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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 (20 September 2022) | Viewed by 31689

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Special Issue Editors

Prof. Dr. Alexander E. Hramov

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Guest Editor

1. Baltic Center for Artificial Intelligence and Neurotechnology, Immanuel Kant Baltic Federal University, Kaliningrad 236041, Russia
2. Neuroscience and Cognitive Technology Laboratory, Innopolis University, Kazan 420500, Russia
Interests: neuroscience; nonlinear dynamics; wavelets; intelligent systems; synchronization; biomedical signal processing; neuronal networks
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Alexander N. Pisarchik

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Guest Editor

Center for Biomedical Technology, Technical University of Madrid, Campus Montegancedo, Pozuelo de Alarcón, 28223 Madrid, Spain
Interests: complex systems; bioinformatics; mathematical and computational biology; optics and photonics; biological physics; cognitive neuroscience
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The development of in vivo neuroimaging techniques has led to an incredible amount of digital information about the brain. Neuroimaging techniques are increasingly being used to study human cognitive processes, create brain–machine interfaces, and also to identify and diagnose certain brain disorders. Currently, neuroscientists and medics actively use different methods for brain scans, including electro- and magnetoencephalography (EEG/MEG), functional near-infrared spectroscopy (fNIRS), electrocorticography (ECoG), functional magnetic resonance imaging (fMRI), positron emission tomography (PET), and diffusion tensor imaging (DTI). Recent advances in signal processing and machine learning for neuroimaging data using various signal processing methods have made impressive progress in solving a number of practical tasks in medicine, healthcare, neuroscience, biomedical engineering, brain–machine interfaces, and cognitive science, to name but a few. This Special Issue aims to provide a forum for academic and industrial communities to present and discuss the latest theoretical and experimental results related to recent advances in neuroimaging data processing in terms of new theories, algorithms, architectures, and applications. We invite you to submit original papers about your research covering innovative physical, mathematical, and engineering approaches, novel computational methods, advanced technologies, and meaningful applications that could potentially improve our knowledge about brain functionality.

Prof. Dr. Alexander E. Hramov
Prof. Dr. Alexander N. Pisarchik
Guest Editors

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Keywords

MEG/EEG data processing
MEG/EEG source reconstruction
functional near-infrared spectroscopy (fNIRS)
functional magnetic resonance imaging (fMRI)
positron emission tomography (PET)
neuroimaging workflow design and data mining
invasive and noninvasive brain data analyses
brain functional connectivity restoration
brain states classification
neuroinformatics
brain–computer and brain–machine interfaces

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Published Papers (11 papers)

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Editorial

Jump to: Research

3 pages, 196 KiB

Open AccessEditorial

Special Issue “Advances in Neuroimaging Data Processing”

by Alexander. E. Hramov and Alexander. N. Pisarchik

Appl. Sci. 2023, 13(4), 2060; https://doi.org/10.3390/app13042060 - 5 Feb 2023

Viewed by 1187

Abstract

The development of in vivo neuroimaging technology has led to an incredible amount of digital information concerning the brain [...] Full article

(This article belongs to the Special Issue Advances in Neuroimaging Data Processing)

Research

Jump to: Editorial

15 pages, 2458 KiB

Open AccessArticle

Building Networks with a New Cross-Bubble Transition Entropy for Quantitative Assessment of Mental Arithmetic Electroencephalogram

by Xiaobi Chen, Guanghua Xu, Sicong Zhang, Xun Zhang and Zhicheng Teng

Appl. Sci. 2022, 12(21), 11165; https://doi.org/10.3390/app122111165 - 3 Nov 2022

Cited by 3 | Viewed by 1467

Abstract

The complex network nature of human brains has led an increasing number of researchers to adopt a complex network to assess the cognitive load. The method of constructing complex networks has a direct impact on assessment results. During the process of using the cross-permutation entropy (CPE) method to construct complex networks for cognitive load assessment, it is found that the CPE method has the shortcomings of ignoring the transition relationship between symbols and the analysis results are vulnerable to parameter settings. In order to address this issue, a new method based on the CPE principle is proposed by combining the advantages of the transition networks and the bubble entropy. From an interaction perspective, this method suggested that the node-wise out-link transition entropy of the cross-transition network between two time series is used as the edge weight to build a complex network. The proposed method was tested on the unidirectional coupled Henon model and the results demonstrated its suitability for the analysis of short time series by decreasing the influence of the embedding dimension and improving the reliability under the weak coupling conditions. The proposed method was further tested on the publicly available EEG dataset and showed significant superiority compared with the conventional CPE method. Full article

(This article belongs to the Special Issue Advances in Neuroimaging Data Processing)

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14 pages, 8623 KiB

Open AccessArticle

Structure-Function Coupling Reveals Seizure Onset Connectivity Patterns

by Christina Maher, Arkiev D’Souza, Michael Barnett, Omid Kavehei, Chenyu Wang and Armin Nikpour

Appl. Sci. 2022, 12(20), 10487; https://doi.org/10.3390/app122010487 - 18 Oct 2022

Cited by 3 | Viewed by 1847

Abstract

The implications of combining structural and functional connectivity to quantify the most active brain regions in seizure onset remain unclear. This study tested a new model that may facilitate the incorporation of diffusion MRI (dMRI) in clinical practice. We obtained structural connectomes from dMRI and functional connectomes from electroencephalography (EEG) to assess whether high structure-function coupling corresponded with the seizure onset region. We mapped individual electrodes to their nearest cortical region to allow for a one-to-one comparison between the structural and functional connectomes. A seizure laterality score and expected onset zone were defined. The patients with well-lateralised seizures revealed high structure-function coupling consistent with the seizure onset zone. However, a lower seizure lateralisation score translated to reduced alignment between the high structure-function coupling regions and the seizure onset zone. We illustrate that dMRI, in combination with EEG, can improve the identification of the seizure onset zone. Our model may be valuable in enhancing ultra-long-term monitoring by indicating optimal, individualised electrode placement. Full article

(This article belongs to the Special Issue Advances in Neuroimaging Data Processing)

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20 pages, 5231 KiB

Open AccessArticle

A Semi-Automatic Wheelchair with Navigation Based on Virtual-Real 2D Grid Maps and EEG Signals

by Ba-Viet Ngo and Thanh-Hai Nguyen

Appl. Sci. 2022, 12(17), 8880; https://doi.org/10.3390/app12178880 - 4 Sep 2022

Cited by 4 | Viewed by 2338

Abstract

A semi-automatic wheelchair allows disabled people to possibly control in an indoor environment with obstacles and targets. The paper proposes an EEG-based control system for the wheelchair based on a grid map designed to allow disabled people to reach any preset destination. In particular, the grid map is constructed by dividing it into grid cells that may contain free spaces or obstacles. The map with the grid cells is simulated to find the optimal paths to the target positions using a Deep Q-Networks (DQNs) model with the Parametric Rectified Linear Unit (PReLU) activation function, in which a novel algorithm for finding the optimal path planning by converting wheelchair actions is applied using the output parameters of the DQNs. For the wheelchair movement in one real indoor environment corresponding to the virtual 2D grid map, the initial position of the wheelchair will be determined based on natural landmarks and a graphical user interface designed for on-screen display can support disabled people in selecting the desired destination from a list of predefined locations using Electroencephalogram (EEG) signals by blinking eyes. Therefore, one user can easily and safely control the wheelchair using an EEG system to reach the desired target when the wheelchair position and destination are determined in the indoor environment. As a result, a grid map was developed and experiments for the semi-automatic wheelchair control were performed in real indoor environments to illustrate the effectiveness of the proposed method. In addition, the system is a platform to develop different types of controls depending on the types of user disabilities and different environmental maps built. Full article

(This article belongs to the Special Issue Advances in Neuroimaging Data Processing)

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17 pages, 714 KiB

Open AccessArticle

Classification of Event-Related Potentials with Regularized Spatiotemporal LCMV Beamforming

by Arne Van Den Kerchove, Arno Libert, Benjamin Wittevrongel and Marc M. Van Hulle

Appl. Sci. 2022, 12(6), 2918; https://doi.org/10.3390/app12062918 - 12 Mar 2022

Cited by 4 | Viewed by 2695

Abstract

The usability of EEG-based visual brain–computer interfaces (BCIs) based on event-related potentials (ERPs) benefits from reducing the calibration time before BCI operation. Linear decoding models, such as the spatiotemporal beamformer model, yield state-of-the-art accuracy. Although the training time of this model is generally low, it can require a substantial amount of training data to reach functional performance. Hence, BCI calibration sessions should be sufficiently long to provide enough training data. This work introduces two regularized estimators for the beamformer weights. The first estimator uses cross-validated L2-regularization. The second estimator exploits prior information about the structure of the EEG by assuming Kronecker–Toeplitz-structured covariance. The performances of these estimators are validated and compared with the original spatiotemporal beamformer and a Riemannian-geometry-based decoder using a BCI dataset with P300-paradigm recordings for 21 subjects. Our results show that the introduced estimators are well-conditioned in the presence of limited training data and improve ERP classification accuracy for unseen data. Additionally, we show that structured regularization results in lower training times and memory usage, and a more interpretable classification model. Full article

(This article belongs to the Special Issue Advances in Neuroimaging Data Processing)

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20 pages, 3748 KiB

Open AccessArticle

Neural Networks for Directed Connectivity Estimation in Source-Reconstructed EEG Data

by Axel Faes, Iris Vantieghem and Marc M. Van Hulle

Appl. Sci. 2022, 12(6), 2889; https://doi.org/10.3390/app12062889 - 11 Mar 2022

Cited by 1 | Viewed by 2221

Abstract

Directed connectivity between brain sources identified from scalp electroencephalography (EEG) can shed light on the brain’s information flows and provide a biomarker of neurological disorders. However, as volume conductance results in scalp activity being a mix of activities originating from multiple sources, the correct interpretation of their connectivity is a formidable challenge despite source localization being applied with some success. Traditional connectivity approaches rely on statistical assumptions that usually do not hold for EEG, calling for a model-free approach. We investigated several types of Artificial Neural Networks in estimating Directed Connectivity between Reconstructed EEG Sources and assessed their accuracy with respect to several ground truths. We show that a Long Short-Term Memory neural network with Non-Uniform Embedding yields the most promising results due to its relative robustness to differing dipole locations. We conclude that certain network architectures can compete with the already established methods for brain connectivity analysis. Full article

(This article belongs to the Special Issue Advances in Neuroimaging Data Processing)

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14 pages, 1115 KiB

Open AccessFeature PaperArticle

Monitoring Brain State and Behavioral Performance during Repetitive Visual Stimulation

by Alexander K. Kuc, Semen A. Kurkin, Vladimir A. Maksimenko, Alexander N. Pisarchik and Alexander E. Hramov

Appl. Sci. 2021, 11(23), 11544; https://doi.org/10.3390/app112311544 - 6 Dec 2021

Cited by 8 | Viewed by 1829

Abstract

We tested whether changes in prestimulus neural activity predict behavioral performance (decision time and errors) during a prolonged visual task. The task was to classify ambiguous stimuli—Necker cubes; manipulating the degree of ambiguity from low ambiguity (LA) to high ambiguity (HA) changed the task difficulty. First, we assumed that the observer’s state changes over time, which leads to a change in the prestimulus brain activity. Second, we supposed that the prestimulus state produces a different effect on behavioral performance depending on the task demands. Monitoring behavioral responses, we revealed that the observer’s decision time decreased for both LA and HA stimuli during the task performance. The number of perceptual errors lowered for HA, but not for LA stimuli. EEG analysis revealed an increase in the prestimulus 9–11 Hz EEG power with task time. Finally, we found associations between the behavioral and neural estimates. The prestimulus EEG power negatively correlated with the decision time for LA stimuli and the erroneous responses rate for HA stimuli. The obtained results confirm that monitoring prestimulus EEG power enables predicting perceptual performance on the behavioral level. The observed different time-on-task effects on the LA and HA stimuli processing may shed light on the features of ambiguous perception. Full article

(This article belongs to the Special Issue Advances in Neuroimaging Data Processing)

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20 pages, 4841 KiB

Open AccessArticle

MuseStudio: Brain Activity Data Management Library for Low-Cost EEG Devices

by Miguel Ángel Sánchez-Cifo, Francisco Montero and María Teresa López

Appl. Sci. 2021, 11(16), 7644; https://doi.org/10.3390/app11167644 - 20 Aug 2021

Cited by 4 | Viewed by 6288

Abstract

Collecting data allows researchers to store and analyze important information about activities, events, and situations. Gathering this information can also help us make decisions, control processes, and analyze what happens and when it happens. In fact, a scientific investigation is the way scientists use the scientific method to collect the data and evidence that they plan to analyze. Neuroscience and other related activities are set to collect their own big datasets, but to exploit their full potential, we need ways to standardize, integrate, and synthesize diverse types of data. Although the use of low-cost ElectroEncephaloGraphy (EEG) devices has increased, such as those whose price is below 300 USD, their role in neuroscience research activities has not been well supported; there are weaknesses in collecting the data and information. The primary objective of this paper was to describe a tool for data management and visualization, called MuseStudio, for low-cost devices; specifically, our tool is related to the Muse brain-sensing headband, a personal meditation assistant with additional possibilities. MuseStudio was developed in Python following the best practices in data analysis and is fully compatible with the Brain Imaging Data Structure (BIDS), which specifies how brain data must be managed. Our open-source tool can import and export data from Muse devices and allows viewing real-time brain data, and the BIDS exporting capabilities can be successfully validated following the available guidelines. Moreover, these and other functional and nonfunctional features were validated by involving five experts as validators through the DESMET method, and a latency analysis was also performed and discussed. The results of these validation activities were successful at collecting and managing electroencephalogram data. Full article

(This article belongs to the Special Issue Advances in Neuroimaging Data Processing)

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26 pages, 6136 KiB

Open AccessArticle

Kernel-Based Phase Transfer Entropy with Enhanced Feature Relevance Analysis for Brain Computer Interfaces

by Iván De La Pava Panche, Andrés Álvarez-Meza, Paula Marcela Herrera Gómez, David Cárdenas-Peña, Jorge Iván Ríos Patiño and Álvaro Orozco-Gutiérrez

Appl. Sci. 2021, 11(15), 6689; https://doi.org/10.3390/app11156689 - 21 Jul 2021

Cited by 6 | Viewed by 2918

Abstract

Neural oscillations are present in the brain at different spatial and temporal scales, and they are linked to several cognitive functions. Furthermore, the information carried by their phases is fundamental for the coordination of anatomically distributed processing in the brain. The concept of phase transfer entropy refers to an information theory-based measure of directed connectivity among neural oscillations that allows studying such distributed processes. Phase TE is commonly obtained from probability estimations carried out over data from multiple trials, which bars its use as a characterization strategy in brain–computer interfaces. In this work, we propose a novel methodology to estimate TE between single pairs of instantaneous phase time series. Our approach combines a kernel-based TE estimator defined in terms of Renyi’s

α

entropy, which sidesteps the need for probability distribution computation with phase time series obtained by complex filtering the neural signals. Besides, a kernel-alignment-based relevance analysis is added to highlight relevant features from effective connectivity-based representation supporting further classification stages in EEG-based brain–computer interface systems. Our proposal is tested on simulated coupled data and two publicly available databases containing EEG signals recorded under motor imagery and visual working memory paradigms. Attained results demonstrate how the introduced effective connectivity succeeds in detecting the interactions present in the data for the former, with statistically significant results around the frequencies of interest. It also reflects differences in coupling strength, is robust to realistic noise and signal mixing levels, and captures bidirectional interactions of localized frequency content. Obtained results for the motor imagery and working memory databases show that our approach, combined with the relevance analysis strategy, codes discriminant spatial and frequency-dependent patterns for the different conditions in each experimental paradigm, with classification performances that do well in comparison with those of alternative methods of similar nature. Full article

(This article belongs to the Special Issue Advances in Neuroimaging Data Processing)

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10 pages, 1046 KiB

Open AccessArticle

Effects of Sleep Deprivation on the Brain Electrical Activity in Mice

by Alexey N. Pavlov, Alexander I. Dubrovskii, Olga N. Pavlova and Oxana V. Semyachkina-Glushkovskaya

Appl. Sci. 2021, 11(3), 1182; https://doi.org/10.3390/app11031182 - 28 Jan 2021

Cited by 8 | Viewed by 3091

Abstract

Sleep plays a crucial role in maintaining brain health. Insufficient sleep leads to an enhanced permeability of the blood–brain barrier and the development of diseases of small cerebral vessels. In this study, we discuss the possibility of detecting changes in the electrical activity of the brain associated with sleep deficit, using an extended detrended fluctuation analysis (EDFA). We apply this approach to electroencephalograms (EEG) in mice to identify signs of changes that can be caused by short-term sleep deprivation (SD). Although the SD effect is usually subject-dependent, analysis of a group of animals shows the appearance of a pronounced decrease in EDFA scaling exponents, describing power-law correlations and the impact of nonstationarity as a fairly typical response. Using EDFA, we revealed an SD effect in 9 out of 10 mice (Mann–Whitney test,

p < 0.05

) that outperforms the DFA results (7 out of 10 mice). This tool may be a promising method for quantifying SD-induced pathological changes in the brain. Full article

(This article belongs to the Special Issue Advances in Neuroimaging Data Processing)

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12 pages, 2258 KiB

Open AccessFeature PaperArticle

Event-Related Coherence in Visual Cortex and Brain Noise: An MEG Study

by Parth Chholak, Semen A. Kurkin, Alexander E. Hramov and Alexander N. Pisarchik

Appl. Sci. 2021, 11(1), 375; https://doi.org/10.3390/app11010375 - 2 Jan 2021

Cited by 29 | Viewed by 3719

Abstract

The analysis of neurophysiological data using the two most widely used open-source MATLAB toolboxes, FieldTrip and Brainstorm, validates our hypothesis about the correlation between event-related coherence in the visual cortex and neuronal noise. The analyzed data were obtained from magnetoencephalography (MEG) experiments based on visual perception of flickering stimuli, in which fifteen subjects effectively participated. Before coherence and brain noise calculations, MEG data were first transformed from recorded channel data to brain source waveforms by solving the inverse problem. The inverse solution was obtained for a 2D cortical shape in Brainstorm and a 3D volume in FieldTrip. We found that stronger brain entrainment to the visual stimuli concurred with higher brain noise in both studies. Full article

(This article belongs to the Special Issue Advances in Neuroimaging Data Processing)

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