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Electronics
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Development and Validation of Innovative Low Cost Brain-Computer-Interfaces
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Development and Validation of Innovative Low Cost Brain-Computer-Interfaces

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: closed (31 January 2023) | Viewed by 10701

Special Issue Editor

Dr. Luca Mesin

E-Mail Website
Guest Editor
Department of Electronics and Telecommunications, Polytechnic University of Turin, Turin, Italy
Interests: biomedical signal and image processing and classification; biophysical modelling; clinical studies; mathematical biology and physiology; noninvasive monitoring of the volemic status of patients; nonlinear biomedical signal processing; optimal non-uniform down-sampling; systems for human–machine interaction
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Brain–computer interfaces (BCIs) allow direct communication between the brain and an external device. They have already found many applications, for example in neuro-rehabilitation, the restoration of capabilities lost by the user (e.g., motion or communication), gaming and entertainment. This Special Issue is interested in hardware and processing solutions, supporting the development of low-cost systems.

Possible topics include, but are not limited to:

  • The development of new prototypes and/or paradigms for interfacing to the user’s brain.
  • Development of stable sensors for long-term applications.
  • Innovative algorithms for the processing of EEG in BCI applications (artifact removal, identification of cortical responses, etc.).
  • Real-time processing and simple user interface for effective interaction.
  • Advanced applications for healthy subjects in challenging situations (augmented control of devices, monitoring while driving a car, etc.).
  • Applications in patients (home automation, restoration of motion or communication, etc.).
  • Development of hybrid BCI systems that use multimodal inputs to improve their performances (EEG-EMG, EEG-pupil size, etc.).

Prof. Dr. Luca Mesin
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

  • brain–computer interface (BCI)
  • human–machine interaction
  • EEG
  • pupil control
  • NIRS
  • rehabilitation
  • communication
  • vigilance

Published Papers (6 papers)

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Research

13 pages, 1069 KiB  
Article
Non-Linear Adapted Spatio-Temporal Filter for Single-Trial Identification of Movement-Related Cortical Potential
by Luca Mesin, Usman Ghani and Imran Khan Niazi
Electronics 2023, 12(5), 1246; https://doi.org/10.3390/electronics12051246 - 05 Mar 2023
Cited by 2 | Viewed by 1095
Abstract
The execution or imagination of a movement is reflected by a cortical potential that can be recorded by electroencephalography (EEG) as Movement-Related Cortical Potentials (MRCPs). The identification of MRCP from a single trial is a challenging possibility to get a natural control of [...] Read more.
The execution or imagination of a movement is reflected by a cortical potential that can be recorded by electroencephalography (EEG) as Movement-Related Cortical Potentials (MRCPs). The identification of MRCP from a single trial is a challenging possibility to get a natural control of a Brain–Computer Interface (BCI). We propose a novel method for MRCP detection based on optimal non-linear filters, processing different channels of EEG including delayed samples (getting a spatio-temporal filter). Different outputs can be obtained by changing the order of the temporal filter and of the non-linear processing of the input data. The classification performances of these filters are assessed by cross-validation on a training set, selecting the best ones (adapted to the user) and performing a majority voting from the best three to get an output using test data. The method is compared to another state-of-the-art filter recently introduced by our group when applied to EEG data recorded from 16 healthy subjects either executing or imagining 50 self-paced upper-limb palmar grasps. The new approach has a median accuracy on the overall dataset of 80%, which is significantly better than that of the previous filter (i.e., 63%). It is feasible for online BCI system design with asynchronous, self-paced applications. Full article
(This article belongs to the Special Issue Development and Validation of Innovative Low Cost Brain-Computer-Interfaces)
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25 pages, 2636 KiB  
Article
The BciAi4SLA Project: Towards a User-Centered BCI
by Cristina Gena, Dize Hilviu, Giovanni Chiarion, Silvestro Roatta, Francesca M. Bosco, Andrea Calvo, Claudio Mattutino and Stefano Vincenzi
Electronics 2023, 12(5), 1234; https://doi.org/10.3390/electronics12051234 - 04 Mar 2023
Cited by 4 | Viewed by 1717
Abstract
The brain–computer interfaces (BCI) are interfaces that put the user in communication with an electronic device based on signals originating from the brain. In this paper, we describe a proof of concept that took place within the context of BciAi4Sla, a multidisciplinary project [...] Read more.
The brain–computer interfaces (BCI) are interfaces that put the user in communication with an electronic device based on signals originating from the brain. In this paper, we describe a proof of concept that took place within the context of BciAi4Sla, a multidisciplinary project involving computer scientists, physiologists, biomedical engineers, neurologists, and psychologists with the aim of designing and developing a BCI system following a user-centered approach, involving domain experts and users since initial prototyping steps in a design–test–redesign development cycle. The project intends to develop a software platform able to restore a communication channel in patients who have compromised their communication possibilities due to illness or accidents. The most common case is the patients with amyotrophic lateral sclerosis (ALS). In this paper, we describe the background and the main development steps of the project, also reporting some initial and promising user evaluation results, including real-time performance classification and a proof-of-concept prototype. Full article
(This article belongs to the Special Issue Development and Validation of Innovative Low Cost Brain-Computer-Interfaces)
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27 pages, 14892 KiB  
Article
Measuring Brain Activation Patterns from Raw Single-Channel EEG during Exergaming: A Pilot Study
by Gianluca Amprimo, Irene Rechichi, Claudia Ferraris and Gabriella Olmo
Electronics 2023, 12(3), 623; https://doi.org/10.3390/electronics12030623 - 26 Jan 2023
Cited by 3 | Viewed by 2170
Abstract
Physical and cognitive rehabilitation is deemed crucial to attenuate symptoms and to improve the quality of life in people with neurodegenerative disorders, such as Parkinson’s Disease. Among rehabilitation strategies, a novel and popular approach relies on exergaming: the patient performs a motor or [...] Read more.
Physical and cognitive rehabilitation is deemed crucial to attenuate symptoms and to improve the quality of life in people with neurodegenerative disorders, such as Parkinson’s Disease. Among rehabilitation strategies, a novel and popular approach relies on exergaming: the patient performs a motor or cognitive task within an interactive videogame in a virtual environment. These strategies may widely benefit from being tailored to the patient’s needs and engagement patterns. In this pilot study, we investigated the ability of a low-cost BCI based on single-channel EEG to measure the user’s engagement during an exergame. As a first step, healthy subjects were recruited to assess the system’s capability to distinguish between (1) rest and gaming conditions and (2) gaming at different complexity levels, through Machine Learning supervised models. Both EEG and eye-blink features were employed. The results indicate the ability of the exergame to stimulate engagement and the capability of the supervised classification models to distinguish resting stage from game-play (accuracy > 95%). Finally, different clusters of subject responses throughout the game were identified, which could help define models of engagement trends. This result is a starting point in developing an effectively subject-tailored exergaming system. Full article
(This article belongs to the Special Issue Development and Validation of Innovative Low Cost Brain-Computer-Interfaces)
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13 pages, 891 KiB  
Article
Gaming for Training Voluntary Control of Pupil Size
by Leonardo Cardinali, Silvestro Roatta, Raffaele Pertusio, Marcella Testa and Cristina Moglia
Electronics 2022, 11(22), 3713; https://doi.org/10.3390/electronics11223713 - 13 Nov 2022
Cited by 2 | Viewed by 1149
Abstract
Users can “voluntarily” control the size of their pupil by switching focus from a far target A (large pupil size) to a near target B (small pupil size), according to the pupillary accommodative response (PAR). Pupil size is governed by smooth muscles and [...] Read more.
Users can “voluntarily” control the size of their pupil by switching focus from a far target A (large pupil size) to a near target B (small pupil size), according to the pupillary accommodative response (PAR). Pupil size is governed by smooth muscles and has been suggested as communication pathway for patients affected by paralysis of skeletal muscles, such as in amyotrophic lateral sclerosis (ALS). We here present a video game that relies on PAR: a 2d side-scroller game where the user, by varying pupil size, controls the height at which a spaceship is moving aiming at colliding with bubbles to burst them and score points. The height at which the spaceship flies inversely depends on pupil area. The game is implemented on a Raspberry Pi board equipped with a IR camera and may record the time course of pupil size during the game, for off-line analysis. This application is intended as a tool to train and familiarize with the control of pupil size for alternative augmentative communication. Full article
(This article belongs to the Special Issue Development and Validation of Innovative Low Cost Brain-Computer-Interfaces)
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17 pages, 5504 KiB  
Article
A Fully Integrated Passive Self-Jamming Cancellation Architecture with Fast Settling Time for UHF RFID Reader
by Qinan Chen, Zheng Li, Dahai Jiang, Qiang Shan, Zihui Wei, Jinjin Xiao, Shuilong Huang and Yu Liu
Electronics 2022, 11(15), 2311; https://doi.org/10.3390/electronics11152311 - 25 Jul 2022
Viewed by 1468
Abstract
This paper presents a fully integrated passive self-jamming cancellation (SJC) circuit in 0.18 µm Complementary Metal Oxide Semiconductor (CMOS) technology for ultra-high frequency (UHF) radio frequency identification (RFID) applications. Based on the active amplitude and phase control, a novel passive variable capacitor array [...] Read more.
This paper presents a fully integrated passive self-jamming cancellation (SJC) circuit in 0.18 µm Complementary Metal Oxide Semiconductor (CMOS) technology for ultra-high frequency (UHF) radio frequency identification (RFID) applications. Based on the active amplitude and phase control, a novel passive variable capacitor array and signal combiner are adopted instead of a traditional variable amplifier/attenuator and a phase shifter to reduce the circuit complexity and thus achieve higher linearity and low noise. We use an improved cancellation algorithm based on the local search method to quickly and accurately find the cancellation point that minimizes the self-jamming signal power. The simulation and measurement results are constant, and a suppression of 38 dB can be achieved in the working frequency of 860–960 MHz. The cancellation algorithm can be finished within 0.5 ms. These results indicate that the designed SJC circuit can be a promising candidate for UHF RFID applications. Full article
(This article belongs to the Special Issue Development and Validation of Innovative Low Cost Brain-Computer-Interfaces)
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17 pages, 3229 KiB  
Article
A Low-Power ADPLL with Calibration-Free RO-Based Injection-Locking TDC for BLE Applications
by Qinan Chen, Qiang Shan, Zihui Wei, Xiaosong Wang, Shuilong Huang and Yu Liu
Electronics 2022, 11(13), 1953; https://doi.org/10.3390/electronics11131953 - 22 Jun 2022
Cited by 1 | Viewed by 1817
Abstract
This paper proposes a low-power all-digital phase-locked loop (ADPLL) with calibration-free ring oscillator (RO)-based injection-locking time to digital converter (TDC) for BLE applications. The RO is reused as the delay cell of TDC, and the quantization step of TDC is always tracked with [...] Read more.
This paper proposes a low-power all-digital phase-locked loop (ADPLL) with calibration-free ring oscillator (RO)-based injection-locking time to digital converter (TDC) for BLE applications. The RO is reused as the delay cell of TDC, and the quantization step of TDC is always tracked with the RO period; hence no calibration is needed in this architecture. We adopt RO tuning to lower the injection-locking bandwidth so as to decrease the power consumption of the injection current. Moreover, the fractional part of phase error detection is turned down in the coarse tuning of ADPLL to save power. An LC-based digital-controlled oscillator (LCDCO) with a 6.4 nH inductor and a resistive bias is used to have a low power and better phase noise performance. The ADPLL is fabricated in 40 nm CMOS with a 1 V supply and consumes 1.4 mW when it is locked. The measured phase noise is −114 dBc/Hz at 1 MHz offset. The test results show significant power saving. Thus, it can be a promising candidate for BLE applications. Full article
(This article belongs to the Special Issue Development and Validation of Innovative Low Cost Brain-Computer-Interfaces)
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