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Advancing Signal Processing and Analytics of EEG Signals

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A special issue of Signals (ISSN 2624-6120).

Deadline for manuscript submissions: closed (30 April 2024) | Viewed by 19124

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Special Issue Editors

Dr. Ran Xiao

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Guest Editor

School of Nursing, Duke University, Durham, NC 27708, USA
Interests: biomedical signal processing; machine learning; biomedical informatics

Dr. Quanying Liu

E-Mail Website
Guest Editor

Department of Biomedical Engineering, Southern University of Science and Technology, Shenzhen 518055, China
Interests: brain network analysis; neuroimaging; EEG source imaging; computational modelling

Special Issue Information

Dear Colleagues,

As one of the oldest clinical tools for measuring brain physiology, electroencephalography (EEG) has been adopted in a wide spectrum of brain research, such as clinical and psychiatric studies, psychology and neuroscience, brain–computer interface, and so on. Despite significant advancements in EEG research in recent decades, there is still room for improvement on processing approaches and computational methods to improve EEG signal quality and make new discoveries.

This Special Issue of Signals aims to communicate technological and methodological innovations toward knowledge discovery using EEG.

Suitable topics include but are not limited to the following:

Development of novel biomedical signal processing techniques or approaches to improve EEG signal quality;
Application of cutting-edge AI, machine learning, or deep learning algorithms to advance EEG-related research;
Improvements to the performance of EEG-based brain–computer interfaces;
Biomarker discovery in EEG associated with infant/child brain development;
Methodological innovation to facilitate endpoint detection or prediction using EEG, e.g., seizure detection and person identification;
Advances in the understanding of complex brain networks through computational neuroscience using EEG.

Dr. Ran Xiao
Dr. Quanying Liu
Guest Editors

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. Signals is an international peer-reviewed open access quarterly 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 1000 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

signal processing
artificial intelligence
machine learning
deep learning
brain–computer interface (BCI)
developmental EEG
computational neuroscience
brain networks

Published Papers (11 papers)

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Research

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17 pages, 2266 KiB

Open AccessArticle

CNN-Based Pattern Classifiers for Precise Identification of Perinatal EEG Biomarkers of Brain Injury in Preterm Neonates

by Hamid Abbasi, Malcolm R. Battin, Deborah Rowe, Robyn Butler, Alistair J. Gunn and Laura Bennet

Signals 2024, 5(2), 264-280; https://doi.org/10.3390/signals5020014 (registering DOI) - 28 Apr 2024

Viewed by 210

Abstract

Electroencephalographic (EEG) monitoring is important for the diagnosis of hypoxic-ischemic (HI) brain injury in high-risk preterm infants. EEG monitoring is limited by the reliance on expert clinical observation. However, high-risk preterm infants often do not present observable symptoms due to their frailty. Thus, there is an urgent need to find better ways to automatically quantify changes in the EEG these high-risk babies. This article is a first step towards this goal. This innovative study demonstrates the effectiveness of deep Convolutional Neural Networks (CNN) pattern classifiers, trained on spectrally-detailed Wavelet Scalograms (WS) images derived from neonatal EEG sharp waves—a potential translational HI biomarker, at birth. The WS-CNN classifiers exhibit outstanding performance in identifying HI sharp waves within an exclusive clinical EEG recordings dataset of preterm infants immediately after birth. The work has impact as it demonstrates exceptional high accuracy of 99.34 ± 0.51% cross-validated across 13,624 EEG patterns over 48 h raw EEG at low 256 Hz clinical sampling rates. Furthermore, the WS-CNN pattern classifier is able to accurately identify the sharp-waves within the most critical first hours of birth (n = 8, 4:36 ± 1:09 h), regardless of potential morphological changes influenced by different treatments/drugs or the evolutionary ‘timing effects’ of the injury. This underscores its reliability as a tool for the identification and quantification of clinical EEG sharp-wave biomarkers at bedside. Full article

(This article belongs to the Special Issue Advancing Signal Processing and Analytics of EEG Signals)

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22 pages, 3319 KiB

Open AccessArticle

The Effect of Jittered Stimulus Onset Interval on Electrophysiological Markers of Attention in a Brain–Computer Interface Rapid Serial Visual Presentation Paradigm

by Daniel Klee, Tab Memmott and Barry Oken

Signals 2024, 5(1), 18-39; https://doi.org/10.3390/signals5010002 - 09 Jan 2024

Viewed by 798

Abstract

Brain responses to discrete stimuli are modulated when multiple stimuli are presented in sequence. These alterations are especially pronounced when the time course of an evoked response overlaps with responses to subsequent stimuli, such as in a rapid serial visual presentation (RSVP) paradigm used to control a brain–computer interface (BCI). The present study explored whether the measurement or classification of select brain responses during RSVP would improve through application of an established technique for dealing with overlapping stimulus presentations, known as irregular or “jittered” stimulus onset interval (SOI). EEG data were collected from 24 healthy adult participants across multiple rounds of RSVP calibration and copy phrase tasks with varying degrees of SOI jitter. Analyses measured three separate brain signals sensitive to attention: N200, P300, and occipitoparietal alpha attenuation. Presentation jitter visibly reduced intrusion of the SSVEP, but in general, it did not positively or negatively affect attention effects, classification, or system performance. Though it remains unclear whether stimulus overlap is detrimental to BCI performance overall, the present study demonstrates that single-trial classification approaches may be resilient to rhythmic intrusions like SSVEP that appear in the averaged EEG. Full article

(This article belongs to the Special Issue Advancing Signal Processing and Analytics of EEG Signals)

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17 pages, 6326 KiB

Open AccessArticle

Quantitative Electroencephalography: Cortical Responses under Different Postural Conditions

by Marco Ivaldi, Lorenzo Giacometti and David Conversi

Signals 2023, 4(4), 708-724; https://doi.org/10.3390/signals4040039 - 18 Oct 2023

Viewed by 771

Abstract

In this study, the alpha and beta spectral frequency bands and amplitudes of EEG signals recorded from 10 healthy volunteers using an experimental cap with neoprene jacketed electrodes were analysed. Background: One of the main limitations in the analysis of EEG signals during movement is the presence of artefacts due to cranial muscle contraction; the objectives of this study therefore focused on two main aspects: (1) validating a tool capable of decreasing movement artefacts, while developing a reliable method for the quantitative analysis of EEG data; (2) using this method to analyse the EEG signal recorded during a particular motor activity (bi- and monopodalic postural control). Methods: The EEG sampling frequency was 512 Hz; the signal was acquired on 16 channels with monopolar montage and the reference on Cz. The recorded signals were processed using a specifically written Matlab script and also by exploiting open-source software (Eeglab). Results: The procedure used showed excellent reliability, allowing for a significant decrease in movement artefacts even during motor tasks performed both with eyes open and with eyes closed. Conclusions: This preliminary study lays the foundation for correctly recording EEG signals as an additional source of information in the study of human movement. Full article

(This article belongs to the Special Issue Advancing Signal Processing and Analytics of EEG Signals)

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14 pages, 2039 KiB

Open AccessArticle

Early Signatures of Brain Injury in the Preterm Neonatal EEG

by Hamid Abbasi, Malcolm R. Battin, Robyn Butler, Deborah Rowe, Benjamin A. Lear, Alistair J. Gunn and Laura Bennet

Signals 2023, 4(3), 630-643; https://doi.org/10.3390/signals4030034 - 06 Sep 2023

Cited by 2 | Viewed by 1128

Abstract

Reliable prognostic biomarkers are needed to support the early diagnosis of brain injury in extremely preterm infants, and to develop effective neuroprotective protocols that are tailored to the progressing phases of injury. Experimental and clinical research shows that severity of neuronal damage is correlated with changes in the electroencephalogram (EEG) after hypoxic-ischemia (HI). We have previously reported that micro-scale sharp-wave EEG waveforms have prognostic utility within the early hours of post-HI recordings in preterm fetal sheep, before injury develops. This article aims to investigate whether these subtle EEG patterns are translational in the early hours of life in clinical recordings from extremely preterm newborns. This work evaluates the existence and morphological similarity of the sharp-waves automatically identified throughout the entire duration of EEG data from a cohort of fetal sheep 6 h after HI (n = 7, at 103 ± 1 day gestation) and in recordings commencing before 6 h of life in extremely preterm neonates (n = 7, 27 ± 2.0 weeks gestation). We report that micro-scale EEG waveforms with similar morphology and characteristics (r = 0.94) to those seen in fetal sheep after HI are also present after birth in recordings started before 6 h of life in extremely preterm neonates. This work further indicates that the post-HI sharp-waves show rapid morphological evolution, influenced by age and/or severity of neuronal loss, and thus that automated algorithms should be validated against such signal variations. Finally, this article discusses the need for more focused research on the early assessment of EEG changes in preterm infants to help determine the timing of brain injury to identify biomarkers that could assist in targeting novel therapies for particular phases of injury. Full article

(This article belongs to the Special Issue Advancing Signal Processing and Analytics of EEG Signals)

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18 pages, 4316 KiB

Open AccessArticle

Beyond Frequency Band Constraints in EEG Analysis: The Role of the Mode Decomposition in Pushing the Boundaries

by Eduardo Arrufat-Pié, Mario Estévez-Báez, José Mario Estévez-Carreras, Gerry Leisman, Calixto Machado and Carlos Beltrán-León

Signals 2023, 4(3), 489-506; https://doi.org/10.3390/signals4030026 - 05 Jul 2023

Viewed by 1233

Abstract

This study investigates the use of empirical mode decomposition (EMD) to extract intrinsic mode functions (IMFs) for the spectral analysis of EEG signals in healthy individuals and its possible biological interpretations. Unlike traditional EEG analysis, this approach does not require the establishment of arbitrary band limits. The study uses a multivariate EMD algorithm (APIT-MEMD) to extract IMFs from the EEG signals of 34 healthy volunteers. The first six IMFs are analyzed using two different methods, based on FFT and HHT, and the results compared using the ANOVA test and the Bland–Altman method for agreement test. The outcomes show that the frequency values of the first six IMFs fall within the range of classic EEG bands (1.72–52.4 Hz). Although there was a lack of agreement in the mean weighted frequency values of the first three IMFs between the two methods (>3 Hz), both methods showed similar results for power spectral density (<5% normalized units, %, of power spectral density). The HHT method is found to have better frequency resolution than APIT-MEMD associated with FTT that produce less overlapping between IMF3 and 4 (p = 0.0046) and it is recommended for analyzing the spectral properties of IMFs. The study concludes that the HHT method could help to avoid the assumption of strict frequency band limits, and that the potential impact of EEG physiological phenomenon on mode-mixing interpretation, particularly for the alpha and theta ranges, must be considered in future research. Full article

(This article belongs to the Special Issue Advancing Signal Processing and Analytics of EEG Signals)

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16 pages, 3345 KiB

Open AccessArticle

A Sparse Multiclass Motor Imagery EEG Classification Using 1D-ConvResNet

by Harshini Gangapuram and Vidya Manian

Signals 2023, 4(1), 235-250; https://doi.org/10.3390/signals4010013 - 14 Mar 2023

Viewed by 1745

Abstract

Multiclass motor imagery classification is essential for brain–computer interface systems such as prosthetic arms. The compressive sensing of EEG helps classify brain signals in real-time, which is necessary for a BCI system. However, compressive sensing is limited, despite its flexibility and data efficiency, because of its sparsity and high computational cost in reconstructing signals. Although the constraint of sparsity in compressive sensing has been addressed through neural networks, its signal reconstruction remains slow, and the computational cost increases to classify the signals further. Therefore, we propose a 1D-Convolutional Residual Network that classifies EEG features in the compressed (sparse) domain without reconstructing the signal. First, we extract only wavelet features (energy and entropy) from raw EEG epochs to construct a dictionary. Next, we classify the given test EEG data based on the sparse representation of the dictionary. The proposed method is computationally inexpensive, fast, and has high classification accuracy as it uses a single feature to classify without preprocessing. The proposed method is trained, validated, and tested using multiclass motor imagery data of 109 subjects from the PhysioNet database. The results demonstrate that the proposed method outperforms state-of-the-art classifiers with 96.6% accuracy. Full article

(This article belongs to the Special Issue Advancing Signal Processing and Analytics of EEG Signals)

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14 pages, 588 KiB

Open AccessArticle

Manual 3D Control of an Assistive Robotic Manipulator Using Alpha Rhythms and an Auditory Menu: A Proof-of-Concept

by Ana S. Santos Cardoso, Rasmus L. Kæseler, Mads Jochumsen and Lotte N. S. Andreasen Struijk

Signals 2022, 3(2), 396-409; https://doi.org/10.3390/signals3020024 - 16 Jun 2022

Cited by 1 | Viewed by 1934

Abstract

Brain–Computer Interfaces (BCIs) have been regarded as potential tools for individuals with severe motor disabilities, such as those with amyotrophic lateral sclerosis, that render interfaces that rely on movement unusable. This study aims to develop a dependent BCI system for manual end-point control of a robotic arm. A proof-of-concept system was devised using parieto-occipital alpha wave modulation and a cyclic menu with auditory cues. Users choose a movement to be executed and asynchronously stop said action when necessary. Tolerance intervals allowed users to cancel or confirm actions. Eight able-bodied subjects used the system to perform a pick-and-place task. To investigate the potential learning effects, the experiment was conducted twice over the course of two consecutive days. Subjects obtained satisfactory completion rates (84.0 ± 15.0% and 74.4 ± 34.5% for the first and second day, respectively) and high path efficiency (88.9 ± 11.7% and 92.2 ± 9.6%). Subjects took on average 439.7 ± 203.3 s to complete each task, but the robot was only in motion 10% of the time. There was no significant difference in performance between both days. The developed control scheme provided users with intuitive control, but a considerable amount of time is spent waiting for the right target (auditory cue). Implementing other brain signals may increase its speed. Full article

(This article belongs to the Special Issue Advancing Signal Processing and Analytics of EEG Signals)

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Review

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15 pages, 1470 KiB

Open AccessReview

Approaching Electroencephalographic Pathological Spikes in Terms of Solitons

by Arturo Tozzi

Signals 2024, 5(2), 281-295; https://doi.org/10.3390/signals5020015 (registering DOI) - 01 May 2024

Viewed by 215

Abstract

A delicate balance between dissipative and nonlinear forces allows traveling waves termed solitons to preserve their shape and energy for long distances without steepening and flattening out. Solitons are so widespread that they can generate both destructive waves on oceans’ surfaces and noise-free message propagation in silica optic fibers. They are naturally observed or artificially produced in countless physical systems at very different coarse-grained scales, from solar winds to Bose–Einstein condensates. We hypothesize that some of the electric oscillations detectable by scalp electroencephalography (EEG) could be assessed in terms of solitons. A nervous spike must fulfill strict mathematical and physical requirements to be termed a soliton. They include the proper physical parameters like wave height, horizontal distance and unchanging shape; the appropriate nonlinear wave equations’ solutions and the correct superposition between sinusoidal and non-sinusoidal waves. After a thorough analytical comparison with the EEG traces available in the literature, we argue that solitons bear striking similarities with the electric activity recorded from medical conditions like epilepsies and encephalopathies. Emerging from the noisy background of the normal electric activity, high-amplitude, low-frequency EEG soliton-like pathological waves with relatively uniform morphology and duration can be observed, characterized by repeated, stereotyped patterns propagating on the hemispheric surface of the brain over relatively large distances. Apart from the implications for the study of cognitive activities in the healthy brain, the theoretical possibility to treat pathological brain oscillations in terms of solitons has powerful operational implications, suggesting new therapeutical options to counteract their detrimental effects. Full article

(This article belongs to the Special Issue Advancing Signal Processing and Analytics of EEG Signals)

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14 pages, 1189 KiB

Open AccessReview

A Review of Online Classification Performance in Motor Imagery-Based Brain–Computer Interfaces for Stroke Neurorehabilitation

by Athanasios Vavoulis, Patricia Figueiredo and Athanasios Vourvopoulos

Signals 2023, 4(1), 73-86; https://doi.org/10.3390/signals4010004 - 20 Jan 2023

Cited by 7 | Viewed by 3090

Abstract

Motor imagery (MI)-based brain–computer interfaces (BCI) have shown increased potential for the rehabilitation of stroke patients; nonetheless, their implementation in clinical practice has been restricted due to their low accuracy performance. To date, although a lot of research has been carried out in benchmarking and highlighting the most valuable classification algorithms in BCI configurations, most of them use offline data and are not from real BCI performance during the closed-loop (or online) sessions. Since rehabilitation training relies on the availability of an accurate feedback system, we surveyed articles of current and past EEG-based BCI frameworks who report the online classification of the movement of two upper limbs in both healthy volunteers and stroke patients. We found that the recently developed deep-learning methods do not outperform the traditional machine-learning algorithms. In addition, patients and healthy subjects exhibit similar classification accuracy in current BCI configurations. Lastly, in terms of neurofeedback modality, functional electrical stimulation (FES) yielded the best performance compared to non-FES systems. Full article

(This article belongs to the Special Issue Advancing Signal Processing and Analytics of EEG Signals)

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10 pages, 2623 KiB

Open AccessReview

A Survey on Denoising Techniques of Electroencephalogram Signals Using Wavelet Transform

by Maximilian Grobbelaar, Souvik Phadikar, Ebrahim Ghaderpour, Aaron F. Struck, Nidul Sinha, Rajdeep Ghosh and Md. Zaved Iqubal Ahmed

Signals 2022, 3(3), 577-586; https://doi.org/10.3390/signals3030035 - 17 Aug 2022

Cited by 23 | Viewed by 3747

Abstract

Electroencephalogram (EEG) artifacts such as eyeblink, eye movement, and muscle movements widely contaminate the EEG signals. Those unwanted artifacts corrupt the information contained in the EEG signals and degrade the performance of qualitative analysis of clinical applications and as well as EEG-based brain–computer interfaces (BCIs). The applications of wavelet transform in denoising EEG signals are increasing day by day due to its capability of handling non-stationary signals. All the reported wavelet denoising techniques for EEG signals are surveyed in this paper in terms of the quality of noise removal and retrieving important information. In order to evaluate the performance of wavelet denoising techniques for EEG signals and to express the quality of reconstruction, the techniques were evaluated based on the results shown in the respective literature. We also compare certain features in the evaluation of the wavelet denoising techniques, such as the requirement of reference channel, automation, online, and performance on a single channel. Full article

(This article belongs to the Special Issue Advancing Signal Processing and Analytics of EEG Signals)

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Other

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9 pages, 2639 KiB

Open AccessCase Report

Case Report: Modulation of Effective Connectivity in Brain Networks after Prosthodontic Tooth Loss Repair

by Antonella Muroni, Daniel Barbar, Matteo Fraschini, Marco Monticone, Giovanni Defazio and Francesco Marrosu

Signals 2022, 3(3), 550-558; https://doi.org/10.3390/signals3030033 - 05 Aug 2022

Cited by 1 | Viewed by 1920

Abstract

INTRODUCTION. Recent neuroimaging studies suggest that dental loss replacements induce changes in neuroplasticity as well as in correlated connectivity between brain networks. However, as the typical temporal delay in detecting brain activity by neuroimaging cannot account for the influence one neural system exerts over another in a context of real activation (“effective” connectivity), it seems of interest to approach this dynamic aspect of brain networking in the time frame of milliseconds by exploiting electroencephalographic (EEG) data. MATERIAL AND METHODS. The present study describes one subject who received a new prosthodontic provisional implant in substitution for previous dental repairs. Two EEG sessions led with a portable device were recorded before and after positioning the new dental implant. By following MATLAB-EEGLAB processing supported by the plugins FIELDTRIP and SIFT, the independent component analysis (ICA) derived from EEG raw signals was rendered as current density fields and interpolated with the dipoles generated by each electrode for a dynamic study of the effective connectivity. One more recording session was undertaken six months after the placement of the final implant. RESULTS. Compared to the baseline, the new prosthodontic implant induced a novel modulation of the neuroplasticity in sensory-motor areas which was maintained following the definitive implant after six months, as revealed by changes in the effective connectivity from the basal strong enslavement of a single brain area over the others, to an equilibrate inter-related connectivity evenly distributed along the frontotemporal regions of both hemispheres. CONCLUSIONS. The rapid shift of the effective connectivity after positioning the new prosthodontic implant and its substantial stability after six months suggest the possibility that synaptic modifications, induced by novel sensory motor conditions, modulate the neuroplasticity and reshape the final dynamic frame of the interarea connectivity. Moreover, given the viability of the EEG practice, this approach could be of some interest in assessing the association between oral pathophysiology and neuronal networking. Full article

(This article belongs to the Special Issue Advancing Signal Processing and Analytics of EEG Signals)

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