäubli robot arm model TX60. Communication between Openvibe and the robot was carried out through the Virtual Reality Peripheral Network (VRPN) protocol. EEG signals were acquired with the 8-channel Enobio amplifier from Neuroelectrics. For the processing of the EEG signals, Common Spatial Pattern (CSP) filters and a Linear Discriminant Analysis classifier (LDA) were used. Five healthy subjects tried the BCI. This work allowed the communication and integration of a well-known BCI development platform such as Openvibe with the specific control software of a robot arm such as Stäubli TX60 using the VRPN protocol. It can be concluded from this study that it is possible to control the robotic arm with an SSVEP-based BCI with a reduced number of dry electrodes to facilitate the use of the system. Full article
(This article belongs to the Special Issue Real-Life Wearable EEG-Based BCI: Open Challenges)
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Review

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42 pages, 1011 KiB  
Review
EEG-Based BCIs on Motor Imagery Paradigm Using Wearable Technologies: A Systematic Review
by Aurora Saibene, Mirko Caglioni, Silvia Corchs and Francesca Gasparini
Sensors 2023, 23(5), 2798; https://doi.org/10.3390/s23052798 - 03 Mar 2023
Cited by 7 | Viewed by 4044
Abstract
In recent decades, the automatic recognition and interpretation of brain waves acquired by electroencephalographic (EEG) technologies have undergone remarkable growth, leading to a consequent rapid development of brain–computer interfaces (BCIs). EEG-based BCIs are non-invasive systems that allow communication between a human being and [...] Read more.
In recent decades, the automatic recognition and interpretation of brain waves acquired by electroencephalographic (EEG) technologies have undergone remarkable growth, leading to a consequent rapid development of brain–computer interfaces (BCIs). EEG-based BCIs are non-invasive systems that allow communication between a human being and an external device interpreting brain activity directly. Thanks to the advances in neurotechnologies, and especially in the field of wearable devices, BCIs are now also employed outside medical and clinical applications. Within this context, this paper proposes a systematic review of EEG-based BCIs, focusing on one of the most promising paradigms based on motor imagery (MI) and limiting the analysis to applications that adopt wearable devices. This review aims to evaluate the maturity levels of these systems, both from the technological and computational points of view. The selection of papers has been performed following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA), leading to 84 publications considered in the last ten years (from 2012 to 2022). Besides technological and computational aspects, this review also aims to systematically list experimental paradigms and available datasets in order to identify benchmarks and guidelines for the development of new applications and computational models. Full article
(This article belongs to the Special Issue Real-Life Wearable EEG-Based BCI: Open Challenges)
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