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Brain Activity Exploration with Non-invasive Sensor Arrays
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Brain Activity Exploration with Non-invasive Sensor Arrays

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

Deadline for manuscript submissions: closed (31 October 2021) | Viewed by 7573

Special Issue Editors

Dr. Alexei Ossadtchi

E-Mail Website
Guest Editor
Center for Bioelectric Interfaces, HSE University
Interests: Application of signal processing and statistics to the problems of non-invasive brain imaging for: Non-invasive cortical connectivity analysis, synchrony detection, solution of ill-posed inverse problems (EEG/MEG/Spectroscopy), deep brain neurofeedback, data-driven spatial filtering and beamforming, noninvasive presurgical mapping, interaction detection between several potentially epileptogenic brain regions, blind source separation, modeling of MEG/EEG signals, bootstrap statistical analysis of the evoked responses, brain–computer interface, and applications of adaptive control theory to neurofeedback
Dr. Anton Vershovskii

E-Mail Website
Guest Editor
Ioffe Institute, 194021 St. Petersburg, Russia
Interests: quantum optics; atomic spectroscopy; atomic radio-optical spectroscopy; optical quantum sensors; optical magnetometers; atomic frequency standards; nuclear magnetic gyroscopes; atomic interferometers; laser cooling; and optical sensors based on laser-cooled atoms
Dr. Guido Nolte

E-Mail Website
Guest Editor
Department of Neurophysiology and Pathophysiology, UKE, Hamburg

Special Issue Information

Dear Colleagues,

Brain activity exploration has been a part of the multinational research agenda for several decades. This has resulted in a significant amount of new knowledge, models, tools, and methods to investigate neural activity at various spatial and temporal resolution scales. Network models currently dominate and postulate the existence of dynamic functional connections between spatially distributed neuronal assemblies at a range of different scales, which manifests the need for hardware and software solutions capable of concurrently sensing the distributed neuronal populations and extracting regularities present in the measured data. 

Capitalizing on the recent explosive technological developments in material science, microfabrication, and big data analysis, it is now time for a new twist in the spiral of developing novel tools for sensing brain activity. Given the need to register the activity of neural networks whose nodes are spread across the brain volume, non-invasive whole-brain imaging approaches are of specific interest, and are capable of registering the activity of cortical and subcortical sources at various spatial and temporal resolution scales.

Dr. Alexei Ossadtchi
Dr. Anton Vershovskii
Dr. Guido Nolte
Guest Editors

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Keywords

  • functional neuroimaging
  • electroencephalography(EEG)
  • magnetoencephalography (MEG)
  • positron-emission tomography
  • functional magnetic resonance imaging
  • wearable sensor arrays
  • dry EEG electrodes
  • optical quantum sensors
  • optically pumped magnetometers
  • near-infrared spectroscopy
  • active sensing
  • ultrasound
  • cortex
  • subcortical structures
  • hemodynamics
  • electrical activity

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Published Papers (2 papers)

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Research

29 pages, 3378 KiB  
Article
A Machine Learning Approach Involving Functional Connectivity Features to Classify Rest-EEG Psychogenic Non-Epileptic Seizures from Healthy Controls
by Giuseppe Varone, Wadii Boulila, Michele Lo Giudice, Bilel Benjdira, Nadia Mammone, Cosimo Ieracitano, Kia Dashtipour, Sabrina Neri, Sara Gasparini, Francesco Carlo Morabito, Amir Hussain and Umberto Aguglia
Sensors 2022, 22(1), 129; https://doi.org/10.3390/s22010129 - 25 Dec 2021
Cited by 29 | Viewed by 4689
Abstract
Until now, clinicians are not able to evaluate the Psychogenic Non-Epileptic Seizures (PNES) from the rest-electroencephalography (EEG) readout. No EEG marker can help differentiate PNES cases from healthy subjects. In this paper, we have investigated the power spectrum density (PSD), in resting-state EEGs, [...] Read more.
Until now, clinicians are not able to evaluate the Psychogenic Non-Epileptic Seizures (PNES) from the rest-electroencephalography (EEG) readout. No EEG marker can help differentiate PNES cases from healthy subjects. In this paper, we have investigated the power spectrum density (PSD), in resting-state EEGs, to evaluate the abnormalities in PNES affected brains. Additionally, we have used functional connectivity tools, such as phase lag index (PLI), and graph-derived metrics to better observe the integration of distributed information of regular and synchronized multi-scale communication within and across inter-regional brain areas. We proved the utility of our method after enrolling a cohort study of 20 age- and gender-matched PNES and 19 healthy control (HC) subjects. In this work, three classification models, namely support vector machine (SVM), linear discriminant analysis (LDA), and Multilayer perceptron (MLP), have been employed to model the relationship between the functional connectivity features (rest-HC versus rest-PNES). The best performance for the discrimination of participants was obtained using the MLP classifier, reporting a precision of 85.73%, a recall of 86.57%, an F1-score of 78.98%, and, finally, an accuracy of 91.02%. In conclusion, our results hypothesized two main aspects. The first is an intrinsic organization of functional brain networks that reflects a dysfunctional level of integration across brain regions, which can provide new insights into the pathophysiological mechanisms of PNES. The second is that functional connectivity features and MLP could be a promising method to classify rest-EEG data of PNES form healthy controls subjects. Full article
(This article belongs to the Special Issue Brain Activity Exploration with Non-invasive Sensor Arrays)
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19 pages, 4036 KiB  
Article
Application of Empirical Mode Decomposition for Decoding Perception of Faces Using Magnetoencephalography
by Chun-Hsien Hsu and Ya-Ning Wu
Sensors 2021, 21(18), 6235; https://doi.org/10.3390/s21186235 - 17 Sep 2021
Cited by 1 | Viewed by 1911
Abstract
Neural decoding is useful to explore the timing and source location in which the brain encodes information. Higher classification accuracy means that an analysis is more likely to succeed in extracting useful information from noises. In this paper, we present the application of [...] Read more.
Neural decoding is useful to explore the timing and source location in which the brain encodes information. Higher classification accuracy means that an analysis is more likely to succeed in extracting useful information from noises. In this paper, we present the application of a nonlinear, nonstationary signal decomposition technique—the empirical mode decomposition (EMD), on MEG data. We discuss the fundamental concepts and importance of nonlinear methods when it comes to analyzing brainwave signals and demonstrate the procedure on a set of open-source MEG facial recognition task dataset. The improved clarity of data allowed further decoding analysis to capture distinguishing features between conditions that were formerly over-looked in the existing literature, while raising interesting questions concerning hemispheric dominance to the encoding process of facial and identity information. Full article
(This article belongs to the Special Issue Brain Activity Exploration with Non-invasive Sensor Arrays)
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