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A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Applied Biosciences and Bioengineering".

Deadline for manuscript submissions: closed (30 December 2022) | Viewed by 30618

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Special Issue Editors

Dr. Szczepan Paszkiel

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Guest Editor

Faculty of Electrical Engineering, Automatic Control & Informatics, Opole University of Technology, Opole, Poland
Interests: EEG; electroencephalography; biofeedback; analysis; neuroscience; brain-computer interfaces technology; digital signal processing; biomedical signals; analysis of biomedical data.
Special Issues, Collections and Topics in MDPI journals

Dr. Ningrong Lei

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Guest Editor

Department of Engineering and Mathematics, Sheffield Hallam University, Sheffield S1 1WB, UK
Interests: health monitoring service platform; DL; Internet of Things EEG; electroencephalography; biofeedback; analysis; neuroscience
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Prof. Dr. Maria António Castro

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Guest Editor

Higher School of Health, CitechCare, CDRSP, Polytechnic Institute of Leiria, 2411-901 Leiria, Portugal
Interests: biomechanics and motor control of human movements
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Brain–computer interface technology (BCI) emerged five decades ago as a new communication technology to permit subjects with severe neuromuscular disorders to communicate and interact with the outer world. The rapid development of wireless technology has opened the door for out of the lab applications, such as in the field of entertainment, neurogaming, industry, education, and neuromarketing. More and more new applications of brain–computer interfaces technology are emerging, including implementations in motor imagery.

This Special Issue on “Advance in Technology of Brain-Computer Interface” will explore the implementations and future prospects of both non-invasive and invasive brain–computer interface technology. The scope of the release includes, among others, BCI technology, machine learning, motor imagery, analysis of biomedical signals, modeling–neuroinformatics, biomedical engineering, control and robotics, and computer engineering.

Dr. Szczepan Paszkiel
Dr. Ningrong Lei
Prof. Dr. Maria António Castro
Guest Editors

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Keywords

electroencephalography
machine learning
EEG signal processing
neural networks
artificial intelligence
motor imagery
brain–computer interface
BCI
methods for neurosignal processing and analysis
modeling
neuroinformatics

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Published Papers (10 papers)

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Research

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22 pages, 4524 KiB

Open AccessArticle

BCI Wheelchair Control Using Expert System Classifying EEG Signals Based on Power Spectrum Estimation and Nervous Tics Detection

by Dawid Pawuś and Szczepan Paszkiel

Appl. Sci. 2022, 12(20), 10385; https://doi.org/10.3390/app122010385 - 14 Oct 2022

Cited by 16 | Viewed by 2716

Abstract

The constantly developing biomedical engineering field and newer and more advanced BCI (brain–computer interface) systems require their designers to constantly develop and search for various innovative methods used in their creation. In response to practical requirements and the possibility of using the system in real conditions, the authors propose an advanced solution using EEG (electroencephalography) signal analysis. A BCI system design approach using artificial intelligence for the advanced analysis of signals containing facial expressions as control commands was used. The signals were burdened with numerous artifacts caused by simulated nervous tics. The proposed expert system consisted of two neural networks. The first one allowed for the analysis of one-second samples of EEG signals from selected electrodes on the basis of power spectrum estimation waveforms. Thus, it was possible to generate an appropriate control signal as a result of appropriate facial expression commands. The second of the neural networks detected the appearance and type of nervous tics in the signal. Additionally, the participants were affected by interference such as street and TV or radio sound, Wi-Fi and radio waves. The system designed in such a way is adapted to the requirements of the everyday life of people with disabilities, in particular those in wheelchairs, whose control is based on BCI technology. Full article

(This article belongs to the Special Issue Advances in Technology of Brain-Computer Interface)

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13 pages, 966 KiB

Open AccessArticle

Influence of Cognitive Task Difficulty in Postural Control and Hemodynamic Response in the Prefrontal Cortex during Static Postural Standing

by Marina Saraiva, Szczepan Paszkiel, João Paulo Vilas-Boas and Maria António Castro

Appl. Sci. 2022, 12(13), 6363; https://doi.org/10.3390/app12136363 - 22 Jun 2022

Cited by 4 | Viewed by 2084

Abstract

In daily life, we perform several tasks simultaneously, and it is essential to have adequate postural control to succeed. Furthermore, when performing two or more tasks concurrently, changes in postural oscillation are expected due to the competition for the attentional resources. The aim of this study was to evaluate and compare the center of pressure (CoP) behavior and the hemodynamic response of the prefrontal cortex during static postural standing while performing cognitive tasks of increasing levels of difficulty on a smartphone in young adults. Participants were 35 healthy young adults (mean age ± SD = 22.91 ± 3.84 years). Postural control was assessed by the CoP analysis (total excursion of the CoP (TOTEX CoP), displacements of the CoP in medial–lateral (CoP-ML) and anterior–posterior (CoP-AP) directions, mean total velocity displacement of CoP (MVELO CoP), mean displacement velocity of CoP in medial–lateral (MVELO CoP-ML) and anterior–posterior (MVELO CoP-AP) directions, and 95% confidence ellipse sway area (CEA)), the hemodynamic response by the oxyhemoglobin ([oxy-Hb]), deoxyhemoglobin ([deoxy-Hb]), and total hemoglobin ([total-Hb]) concentrations using a force plate and functional near-infrared spectroscopy (fNIR), respectively. The results showed that the difficult cognitive task while performing static postural standing caused an increase in all CoP variables in analysis (p < 0.05) and of [oxy-Hb] (p < 0.05), [deoxy-Hb] (p < 0.05) and [total-Hb] (p < 0.05) compared to the postural task. In conclusion, the increase in the cognitive demands negatively affected the performance of the postural task when performing them concurrently, compared to the postural task alone. The difficult cognitive task while performing the postural task presented a greater influence on postural sway and activation of the prefrontal cortex than the postural task and the easy cognitive task. Full article

(This article belongs to the Special Issue Advances in Technology of Brain-Computer Interface)

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19 pages, 3355 KiB

Open AccessArticle

Multi-Classification of Motor Imagery EEG Signals Using Bayesian Optimization-Based Average Ensemble Approach

by Souha Kamhi, Shuai Zhang, Mohamed Ait Amou, Mohamed Mouhafid, Imran Javaid, Isah Salim Ahmad, Isselmou Abd El Kader and Ummay Kulsum

Appl. Sci. 2022, 12(12), 5807; https://doi.org/10.3390/app12125807 - 7 Jun 2022

Cited by 6 | Viewed by 3025

Abstract

Motor Imagery (MI) classification using electroencephalography (EEG) has been extensively applied in healthcare scenarios for rehabilitation aims. EEG signal decoding is a difficult process due to its complexity and poor signal-to-noise ratio. Convolutional neural networks (CNN) have demonstrated their ability to extract time–space characteristics from EEG signals for better classification results. However, to discover dynamic correlations in these signals, CNN models must be improved. Hyperparameter choice strongly affects the robustness of CNNs. It is still challenging since the manual tuning performed by domain experts lacks the high performance needed for real-life applications. To overcome these limitations, we presented a fusion of three optimum CNN models using the Average Ensemble strategy, a method that is utilized for the first time for MI movement classification. Moreover, we adopted the Bayesian Optimization (BO) algorithm to reach the optimal hyperparameters’ values. The experimental results demonstrate that without data augmentation, our approach reached 92% accuracy, whereas Linear Discriminate Analysis, Support Vector Machine, Random Forest, Multi-Layer Perceptron, and Gaussian Naive Bayes achieved 68%, 70%, 58%, 64%, and 40% accuracy, respectively. Further, we surpassed state-of-the-art strategies on the BCI competition IV-2a multiclass MI database by a wide margin, proving the benefit of combining the output of CNN models with automated hyperparameter tuning. Full article

(This article belongs to the Special Issue Advances in Technology of Brain-Computer Interface)

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23 pages, 5792 KiB

Open AccessArticle

Application of EEG Signals Integration to Proprietary Classification Algorithms in the Implementation of Mobile Robot Control with the Use of Motor Imagery Supported by EMG Measurements

by Dawid Pawuś and Szczepan Paszkiel

Appl. Sci. 2022, 12(11), 5762; https://doi.org/10.3390/app12115762 - 6 Jun 2022

Cited by 11 | Viewed by 3339

Abstract

This article is a continuation and extension of research on a new approach to the classification and recognition of EEG signals. Their goal is to control the mobile robot through mental commands, using a measuring set such as Emotiv Epoc Flex Gel. The headset, despite its relative advancement, is rarely found in this type of research, which makes it possible to search for its advanced and innovative applications. The uniqueness of the proposed approach is the use of an EMG measuring device located on the biceps, i.e., MyoWare Muscle Sensor. This is to verify pure mental commands without additional muscle contractions. The participants of the study were asked to imagine the forearm movement that was responsible for triggering the movement command of the LEGO Mindstorms EV3 robot. The change in direction of movement is controlled by artifacts in the signal caused by the blink of an eyelid. The measured EEG signal was subjected to meticulous analysis by an expert system containing a classic classification algorithm and an artificial neural network. It was supposed to recognize mental commands, as well as detect artifacts in the form of blinking and change the direction of the robot’s movement. In addition, the system monitored the analysis of the EMG signal, detecting possible muscle tensions. The output of the expert algorithm was a control signal sent to the mobile robot. Full article

(This article belongs to the Special Issue Advances in Technology of Brain-Computer Interface)

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10 pages, 445 KiB

Open AccessArticle

Retrospective Analysis of Functional Pain among Professional Climbers

by Matuska Jakub, Jokiel Marta, Domaszewski Przemysław, Konieczny Mariusz, Pakosz Paweł, Dybek Tomasz, Wotzka Daria and Skorupska Elżbieta

Appl. Sci. 2022, 12(5), 2653; https://doi.org/10.3390/app12052653 - 4 Mar 2022

Cited by 2 | Viewed by 2093

Abstract

Climbing became one of the official Olympic sports in 2020. The nociplastic pain mechanism is indicated as important in professional sports. Functional pain, which has not been examined in climbers until now, can be an example of nociplastic pain. This study aimed to determine functional pain locations in climbers according to gender and dominant climbing style. Climbers (n = 183) and healthy subjects (n = 160) completed an online survey focused on functional pain occurrence in the head, spine, and upper limbs. The logistic regression showed that climbing predisposes one to functional pain at: Gleno-humeral joint (odds ratio (OR): 3.06; area under the curve (AUC): 0.635), elbow (OR: 2.86; AUC: 0.625), fingers (OR: 7.74; AUC: 0.733), all (p < 0.05). Among the climbers, the female gender predisposed one to pain at: GHJ (OR: 3.34; AUC: 0.638), thoracic spine (OR: 1.95; AUC: 0.580), and lumbosacral spine (OR: 1.96; AUC: 0.578), all (p < 0.05). Climbing predisposes one to functional pain development in the upper limb. While the male climbers mainly suffered from finger functional pain, the female climbers reported functional pain in the GHJ and the thoracic and lumbosacral spine. Further studies on functional pain occurrence are recommended. Full article

(This article belongs to the Special Issue Advances in Technology of Brain-Computer Interface)

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19 pages, 3257 KiB

Open AccessArticle

The Application of Integration of EEG Signals for Authorial Classification Algorithms in Implementation for a Mobile Robot Control Using Movement Imagery—Pilot Study

by Dawid Pawuś and Szczepan Paszkiel

Appl. Sci. 2022, 12(4), 2161; https://doi.org/10.3390/app12042161 - 18 Feb 2022

Cited by 14 | Viewed by 3562

Abstract

This paper presents a new approach to the issue of recognition and classification of electroencephalographic signals (EEG). A small number of investigations using the Emotiv Epoc Flex sensor set was the reason for searching for original solutions including control of elements of robotics with mental orders given by a user. The signal, measured and archived with a 32-electrode device, was prepared for classification using a new solution consisting of EEG signal integration. The new waveforms modified in this way could be subjected to recognition both by a classic authorial software and an artificial neural network. The properly classified signals made it possible to use them as the signals controlling the LEGO EV3 Mindstorms robot. Full article

(This article belongs to the Special Issue Advances in Technology of Brain-Computer Interface)

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21 pages, 2391 KiB

Open AccessArticle

Image-Based Learning Using Gradient Class Activation Maps for Enhanced Physiological Interpretability of Motor Imagery Skills

by Diego F. Collazos-Huertas, Andrés M. Álvarez-Meza and German Castellanos-Dominguez

Appl. Sci. 2022, 12(3), 1695; https://doi.org/10.3390/app12031695 - 7 Feb 2022

Cited by 5 | Viewed by 2717

Abstract

Brain activity stimulated by the motor imagery paradigm (MI) is measured by Electroencephalography (EEG), which has several advantages to be implemented with the widely used Brain–Computer Interfaces (BCIs) technology. However, the substantial inter/intra variability of recorded data significantly influences individual skills on the achieved performance. This study explores the ability to distinguish between MI tasks and the interpretability of the brain’s ability to produce elicited mental responses with improved accuracy. We develop a Deep and Wide Convolutional Neuronal Network fed by a set of topoplots extracted from the multichannel EEG data. Further, we perform a visualization technique based on gradient-based class activation maps (namely, GradCam++) at different intervals along the MI paradigm timeline to account for intra-subject variability in neural responses over time. We also cluster the dynamic spatial representation of the extracted maps across the subject set to come to a deeper understanding of MI-BCI coordination skills. According to the results obtained from the evaluated GigaScience Database of motor-evoked potentials, the developed approach enhances the physiological explanation of motor imagery in aspects such as neural synchronization between rhythms, brain lateralization, and the ability to predict the MI onset responses and their evolution during training sessions. Full article

(This article belongs to the Special Issue Advances in Technology of Brain-Computer Interface)

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11 pages, 1429 KiB

Open AccessArticle

Analysis of Upper Limbs Target-Reaching Movement and Muscle Co-Activation in Patients with First Time Stroke for Rehabilitation Progress Monitoring

by Mariusz Konieczny, Paweł Pakosz, Przemysław Domaszewski, Monika Błaszczyszyn and Aleksandra Kawala-Sterniuk

Appl. Sci. 2022, 12(3), 1551; https://doi.org/10.3390/app12031551 - 31 Jan 2022

Cited by 4 | Viewed by 2874

Abstract

In this paper, the authors analysed changes occurring during the rehabilitation processes in patients after early stroke based on analysis of their upper limbs’ target-reaching movement and muscle co-activation. Ischemic stroke often results in reduced mobility of the upper extremities and frequently is a cause for long-term disability. The ever-developing technology of 3D movement analysis and miniaturisation of equipment for testing the bioelectrical activity of muscles can help to assess the progress of rehabilitation. The aim of this study was to examine the use of analysis of target-reaching movement indicators and muscle co-activation for diagnosing the rehabilitation process in post-stroke patients. Twenty ischemic stroke patients in the early post-stroke phase (up to three months after the stroke), and twenty healthy subjects (the control group) took part in the experiments. The novel approach of the proposed research proved the usefulness of this approach in the diagnosis of the rehabilitation efficiency of rehabilitation in early post-stroke phase patients. Full article

(This article belongs to the Special Issue Advances in Technology of Brain-Computer Interface)

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Review

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10 pages, 641 KiB

Open AccessReview

Pedobarography: A Review on Methods and Practical Use in Foot Disorders

by Jacek Lorkowski, Karolina Gawronska and Mieczyslaw Pokorski

Appl. Sci. 2021, 11(22), 11020; https://doi.org/10.3390/app112211020 - 21 Nov 2021

Cited by 11 | Viewed by 4189

Abstract

Pedobarographic examination is a non-invasive method that enables the quantitative and qualitative evaluation of plantar pressure distribution, notably the plantar pressure distribution, referring to the function of the entire musculoskeletal system. This is a scoping review that aims to update knowledge on the practical use of pedobarography in foot disorders. We also attempted to systematize the methodological principles of conducting the pedobarographic examination. We searched Medline/PubMed, Embase, Web of Science, and the Cochrane Database of Systematic Reviews for the articles on the methodology of pedobarography. The search encompassed clinical trials, randomized controlled trials, meta-analyses, and reviews published in English between January 1982 and February 2021. The literature distinguishes three different types of examinations: static, postural, and dynamic. The rationale for each is presented. The review pointedly shows the unique use of pedobarography for the quantitative and qualitative evaluations of the plantar pressure distribution. It also points to the need for enhancing the awareness among medical professionals of the method and advantages it provides for patient management. Shortcomings of the method are discussed of which the difficulty in establishing the cause-and-effect relationship of foot disorders is the most disturbing as it limits the comparative verification of results of different studies. There also appears a need for developing standardized algorithmic protocols and recommendations to seamlessly perform pedobarography in clinical settings, which would help make wider use of this valuable tool. Full article

(This article belongs to the Special Issue Advances in Technology of Brain-Computer Interface)

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Other

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10 pages, 1519 KiB

Open AccessCase Report

MATLAB Analysis of SP Test Results—An Unusual Parasympathetic Nervous System Activity in Low Back Leg Pain: A Case Report

by Elzbieta Skorupska, Tomasz Dybek, Daria Wotzka, Michał Rychlik, Marta Jokiel, Paweł Pakosz, Mariusz Konieczny, Przemysław Domaszewski and Paweł Dobrakowski

Appl. Sci. 2022, 12(4), 1970; https://doi.org/10.3390/app12041970 - 14 Feb 2022

Cited by 4 | Viewed by 2223

Abstract

The Skorupska Protocol (SP) test is a new validated tool used to confirm nociplastic pain related to muscles based on a pathological autonomic nervous system (ANS) activity due to muscle nociceptive noxious stimulation analyzed automatically. Two types of amplified vasomotor response are defined as possible: vasodilatation and vasoconstriction. Until now, amplified vasodilatation among low back leg pain and/or sciatica subjects in response to the SP test was confirmed. This case report presents an unusual vasomotor response to the SP test within the pain zone of a sciatica-like case. Conducted twice, the SP test confirmed amplified vasoconstriction within the daily complaint due to noxiously stimulated muscle-referred pain for the first time. Additionally, a new type of the SP test analysis using MATLAB was presented. The SP test supported by MATLAB seems to be an interesting solution to confirm nociplastic pain related to muscles based on the pathological autonomic reactivity within the lower leg back pain zone. Further studies using the SP test supported by MATLAB are necessary to compare the SP test results with the clinical state and other types of nociplastic pain examination. Full article

(This article belongs to the Special Issue Advances in Technology of Brain-Computer Interface)

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