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Advances in EEG Brain Dynamics

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A special issue of Brain Sciences (ISSN 2076-3425). This special issue belongs to the section "Computational Neuroscience and Neuroinformatics".

Deadline for manuscript submissions: closed (30 November 2022) | Viewed by 23558

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Special Issue Editors

Dr. Pierpaolo Croce

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1. Department of Neuroscience, Imaging and Clinical Sciences, Gabriele d’Annunzio University, Via Luigi Polacchi 11, 66100 Chieti, Italy
2. Institute for Advanced Biomedical Technologies, “G. d’Annunzio” University, 66100 Chieti, Italy
Interests: EEG/MEG; deep learning; machine learning; fMRI

Dr. Lorenzo Ricci

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Unit of Neurology, Neurophysiology, Neurobiology, University Campus Bio-Medico of Rome, 00128 Rome, Italy
Interests: epilepsy; neurophysiology; pediatric neurology

Dr. Eleonora Tamilia

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1. Fetal Neonatal Neuroimaging and Developmental Science Center, Department of Pediatrics, Boston Children’s Hospital, Boston, MA 02115, USA
2. Division of Newborn Medicine, Boston Children’s Hospital, Harvard Medical School, Boston, MA 02115, USA
Interests: epilepsy surgery; neurophysiology; neuroimaging; signal processing

Special Issue Information

Dear Colleagues,

In recent years we have witnessed a growing interest in the development of new technologies and analytical methods to understand the brain and functional dynamics of EEG activity. The aim of the present special issue is to collect up to date research and works on new and innovative technologies and analysis methods utilized for computational EEG studies in health and disease. Authors are invited to submit cutting-edge research and reviews that address a broad range of topics related to EEG/MEG and brain dynamics: novel methodological approaches to EEG analysis, early diagnosis of neurological diseases, new technologies (e.g., eye-tracking, iEEG, multimodal approaches, wearable sensors), biomarkers, and healthy neuropsychological states.

We are especially interested in new research works focused on the use of EEG signals (also in combination with other modalities) for the evaluation of both health and neurological disorders and/or validation of diagnostic and/or prognostic biomarkers.

Dr. Pierpaolo Croce
Dr. Lorenzo Ricci
Dr. Eleonora Tamilia
Guest Editors

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Keywords

EEG
MEG
brain dynamics
electrical neuroimaging
neurophysiology
computational methods
brain source imaging

Published Papers (11 papers)

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Research

16 pages, 3336 KiB

Open AccessArticle

Time-Frequency Analysis of Somatosensory Evoked High-Frequency (600 Hz) Oscillations as an Early Indicator of Arousal Recovery after Hypoxic-Ischemic Brain Injury

by Ze Ou, Yu Guo, Payam Gharibani, Ariel Slepyan, Denis Routkevitch, Anastasios Bezerianos, Romergryko G. Geocadin and Nitish V. Thakor

Brain Sci. 2023, 13(1), 2; https://0-doi-org.brum.beds.ac.uk/10.3390/brainsci13010002 - 20 Dec 2022

Viewed by 1518

Abstract

Cardiac arrest (CA) remains the leading cause of coma, and early arousal recovery indicators are needed to allocate critical care resources properly. High-frequency oscillations (HFOs) of somatosensory evoked potentials (SSEPs) have been shown to indicate responsive wakefulness days following CA. Nonetheless, their potential in the acute recovery phase, where the injury is reversible, has not been tested. We hypothesize that time-frequency (TF) analysis of HFOs can determine arousal recovery in the acute recovery phase. To test our hypothesis, eleven adult male Wistar rats were subjected to asphyxial CA (five with 3-min mild and six with 7-min moderate to severe CA) and SSEPs were recorded for 60 min post-resuscitation. Arousal level was quantified by the neurological deficit scale (NDS) at 4 h. Our results demonstrated that continuous wavelet transform (CWT) of SSEPs localizes HFOs in the TF domain under baseline conditions. The energy dispersed immediately after injury and gradually recovered. We proposed a novel TF-domain measure of HFO: the total power in the normal time-frequency space (NTFS) of HFO. We found that the NTFS power significantly separated the favorable and unfavorable outcome groups. We conclude that the NTFS power of HFOs provides earlier and objective determination of arousal recovery after CA. Full article

(This article belongs to the Special Issue Advances in EEG Brain Dynamics)

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17 pages, 2060 KiB

Open AccessArticle

DSCNN-LSTMs: A Lightweight and Efficient Model for Epilepsy Recognition

by Zhentao Huang, Yahong Ma, Rongrong Wang, Baoxi Yuan, Rui Jiang, Qin Yang, Weisu Li and Jingbo Sun

Brain Sci. 2022, 12(12), 1672; https://0-doi-org.brum.beds.ac.uk/10.3390/brainsci12121672 - 05 Dec 2022

Cited by 5 | Viewed by 1672

Abstract

Epilepsy is the second most common disease of the nervous system. Because of its high disability rate and the long course of the disease, it is a worldwide medical problem and social public health problem. Therefore, the timely detection and treatment of epilepsy are very important. Currently, medical professionals use their own diagnostic experience to identify seizures by visual inspection of the electroencephalogram (EEG). Not only does it require a lot of time and effort, but the process is also very cumbersome. Machine learning-based methods have recently been proposed for epilepsy detection, which can help clinicians make rapid and correct diagnoses. However, these methods often require extracting the features of EEG signals before using the data. In addition, the selection of features often requires domain knowledge, and feature types also have a significant impact on the performance of the classifier. In this paper, a one-dimensional depthwise separable convolutional neural network and long short-term memory networks (1D DSCNN-LSTMs) model is proposed to identify epileptic seizures by autonomously extracting the features of raw EEG. On the UCI dataset, the performance of the proposed 1D DSCNN-LSTMs model is verified by cross-validation and time complexity comparison. Compared with other previous models, the experimental results show that the highest recognition rates of binary and quintuple classification are 99.57% and 81.30%, respectively. It can be concluded that the 1D DSCNN-LSTMs model proposed in this paper is an effective method to identify seizures based on EEG signals. Full article

(This article belongs to the Special Issue Advances in EEG Brain Dynamics)

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18 pages, 1734 KiB

Open AccessArticle

EEG Network Analysis in Epilepsy with Generalized Tonic–Clonic Seizures Alone

by Dimitrios Pitetzis, Christos Frantzidis, Elizabeth Psoma, Georgia Deretzi, Anna Kalogera-Fountzila, Panagiotis D. Bamidis and Martha Spilioti

Brain Sci. 2022, 12(11), 1574; https://0-doi-org.brum.beds.ac.uk/10.3390/brainsci12111574 - 18 Nov 2022

Cited by 1 | Viewed by 1582

Abstract

Many contradictory theories regarding epileptogenesis in idiopathic generalized epilepsy have been proposed. This study aims to define the network that takes part in the formation of the spike-wave discharges in patients with generalized tonic–clonic seizures alone (GTCSa) and elucidate the network characteristics. Furthermore, we intend to define the most influential brain areas and clarify the connectivity pattern among them. The data were collected from 23 patients with GTCSa utilizing low-density electroencephalogram (EEG). The source localization of generalized spike-wave discharges (GSWDs) was conducted using the Standardized low-resolution brain electromagnetic tomography (sLORETA) methodology. Cortical connectivity was calculated utilizing the imaginary part of coherence. The network characteristics were investigated through small-world propensity and the integrated value of influence (IVI). Source localization analysis estimated that most sources of GSWDs were in the superior frontal gyrus and anterior cingulate. Graph theory analysis revealed that epileptic sources created a network that tended to be regularized during generalized spike-wave activity. The IVI analysis concluded that the most influential nodes were the left insular gyrus and the left inferior parietal gyrus at 3 and 4 Hz, respectively. In conclusion, some nodes acted mainly as generators of GSWDs and others as influential ones across the whole network. Full article

(This article belongs to the Special Issue Advances in EEG Brain Dynamics)

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15 pages, 4398 KiB

Open AccessArticle

A Method for the Study of Cerebellar Cognitive Function—Re-Cognition and Validation of Error-Related Potentials

by Bo Mu, Chang Niu, Jingping Shi, Rumei Li, Chao Yu and Kuiying Yin

Brain Sci. 2022, 12(9), 1173; https://0-doi-org.brum.beds.ac.uk/10.3390/brainsci12091173 - 01 Sep 2022

Cited by 1 | Viewed by 1440

Abstract

The cerebellar region has four times as many brain cells as the brain, but whether the cerebellum functions in cognition, and how it does so, remain unexplored. In order to verify whether the cerebellum is involved in cognition, we chose to investigate whether the cerebellum is involved in the process of error judgment. We designed an experiment in which we could activate the subject’s error-related potentials (ErrP). We recruited 26 subjects and asked them to wear EEG caps with cerebellar regions designed by us to participate in the experiment so that we could record their EEG activity throughout the experiment. We successfully mitigated the majority of noise interference after a series of pre-processing of the data collected from each subject. Our analysis of the preprocessed data revealed that our experiment successfully activated ErrP, and that the EEG signals, including the cerebellum, were significantly different when subjects made errors compared to when they made correct judgments. We designed a feature extraction method that requires selecting channels with large differences under different classifications, firstly by extracting the time-frequency features of these channels, and then screening these features with sequence backward feature (SBS) selection. We use the extracted features as the input and different event types in EEG data as the labels for multiple classifiers to classify the data in the executive and feedback segments, where the average accuracy for two-class classification of executive segments can reach 80.5%. The major contribution of our study is the discovery of the presence of ErrP in cerebellar regions and the extraction of an effective feature extraction method for EEG data. Full article

(This article belongs to the Special Issue Advances in EEG Brain Dynamics)

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16 pages, 2460 KiB

Open AccessArticle

Is Cortical Theta-Gamma Phase-Amplitude Coupling Memory-Specific?

by Orestis Papaioannou, Laura P. Crespo, Kailey Clark, Nicole N. Ogbuagu, Luz Maria Alliende, Steven M. Silverstein and Molly A. Erickson

Brain Sci. 2022, 12(9), 1131; https://0-doi-org.brum.beds.ac.uk/10.3390/brainsci12091131 - 25 Aug 2022

Cited by 3 | Viewed by 2193

Abstract

One of the proposed neural mechanisms involved in working memory is coupling between the theta phase and gamma amplitude. For example, evidence from intracranial recordings shows that coupling between hippocampal theta and cortical gamma oscillations increases selectively during working memory tasks. Theta-gamma phase-amplitude coupling can also be measured non-invasively through scalp EEG; however, EEG can only assess coupling within cortical areas, and it is not yet clear if this cortical-only coupling is truly memory-specific, or a more general phenomenon. We tested this directly by measuring cortical coupling during three different conditions: a working memory task, an attention task, and a passive perception condition. We find similar levels of theta-gamma coupling in all three conditions, suggesting that cortical theta-gamma phase-amplitude coupling is not a memory-specific signal, but instead reflects some other attentional or perceptual processes. Implications for understanding the brain dynamics of visual working memory are discussed. Full article

(This article belongs to the Special Issue Advances in EEG Brain Dynamics)

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9 pages, 2623 KiB

Open AccessArticle

Assessment of 3D Visual Discomfort Based on Dynamic Functional Connectivity Analysis with HMM in EEG

by Zhiying Long, Lu Liu, Xuefeng Yuan, Yawen Zheng, Yantong Niu and Li Yao

Brain Sci. 2022, 12(7), 937; https://0-doi-org.brum.beds.ac.uk/10.3390/brainsci12070937 - 18 Jul 2022

Cited by 2 | Viewed by 1768

Abstract

Stereoscopic displays can induce visual discomfort despite their wide application. Electroencephalography (EEG) technology has been applied to assess 3D visual discomfort, because it can capture brain activities with high temporal resolution. Previous studies explored the frequency and temporal features relevant to visual discomfort in EEG data. Recently, it was demonstrated that functional connectivity between brain regions fluctuates with time. However, the relationship between 3D visual discomfort and dynamic functional connectivity (DFC) remains unknown. Although HMM showed advantages over the sliding window method in capturing the temporal fluctuations of DFC at a single time point in functional magnetic resonance imaging (fMRI) data, it is unclear whether HMM works well in revealing the time-varying functional connectivity of EEG data. In this study, the hidden Markov model (HMM) was introduced to DFC analysis of EEG data for the first time and was used to investigate the DFC features that can be used to assess 3D visual discomfort. The results indicated that state 2, with strong connections between electrodes, occurred more frequently in the early period, whereas state 4, with overall weak connections between electrodes, occurred more frequently in the late period for both visual comfort and discomfort stimuli. Moreover, the 3D visual discomfort stimuli caused subjects to stay in state 4 more frequently, especially in the later period, in contrast to the 3D visual comfort stimuli. The results suggest that the increasing occurrence of state 4 was possibly related to visual discomfort and that the occurrence frequency of state 4 may be used to assess visual discomfort. Full article

(This article belongs to the Special Issue Advances in EEG Brain Dynamics)

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18 pages, 6306 KiB

Open AccessArticle

Feasibility of EEG Phase-Amplitude Coupling to Stratify Encephalopathy Severity in Neonatal HIE Using Short Time Window

by Xinlong Wang, Hanli Liu, Eric B. Ortigoza, Srinivas Kota, Yulun Liu, Rong Zhang and Lina F. Chalak

Brain Sci. 2022, 12(7), 854; https://0-doi-org.brum.beds.ac.uk/10.3390/brainsci12070854 - 29 Jun 2022

Cited by 3 | Viewed by 1666

Abstract

Goal: It is challenging to clinically discern the severity of neonatal hypoxic ischemic encephalopathy (HIE) within hours after birth in time for therapeutic decision-making for hypothermia. The goal of this study was to determine the shortest duration of the EEG based PAC index to provide real-time guidance for clinical decision-making for neonates with HIE. Methods: Neonates were recruited from a single-center Level III NICU between 2017 and 2019. A time-dependent, PAC-frequency-averaged index, tPAC_m, was calculated to characterize intrinsic coupling between the amplitudes of 12–30 Hz and the phases of 1–2 Hz oscillation from 6-h EEG data at electrode P3 during the first day of life, using different sizes of moving windows including 10 s, 20 s, 1 min, 2 min, 5 min, 10 min, 20 min, 30 min, 60 min, and 120 min. Time-dependent receiver operating characteristic (ROC) curves were generated to examine the performance of the accurate window tPAC_m as a neurophysiologic biomarker. Results: A total of 33 neonates (mild-HIE, n = 15 and moderate/severe HIE, n = 18) were enrolled. Mixed effects models demonstrated that tPAC_m between the two groups was significantly different with window time segments of 3–120 min. By observing the estimates of group differences in tPAC_m across different window sizes, we found 20 min was the shortest window size to optimally distinguish the two groups (p < 0.001). Time-varying ROC showed significant average area-under-the-curve of 0.82. Conclusions: We demonstrated the feasibility of using tPAC_m with a 20 min EEG time window to differentiate the severity of HIE and facilitate earlier diagnosis and treatment initiation. Full article

(This article belongs to the Special Issue Advances in EEG Brain Dynamics)

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13 pages, 803 KiB

Open AccessArticle

Compensated Integrated Gradients for Reliable Explanation of Electroencephalogram Signal Classification

by Yuji Kawai, Kazuki Tachikawa, Jihoon Park and Minoru Asada

Brain Sci. 2022, 12(7), 849; https://0-doi-org.brum.beds.ac.uk/10.3390/brainsci12070849 - 28 Jun 2022

Cited by 1 | Viewed by 1786

Abstract

The integrated gradients (IG) method is widely used to evaluate the extent to which each input feature contributes to the classification using a deep learning model because it theoretically satisfies the desired properties to fairly attribute the contributions to the classification. However, this approach requires an appropriate baseline to do so. In this study, we propose a compensated IG method that does not require a baseline, which compensates the contributions calculated using the IG method at an arbitrary baseline by using an example of the Shapley sampling value. We prove that the proposed approach can compute the contributions to the classification results reliably if the processes of each input feature in a classifier are independent of one another and the parameterization of each process is identical, as in shared weights in convolutional neural networks. Using three datasets on electroencephalogram recordings, we experimentally demonstrate that the contributions obtained by the proposed compensated IG method are more reliable than those obtained using the original IG method and that its computational complexity is much lower than that of the Shapley sampling method. Full article

(This article belongs to the Special Issue Advances in EEG Brain Dynamics)

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20 pages, 4096 KiB

Open AccessArticle

A Depression Diagnosis Method Based on the Hybrid Neural Network and Attention Mechanism

by Zhuozheng Wang, Zhuo Ma, Wei Liu, Zhefeng An and Fubiao Huang

Brain Sci. 2022, 12(7), 834; https://0-doi-org.brum.beds.ac.uk/10.3390/brainsci12070834 - 26 Jun 2022

Cited by 7 | Viewed by 2479

Abstract

Depression is a common but easily misdiagnosed disease when using a self-assessment scale. Electroencephalograms (EEGs) provide an important reference and objective basis for the identification and diagnosis of depression. In order to improve the accuracy of the diagnosis of depression by using mainstream algorithms, a high-performance hybrid neural network depression detection method is proposed in this paper combined with deep learning technology. Firstly, a concatenating one-dimensional convolutional neural network (1D-CNN) and gated recurrent unit (GRU) are employed to extract the local features and to determine the global features of the EEG signal. Secondly, the attention mechanism is introduced to form the hybrid neural network. The attention mechanism assigns different weights to the multi-dimensional features extracted by the network, so as to screen out more representative features, which can reduce the computational complexity of the network and save the training time of the model while ensuring high precision. Moreover, dropout is applied to accelerate network training and address the over-fitting problem. Experiments reveal that the 1D-CNN-GRU-ATTN model has more effectiveness and a better generalization ability compared with traditional algorithms. The accuracy of the proposed method in this paper reaches 99.33% in a public dataset and 97.98% in a private dataset, respectively. Full article

(This article belongs to the Special Issue Advances in EEG Brain Dynamics)

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12 pages, 7738 KiB

Open AccessArticle

EEG Classification of Normal and Alcoholic by Deep Learning

by Houchi Li and Lei Wu

Brain Sci. 2022, 12(6), 778; https://0-doi-org.brum.beds.ac.uk/10.3390/brainsci12060778 - 14 Jun 2022

Cited by 8 | Viewed by 3159

Abstract

Alcohol dependence is a common mental disease worldwide. Excessive alcohol consumption may lead to alcoholism and many complications. In severe cases, it will lead to inhibition and paralysis of the centers of the respiratory and circulatory systems and even death. In addition, there is a lack of effective standard test procedures to detect alcoholism. EEG signals are data obtained by measuring brain changes in the cerebral cortex and can be used for the diagnosis of alcoholism. Existing diagnostic methods mainly employ machine learning techniques, which rely on human intervention to learn. In contrast, deep learning, as an end-to-end learning method, can automatically extract EEG signal features, which is more convenient. Nonetheless, there are few studies on the classification of alcohol’s EEG signals using deep learning models. Therefore, in this paper, a new deep learning method is proposed to automatically extract and classify EEG’s features. The method first adopts a multilayer discrete wavelet transform to denoise the input data. Then, the denoised data are used as input, and a convolutional neural network and bidirectional long short-term memory network are used for feature extraction. Finally, alcohol EEG signal classification is performed. The experimental results show that the method proposed in this study can be utilized to effectively diagnose patients with alcoholism, achieving a diagnostic accuracy of 99.32%, which is better than most current algorithms. Full article

(This article belongs to the Special Issue Advances in EEG Brain Dynamics)

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16 pages, 1982 KiB

Open AccessArticle

Inhibitory Control and Brain–Heart Interaction: An HRV-EEG Study

by Maria Daniela Cortese, Martina Vatrano, Paolo Tonin, Antonio Cerasa and Francesco Riganello

Brain Sci. 2022, 12(6), 740; https://0-doi-org.brum.beds.ac.uk/10.3390/brainsci12060740 - 05 Jun 2022

Cited by 5 | Viewed by 2794

Abstract

Background: Motor inhibition is a complex cognitive function regulated by specific brain regions and influenced by the activity of the Central Autonomic Network. We investigate the two-way Brain–Heart interaction during a Go/NoGo task. Spectral EEG ϑ, α powerbands, and HRV parameters (Complexity Index (CI), Low Frequency (LF) and High Frequency (HF) powers) were recorded. Methods: Fourteen healthy volunteers were enrolled. We used a modified version of the classical Go/NoGo task, based on Rule Shift Cards, characterized by a baseline and two different tasks of different complexity. The participants were divided into subjects with Good (GP) and Poor (PP) performances. Results: In the baseline, CI was negatively correlated with α/ϑ. In task 1, the CI was negatively correlated with the errors and α/ϑ, while the errors were positively correlated with α/ϑ. In task 2, CI was negatively correlated with the Reaction Time and positively with α, and the errors were negatively correlated with the Reaction Time and positively correlated with α/ϑ. The GP group showed, at baseline, a negative correlation between CI and α/ϑ. Conclusions: We provide a new combined Brain–Heart model underlying inhibitory control abilities. The results are consistent with the complementary role of α and ϑ oscillations in cognitive control. Full article

(This article belongs to the Special Issue Advances in EEG Brain Dynamics)

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