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

Deadline for manuscript submissions: closed (15 January 2022) | Viewed by 17657

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Prof. Dr. Daniela De Venuto

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Department of Electrical and Information Engineering, Politecnico di Bari, 70125 Bari, Italy
Interests: electronics; sensors
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The study of brain–computer interfaces (BCIs) is a continuously growing research trend that originated in an attempt to permit subjects with severe neuromuscular disorders to communicate and interact with the world around them. Advances in the capabilities of sensors, computation devices, and wireless technologies expanded the BCI concept, and it is now investigated in a wide range of fields such as remote healthcare, industry, marketing, education, and gaming. This topic became hotter in the last year, when BCIs were first applied in driver assistant systems.

This growing trend is projected to pave the way for a new generation of electronic devices that exploit brain signals, on-board computation, and cloud computing to translate cerebral activity in practical application.

This Special Issue will explore the advances, challenges, and future prospects associated with the following topics:

Invasive and non-invasive acquisition and stimulation devices for brain–computer interfaces (e.g., new biosignal headsets, active dry electrodes, neurostimulation systems, and implantable technology);
Online artifact rejection systems (e.g., design and implementation of real-time systems for artifact rejection in mobile BCI (MoBI) and near-sensor rejectors for physiological and non-physiological artifacts);
Brain–computer interface applications (e.g., e-health, remote monitoring, ambient assisted living, emotional and cognitive assessment, emotion recognition, neuro-rehabilitation, biosecurity);
Computing architecture improvements oriented to BCI application (e.g., memory optimization algorithms of electronic control units, new transmission protocols, real-time biosignal computing, and cloud-computing).

Prof. Dr. Daniela De Venuto
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. 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

Brain–computer interfaces (BCIs)
Neurosignal acquisition devices
Artifact rejection
BCI applications
Neurometry
BCI-oriented high-performance computing (HPC)

Published Papers (5 papers)

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Research

16 pages, 3670 KiB

Open AccessArticle

Steady-State Visual Evoked Potential-Based Brain–Computer Interface Using a Novel Visual Stimulus with Quick Response (QR) Code Pattern

by Nannaphat Siribunyaphat and Yunyong Punsawad

Sensors 2022, 22(4), 1439; https://doi.org/10.3390/s22041439 - 13 Feb 2022

Cited by 13 | Viewed by 4324

Abstract

Steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) systems suffer from low SSVEP response intensity and visual fatigue, resulting in lower accuracy when operating the system for continuous commands, such as an electric wheelchair control. This study proposes two SSVEP improvements to create a practical BCI for communication and control in disabled people. The first is flicker pattern modification for increasing SSVEP response through mixing (1) fundamental and first harmonic frequencies, and (2) two fundamental frequencies for an additional number of commands. The second method utilizes a quick response (QR) code for visual stimulus patterns to increase the SSVEP response and reduce visual fatigue. Eight different stimulus patterns from three flickering frequencies (7, 13, and 17 Hz) were presented to twelve participants for the test and score levels of visual fatigue. Two popular SSVEP methods, i.e., power spectral density (PSD) with Welch periodogram and canonical correlation analysis (CCA) with overlapping sliding window, are used to detect SSVEP intensity and response, compared to the checkerboard pattern. The results suggest that the QR code patterns can yield higher accuracy than checkerboard patterns for both PSD and CCA methods. Moreover, a QR code pattern with low frequency can reduce visual fatigue; however, visual fatigue can be easily affected by high flickering frequency. The findings can be used in the future to implement a real-time, SSVEP-based BCI for verifying user and system performance in actual environments. Full article

(This article belongs to the Special Issue Sensors for Brain-Computer Interface)

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16 pages, 3317 KiB

Open AccessArticle

A Cybersecure P300-Based Brain-to-Computer Interface against Noise-Based and Fake P300 Cyberattacks

by Giovanni Mezzina, Valerio F. Annese and Daniela De Venuto

Sensors 2021, 21(24), 8280; https://doi.org/10.3390/s21248280 - 10 Dec 2021

Cited by 3 | Viewed by 2605

Abstract

In a progressively interconnected world where the Internet of Things (IoT), ubiquitous computing, and artificial intelligence are leading to groundbreaking technology, cybersecurity remains an underdeveloped aspect. This is particularly alarming for brain-to-computer interfaces (BCIs), where hackers can threaten the user’s physical and psychological safety. In fact, standard algorithms currently employed in BCI systems are inadequate to deal with cyberattacks. In this paper, we propose a solution to improve the cybersecurity of BCI systems. As a case study, we focus on P300-based BCI systems using support vector machine (SVM) algorithms and EEG data. First, we verified that SVM algorithms are incapable of identifying hacking by simulating a set of cyberattacks using fake P300 signals and noise-based attacks. This was achieved by comparing the performance of several models when validated using real and hacked P300 datasets. Then, we implemented our solution to improve the cybersecurity of the system. The proposed solution is based on an EEG channel mixing approach to identify anomalies in the transmission channel due to hacking. Our study demonstrates that the proposed architecture can successfully identify 99.996% of simulated cyberattacks, implementing a dedicated counteraction that preserves most of BCI functions. Full article

(This article belongs to the Special Issue Sensors for Brain-Computer Interface)

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39 pages, 16109 KiB

Open AccessArticle

Modular Data Acquisition System for Recording Activity and Electrical Stimulation of Brain Tissue Using Dedicated Electronics

by Paweł Jurgielewicz, Tomasz Fiutowski, Ewa Kublik, Andrzej Skoczeń, Małgorzata Szypulska, Piotr Wiącek, Paweł Hottowy and Bartosz Mindur

Sensors 2021, 21(13), 4423; https://doi.org/10.3390/s21134423 - 28 Jun 2021

Cited by 4 | Viewed by 2968

Abstract

In this paper, we present a modular Data Acquisition (DAQ) system for simultaneous electrical stimulation and recording of brain activity. The DAQ system is designed to work with custom-designed Application Specific Integrated Circuit (ASIC) called Neurostim-3 and a variety of commercially available Multi-Electrode Arrays (MEAs). The system can control simultaneously up to 512 independent bidirectional i.e., input-output channels. We present in-depth insight into both hardware and software architectures and discuss relationships between cooperating parts of that system. The particular focus of this study was the exploration of efficient software design so that it could perform all its tasks in real-time using a standard Personal Computer (PC) without the need for data precomputation even for the most demanding experiment scenarios. Not only do we show bare performance metrics, but we also used this software to characterise signal processing capabilities of Neurostim-3 (e.g., gain linearity, transmission band) so that to obtain information on how well it can handle neural signals in real-world applications. The results indicate that each Neurostim-3 channel exhibits signal gain linearity in a wide range of input signal amplitudes. Moreover, their high-pass cut-off frequency gets close to

0.6

Hz

making it suitable for recording both Local Field Potential (LFP) and spiking brain activity signals. Additionally, the current stimulation circuitry was checked in terms of the ability to reproduce complex patterns. Finally, we present data acquired using our system from the experiments on a living rat’s brain, which proved we obtained physiological data from non-stimulated and stimulated tissue. The presented results lead us to conclude that our hardware and software can work efficiently and effectively in tandem giving valuable insights into how information is being processed by the brain. Full article

(This article belongs to the Special Issue Sensors for Brain-Computer Interface)

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24 pages, 4154 KiB

Open AccessArticle

A Single-Trial P300 Detector Based on Symbolized EEG and Autoencoded-(1D)CNN to Improve ITR Performance in BCIs

by Daniela De Venuto and Giovanni Mezzina

Sensors 2021, 21(12), 3961; https://doi.org/10.3390/s21123961 - 08 Jun 2021

Cited by 10 | Viewed by 2744

Abstract

In this paper, we propose a breakthrough single-trial P300 detector that maximizes the information translate rate (ITR) of the brain–computer interface (BCI), keeping high recognition accuracy performance. The architecture, designed to improve the portability of the algorithm, demonstrated full implementability on a dedicated embedded platform. The proposed P300 detector is based on the combination of a novel pre-processing stage based on the EEG signals symbolization and an autoencoded convolutional neural network (CNN). The proposed system acquires data from only six EEG channels; thus, it treats them with a low-complexity preprocessing stage including baseline correction, windsorizing and symbolization. The symbolized EEG signals are then sent to an autoencoder model to emphasize those temporal features that can be meaningful for the following CNN stage. This latter consists of a seven-layer CNN, including a 1D convolutional layer and three dense ones. Two datasets have been analyzed to assess the algorithm performance: one from a P300 speller application in BCI competition III data and one from self-collected data during a fluid prototype car driving experiment. Experimental results on the P300 speller dataset showed that the proposed method achieves an average ITR (on two subjects) of 16.83 bits/min, outperforming by +5.75 bits/min the state-of-the-art for this parameter. Jointly with the speed increase, the recognition performance returned disruptive results in terms of the harmonic mean of precision and recall (F1-Score), which achieve 51.78 ± 6.24%. The same method used in the prototype car driving led to an ITR of ~33 bit/min with an F1-Score of 70.00% in a single-trial P300 detection context, allowing fluid usage of the BCI for driving purposes. The realized network has been validated on an STM32L4 microcontroller target, for complexity and implementation assessment. The implementation showed an overall resource occupation of 5.57% of the total available ROM, ~3% of the available RAM, requiring less than 3.5 ms to provide the classification outcome. Full article

(This article belongs to the Special Issue Sensors for Brain-Computer Interface)

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11 pages, 2522 KiB

Open AccessArticle

Evaluation of Child–Computer Interaction Using Fitts’ Law: A Comparison between a Standard Computer Mouse and a Head Mouse

by Cristina Sanchez, Vanina Costa, Rodrigo Garcia-Carmona, Eloy Urendes, Javier Tejedor and Rafael Raya

Sensors 2021, 21(11), 3826; https://doi.org/10.3390/s21113826 - 31 May 2021

Cited by 5 | Viewed by 3035

Abstract

This study evaluates and compares the suitability for child–computer interaction (CCI, the branch within human–computer interaction focused on interactive computer systems for children) of two devices: a standard computer mouse and the ENLAZA interface, a head mouse that measures the user’s head posture using an inertial sensor. A multidirectional pointing task was used to assess the motor performance and the users’ ability to learn such a task. The evaluation was based on the interpretation of the metrics derived from Fitts’ law. Ten children aged between 6 and 8 participated in this study. Participants performed a series of pre- and post-training tests for both input devices. After the experiments, data were analyzed and statistically compared. The results show that Fitts’ law can be used to detect changes in the learning process and assess the level of psychomotor development (by comparing the performance of adults and children). In addition, meaningful differences between the fine motor control (hand) and the gross motor control (head) were found by comparing the results of the interaction using the two devices. These findings suggest that Fitts’ law metrics offer a reliable and objective way of measuring the progress of physical training or therapy. Full article

(This article belongs to the Special Issue Sensors for Brain-Computer Interface)

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