Research

20 pages, 13057 KiB

Open AccessArticle

DIVA Meets EEG: Model Validation Using Formant-Shift Reflex

by Jhosmary Cuadros, Lucía Z-Rivera, Christian Castro, Grace Whitaker, Mónica Otero, Alejandro Weinstein, Eduardo Martínez-Montes, Pavel Prado and Matías Zañartu

Appl. Sci. 2023, 13(13), 7512; https://doi.org/10.3390/app13137512 - 25 Jun 2023

Viewed by 1351

Abstract

The neurocomputational model ‘Directions into Velocities of Articulators’ (DIVA) was developed to account for various aspects of normal and disordered speech production and acquisition. The neural substrates of DIVA were established through functional magnetic resonance imaging (fMRI), providing physiological validation of the model. [...] Read more.

The neurocomputational model ‘Directions into Velocities of Articulators’ (DIVA) was developed to account for various aspects of normal and disordered speech production and acquisition. The neural substrates of DIVA were established through functional magnetic resonance imaging (fMRI), providing physiological validation of the model. This study introduces DIVA_EEG an extension of DIVA that utilizes electroencephalography (EEG) to leverage the high temporal resolution and broad availability of EEG over fMRI. For the development of DIVA_EEG, EEG-like signals were derived from original equations describing the activity of the different DIVA maps. Synthetic EEG associated with the utterance of syllables was generated when both unperturbed and perturbed auditory feedback (first formant perturbations) were simulated. The cortical activation maps derived from synthetic EEG closely resembled those of the original DIVA model. To validate DIVA_EEG, the EEG of individuals with typical voices (N = 30) was acquired during an altered auditory feedback paradigm. The resulting empirical brain activity maps significantly overlapped with those predicted by DIVA_EEG. In conjunction with other recent model extensions, DIVA_EEG lays the foundations for constructing a complete neurocomputational framework to tackle vocal and speech disorders, which can guide model-driven personalized interventions. Full article

(This article belongs to the Special Issue Application of Electroencephalography (EEG) Signal Analysis in Disease Diagnosis)

► Show Figures

Figure 1

12 pages, 1119 KiB

Open AccessArticle

Quantitative Electroencephalographic Analysis in Women with Migraine during the Luteal Phase

by Héctor Juan Pelayo-González, Verónica Reyes-Meza, Ignacio Méndez-Balbuena, Oscar Méndez-Díaz, Carlos Trenado, Diane Ruge, Gregorio García-Aguilar and Vicente Arturo López-Cortés

Appl. Sci. 2023, 13(13), 7443; https://doi.org/10.3390/app13137443 - 23 Jun 2023

Cited by 1 | Viewed by 934

Abstract

Migraine is a common, headache disorder characterized by recurrent episodes of headache often associated with nausea, vomiting, photophobia, and phonophobia. Prior to puberty, boys and girls are equally affected. Female preponderance emerges after puberty. Migraine pathophysiology is not fully understood, and although the [...] Read more.

Migraine is a common, headache disorder characterized by recurrent episodes of headache often associated with nausea, vomiting, photophobia, and phonophobia. Prior to puberty, boys and girls are equally affected. Female preponderance emerges after puberty. Migraine pathophysiology is not fully understood, and although the hormonal effect of estrogen is significant, it is not clear how hormonal phases affect brain excitability and EEG patterns in women with migraine. The objective of this research was to study the effect of migraine on the resting-state EEG activity of women during the luteal phase. This work compares electroencephalographic (EEG) absolute power in different frequency bands and scalp areas between young women who suffer from migraine and had a migraine attack within 24 h prior to EEG recording (experimental) and ten age-matched young healthy women (controls), all with normal menstrual cycles. For women with migraine, we found a significant decrease/increase in alpha power in the occipitoparietal/frontocentral area, significant decrease in beta power for all areas, significant decrease in delta power in the temporal area, and significant decrease in theta power in the frontocentral and occipitoparietal area. We concluded that women with migraine have a distinct electroencephalographic pattern during the luteal phase in comparison with control women. A possible explanation might be an intermittent rhythmic activity linked to pain. Full article

(This article belongs to the Special Issue Application of Electroencephalography (EEG) Signal Analysis in Disease Diagnosis)

► Show Figures

Figure 1

24 pages, 3294 KiB

Open AccessArticle

Using Mental Shadowing Tasks to Improve the Sound-Evoked Potential of EEG in the Design of an Auditory Brain–Computer Interface

by Koun-Tem Sun, Kai-Lung Hsieh and Shih-Yun Lee

Appl. Sci. 2023, 13(2), 856; https://doi.org/10.3390/app13020856 - 08 Jan 2023

Cited by 1 | Viewed by 1661

Abstract

This study proposed an auditory stimulation protocol based on Shadowing Tasks to improve the sound-evoked potential in an EEG and the efficiency of an auditory brain–computer interface system. We use stories as auditory stimulation to enhance users’ motivation and presented the sound stimuli [...] Read more.

This study proposed an auditory stimulation protocol based on Shadowing Tasks to improve the sound-evoked potential in an EEG and the efficiency of an auditory brain–computer interface system. We use stories as auditory stimulation to enhance users’ motivation and presented the sound stimuli via headphones to enable the user to concentrate better on the keywords in the stories. The protocol presents target stimuli with an oddball P300 paradigm. To decline mental workload, we shift the usual Shadowing Tasks paradigm: Instead of loudly repeating the auditory target stimuli, we ask subjects to echo the target stimuli mentally as it occurs. Twenty-four healthy participants, not one of whom underwent a BCI use or training phase before the experimental procedure, ran twenty trials each. We analyzed the effect of the auditory stimulation based on the Shadowing Tasks theory with the performance of the auditory BCI system. We also evaluated the judgment effectiveness of the three ERPs components (N2P3, P300, and N200) from five chosen electrodes. The best average accuracy of post-analysis was 78.96%. Using component N2P3 to distinguish between target and non-target can improve the efficiency of the auditory BCI system and give it good practicality. We intend to persist in this study and involve the protocol in an aBCI-based home care system (HCS) for target patients to provide daily assistance. Full article

(This article belongs to the Special Issue Application of Electroencephalography (EEG) Signal Analysis in Disease Diagnosis)

► Show Figures

Figure 1

9 pages, 418 KiB

Open AccessCommunication

Predicting the Onset of Freezing of Gait Using EEG Dynamics

by Alka Rachel John, Zehong Cao, Hsiang-Ting Chen, Kaylena Ehgoetz Martens, Matthew Georgiades, Moran Gilat, Hung T. Nguyen, Simon J. G. Lewis and Chin-Teng Lin

Appl. Sci. 2023, 13(1), 302; https://doi.org/10.3390/app13010302 - 27 Dec 2022

Cited by 4 | Viewed by 2290

Abstract

Freezing of gait (FOG) severely incapacitates the mobility of patients with advanced Parkinson’s disease (PD). An accurate prediction of the onset of FOG could improve the quality of life for PD patients. However, it is imperative to distinguish the possibility of the onset [...] Read more.

Freezing of gait (FOG) severely incapacitates the mobility of patients with advanced Parkinson’s disease (PD). An accurate prediction of the onset of FOG could improve the quality of life for PD patients. However, it is imperative to distinguish the possibility of the onset of FOG from that of voluntary stopping. Our previous work demonstrated the neurological differences between the transition to FOG and voluntary stopping using electroencephalogram (EEG) signals. We employed a timed up-and-go (TUG) task to elicit FOG in PD patients. Some of these TUG tasks had an additional voluntary stopping component, where participants stopped walking based on verbal instruction to “stop”. The performance of the convolutional neural network (CNN) in identifying the transition to FOG from normal walking and the transition to voluntary stopping was explored. To the best of our knowledge, this work is the first study to propose a deep learning method to distinguish the transition to FOG from the transition to voluntary stop in PD patients. The models, trained on the EEG data from 17 PD patients who manifested FOG episodes, considering a short two-second transition window for FOG occurrence or voluntary stopping, achieved close to 75% classification accuracy in distinguishing transition to FOG from the transition to voluntary stopping or normal walking. Our results represent an important step toward advanced EEG-based cueing systems for smart FOG intervention, excluding the potential confounding of voluntary stopping. Full article

(This article belongs to the Special Issue Application of Electroencephalography (EEG) Signal Analysis in Disease Diagnosis)

► Show Figures

Figure 1

13 pages, 2109 KiB

Open AccessArticle

Effect of Carotid Stenosis Severity on Patterns of Brain Activity in Patients after Cardiac Surgery

by Irina Tarasova, Olga Trubnikova, Darya Kupriyanova, Irina Kukhareva, Irina Syrova, Anastasia Sosnina, Olga Maleva and Olga Barbarash

Appl. Sci. 2023, 13(1), 20; https://doi.org/10.3390/app13010020 - 20 Dec 2022

Cited by 1 | Viewed by 1080

Abstract

Background: The negative effects of high-grade carotid stenosis on the brain are widely known. However, there are still insufficient data on the brain state in patients with small carotid stenosis and after isolated or combined coronary and carotid surgery. This EEG-based study aimed [...] Read more.

Background: The negative effects of high-grade carotid stenosis on the brain are widely known. However, there are still insufficient data on the brain state in patients with small carotid stenosis and after isolated or combined coronary and carotid surgery. This EEG-based study aimed to analyze the effect of carotid stenosis severity on associated brain activity changes and the neurophysiological test results in patients undergoing coronary artery bypass grafting (CABG) with or without carotid endarterectomy (CEA). Methods: One hundred and forty cardiac surgery patients underwent a clinical and neuropsychological examination and a multichannel EEG before surgery and 7–10 days after surgery. Results: The patients with CA stenoses of less than 50% demonstrated higher values of theta2- and alpha-rhythm power compared to the patients without CA stenoses both before and after CABG. In addition, the patients who underwent right-sided CABG+CEA had generalized EEG “slowdown” compared with isolated CABG and left-sided CABG+CEA patients. Conclusions: The on-pump cardiac surgery accompanied by specific re-arrangements of frequency–spatial patterns of electrical brain activity are dependent on the degree of carotid stenoses. The information obtained can be used to optimize the process of preoperative and postoperative management, as well as the search for neuroprotection and safe surgical strategies for this category of patients. Full article

(This article belongs to the Special Issue Application of Electroencephalography (EEG) Signal Analysis in Disease Diagnosis)

► Show Figures

Figure 1

10 pages, 1319 KiB

Open AccessArticle

Delirium after Spinal Surgery: A Pilot Study of Electroencephalography Signals from a Wearable Device

by Soo-Bin Lee, Ji-Won Kwon, Sahyun Sung, Seong-Hwan Moon and Byung Ho Lee

Appl. Sci. 2022, 12(19), 9899; https://doi.org/10.3390/app12199899 - 01 Oct 2022

Viewed by 1455

Abstract

Postoperative delirium after spinal surgery in elderly patients has been a recent concern. However, there has not been a study of delirium after spinal surgery based on electroencephalography (EEG) signals from a compact wearable device. We aimed to analyze differences in EEG signals [...] Read more.

Postoperative delirium after spinal surgery in elderly patients has been a recent concern. However, there has not been a study of delirium after spinal surgery based on electroencephalography (EEG) signals from a compact wearable device. We aimed to analyze differences in EEG signals from a wearable device in patients with and without delirium after spinal surgery. Thirty-seven patients who underwent cervical or lumbar decompression and instrumented fusion for degenerative spinal disease were included. EEG waves were collected from a compact wearable device, and percentage changes from baseline to within 1 week and 3 months after surgery were compared between patients with and without delirium. In patients with delirium, the anxiety- and stress-related EEG waves—including the H-beta (19.3%; p = 0.003) and gamma (18.8%; p = 0.006) waves—and the tension index (7.8%; p = 0.011) increased, and the relaxation-related theta waves (−23.2%; p = 0.016) decreased within 1 week after surgery compared to the non-delirium group. These results will contribute to understanding of the EEG patterns of postoperative delirium and can be applied for the early detection and prompt treatment of postoperative delirium after spinal surgery. Full article

(This article belongs to the Special Issue Application of Electroencephalography (EEG) Signal Analysis in Disease Diagnosis)

► Show Figures

Figure 1

14 pages, 30796 KiB

Open AccessArticle

by Mariia Chernykh, Bohdan Vodianyk, Ivan Seleznov, Dmytro Harmatiuk, Ihor Zyma, Anton Popov and Ken Kiyono

Appl. Sci. 2022, 12(15), 7849; https://doi.org/10.3390/app12157849 - 04 Aug 2022

Cited by 6 | Viewed by 1888

Abstract

Understanding brain reactions to facial expressions can help in explaining emotion-processing and memory mechanisms. The purpose of this research is to examine the dynamics of electrical brain activity caused by visual emotional stimuli. The focus is on detecting changes in cognitive mechanisms produced [...] Read more.

Understanding brain reactions to facial expressions can help in explaining emotion-processing and memory mechanisms. The purpose of this research is to examine the dynamics of electrical brain activity caused by visual emotional stimuli. The focus is on detecting changes in cognitive mechanisms produced by negative, positive, and neutral expressions on human faces. Three methods were used to study brain reactions: power spectral density, detrending moving average (DMA), and coherence analysis. Using electroencephalogram (EEG) recordings from 48 subjects while presenting facial image stimuli from the International Affective Picture System, the topographic representation of the evoked responses was acquired and evaluated to disclose the specific EEG-based activity patterns in the cortex. The theta and beta systems are two key cognitive systems of the brain that are activated differently on the basis of gender. The obtained results also demonstrate that the DMA method can provide information about the cortical networks’ functioning stability, so it can be coupled with more prevalent methods of EEG analysis. Full article

(This article belongs to the Special Issue Application of Electroencephalography (EEG) Signal Analysis in Disease Diagnosis)

► Show Figures

Figure 1

19 pages, 823 KiB

Open AccessArticle

Promise for Personalized Diagnosis? Assessing the Precision of Wireless Consumer-Grade Electroencephalography across Mental States

by Amedeo D’Angiulli, Guillaume Lockman-Dufour and Derrick Matthew Buchanan

Appl. Sci. 2022, 12(13), 6430; https://doi.org/10.3390/app12136430 - 24 Jun 2022

Cited by 3 | Viewed by 2354

Abstract

In the last decade there has been significant growth in the interest and application of using EEG (electroencephalography) outside of laboratory as well as in medical and clinical settings, for more ecological and mobile applications. However, for now such applications have mainly included [...] Read more.

In the last decade there has been significant growth in the interest and application of using EEG (electroencephalography) outside of laboratory as well as in medical and clinical settings, for more ecological and mobile applications. However, for now such applications have mainly included military, educational, cognitive enhancement, and consumer-based games. Given the monetary and ecological advantages, consumer-grade EEG devices such as the Emotiv EPOC have emerged, however consumer-grade devices make certain compromises of data quality in order to become affordable and easy to use. The goal of this study was to investigate the reliability and accuracy of EPOC as compared to a research-grade device, Brainvision. To this end, we collected data from participants using both devices during three distinct cognitive tasks designed to elicit changes in arousal, valence, and cognitive load: namely, Affective Norms for English Words, International Affective Picture System, and the n-Back task. Our design and analytical strategies followed an ideographic person-level approach (electrode-wise analysis of vincentized repeated measures). We aimed to assess how well the Emotiv could differentiate between mental states using an Event-Related Band Power approach and EEG features such as amplitude and power, as compared to Brainvision. The Emotiv device was able to differentiate mental states during these tasks to some degree, however it was generally poorer than Brainvision, with smaller effect sizes. The Emotiv may be used with reasonable reliability and accuracy in ecological settings and in some clinical contexts (for example, for training professionals), however Brainvision or other, equivalent research-grade devices are still recommended for laboratory or medical based applications. Full article

(This article belongs to the Special Issue Application of Electroencephalography (EEG) Signal Analysis in Disease Diagnosis)

► Show Figures

Figure 1

10 pages, 947 KiB

Open AccessArticle

Effect of Rehabilitation on Brain Functional Connectivity in a Stroke Patient Affected by Conduction Aphasia

by Serena Dattola and Fabio La Foresta

Appl. Sci. 2022, 12(12), 5991; https://doi.org/10.3390/app12125991 - 13 Jun 2022

Viewed by 1721

Abstract

Stroke is a medical condition that affects the brain and represents a leading cause of death and disability. Associated with drug therapy, rehabilitative treatment is essential for promoting recovery. In the present work, we report an EEG-based study concerning a left ischemic stroke [...] Read more.

Stroke is a medical condition that affects the brain and represents a leading cause of death and disability. Associated with drug therapy, rehabilitative treatment is essential for promoting recovery. In the present work, we report an EEG-based study concerning a left ischemic stroke patient affected by conduction aphasia. Specifically, the objective is to compare the brain functional connectivity before and after an intensive rehabilitative treatment. The analysis was performed by means of local and global efficiency measures related to the execution of three tasks: naming, repetition and reading. As expected, the results showed that the treatment led to a balancing of the values of both parameters between the two hemispheres since the rehabilitation contributed to the creation of new neural patterns to compensate for the disrupted ones. Moreover, we observed that for both name and repetition tasks, shortly after the stroke, the global and local connectivity are lower in the affected lobe (left hemisphere) than in the unaffected one (right hemisphere). Conversely, for the reading task, global and local connectivity are higher in the impaired lobe. This apparently contrasting trend can be due to the effects of stroke, which affect not only the site of structural damage but also brain regions belonging to a functional network. Moreover, changes in network connectivity can be task-dependent. This work can be considered a first step for future EEG-based studies to establish the most suitable connectivity measures for supporting the treatment of stroke and monitoring the recovery process. Full article

(This article belongs to the Special Issue Application of Electroencephalography (EEG) Signal Analysis in Disease Diagnosis)

► Show Figures

Figure 1

17 pages, 1311 KiB

Open AccessArticle

Wavelet-Based Multi-Class Seizure Type Classification System

by Hezam Albaqami, Ghulam Mubashar Hassan and Amitava Datta

Appl. Sci. 2022, 12(11), 5702; https://doi.org/10.3390/app12115702 - 03 Jun 2022

Cited by 8 | Viewed by 2144

Abstract

Epilepsy is one of the most common brain diseases that affects more than 1% of the world’s population. It is characterized by recurrent seizures, which come in different types and are treated differently. Electroencephalography (EEG) is commonly used in medical services to diagnose [...] Read more.

Epilepsy is one of the most common brain diseases that affects more than 1% of the world’s population. It is characterized by recurrent seizures, which come in different types and are treated differently. Electroencephalography (EEG) is commonly used in medical services to diagnose seizures and their types. The accurate identification of seizures helps to provide optimal treatment and accurate information to the patient. However, the manual diagnostic procedures of epileptic seizures are laborious and require professional skills. This paper presents a novel automatic technique that involves the extraction of specific features from epileptic seizures’ EEG signals using dual-tree complex wavelet transform (DTCWT) and classifying them into one of the seven types of seizures, including absence, complex-partial, focal non-specific, generalized non-specific, simple-partial, tonic-clonic, and tonic seizures. We evaluated the proposed technique on the TUH EEG Seizure Corpus (TUSZ) ver.1.5.2 dataset and compared the performance with the existing state-of-the-art techniques using the overall F1-score due to class imbalance of seizure types. Our proposed technique achieved the best results of a weighted F1-score of 99.1% and 74.7% for seizure-wise and patient-wise classification, respectively, thereby setting new benchmark results for this dataset. Full article

(This article belongs to the Special Issue Application of Electroencephalography (EEG) Signal Analysis in Disease Diagnosis)

► Show Figures

Figure 1

13 pages, 2558 KiB

Open AccessArticle

EEG Signal Processing and Supervised Machine Learning to Early Diagnose Alzheimer’s Disease

by Daniele Pirrone, Emanuel Weitschek, Primiano Di Paolo, Simona De Salvo and Maria Cristina De Cola

Appl. Sci. 2022, 12(11), 5413; https://doi.org/10.3390/app12115413 - 26 May 2022

Cited by 21 | Viewed by 4979

Abstract

Electroencephalography (EEG) signal analysis is a fast, inexpensive, and accessible technique to detect the early stages of dementia, such as Mild Cognitive Impairment (MCI) and Alzheimer’s disease (AD). In the last years, EEG signal analysis has become an important topic of research to [...] Read more.

Electroencephalography (EEG) signal analysis is a fast, inexpensive, and accessible technique to detect the early stages of dementia, such as Mild Cognitive Impairment (MCI) and Alzheimer’s disease (AD). In the last years, EEG signal analysis has become an important topic of research to extract suitable biomarkers to determine the subject’s cognitive impairment. In this work, we propose a novel simple and efficient method able to extract features with a finite response filter (FIR) in the double time domain in order to discriminate among patients affected by AD, MCI, and healthy controls (HC). Notably, we compute the power intensity for each high- and low-frequency band, using their absolute differences to distinguish among the three classes of subjects by means of different supervised machine learning methods. We use EEG recordings from a cohort of 105 subjects (48 AD, 37 MCI, and 20 HC) referred for dementia to the IRCCS Centro Neurolesi “Bonino-Pulejo” of Messina, Italy. The findings show that this method reaches 97%, 95%, and 83% accuracy when considering binary classifications (HC vs. AD, HC vs. MCI, and MCI vs. AD) and an accuracy of 75% when dealing with the three classes (HC vs. AD vs. MCI). These results improve upon those obtained in previous studies and demonstrate the validity of our approach. Finally, the efficiency of the proposed method might allow its future development on embedded devices for low-cost real-time diagnosis. Full article

(This article belongs to the Special Issue Application of Electroencephalography (EEG) Signal Analysis in Disease Diagnosis)

► Show Figures

Figure 1

14 pages, 2806 KiB

Open AccessArticle

qEEG Analysis in the Diagnosis of Alzheimer’s Disease: A Comparison of Functional Connectivity and Spectral Analysis

by Maria Semeli Frangopoulou and Maryam Alimardani

Appl. Sci. 2022, 12(10), 5162; https://doi.org/10.3390/app12105162 - 20 May 2022

Cited by 1 | Viewed by 1767

Abstract

Alzheimer’s disease (AD) is a brain disorder that is mainly characterized by a progressive degeneration of neurons in the brain and decline of cognitive abilities. This study compared an FFT-based spectral analysis against a functional connectivity analysis for the diagnosis of AD. Both [...] Read more.

Alzheimer’s disease (AD) is a brain disorder that is mainly characterized by a progressive degeneration of neurons in the brain and decline of cognitive abilities. This study compared an FFT-based spectral analysis against a functional connectivity analysis for the diagnosis of AD. Both quantitative methods were applied on an EEG dataset including 20 diagnosed AD patients and 20 age-matched healthy controls (HC). The obtained results showed an advantage of the functional connectivity analysis when compared to the spectral analysis; while the latter could not find any significant differences between the AD and HC groups, the functional connectivity analysis showed statistically higher synchronization levels in the AD group in the lower frequency bands (delta and theta), suggesting a ‘phase-locked’ state in AD-affected brains. Further comparison of functional connectivity between the homotopic regions confirmed that the traits of AD were localized to the centro-parietal and centro-temporal areas in the theta frequency band (4–8 Hz). This study applies a neural metric for Alzheimer’s detection from a data science perspective rather than from a neuroscience one and shows that the combination of bipolar derivations with phase synchronization yields similar results to comparable studies employing alternative analysis methods. Full article

(This article belongs to the Special Issue Application of Electroencephalography (EEG) Signal Analysis in Disease Diagnosis)

► Show Figures

Figure 1

15 pages, 1918 KiB

Open AccessArticle

Use Electroencephalogram Entropy as an Indicator to Detect Stress-Induced Sleep Alteration

by Yun Lo, Yi-Tse Hsiao and Fang-Chia Chang

Appl. Sci. 2022, 12(10), 4812; https://doi.org/10.3390/app12104812 - 10 May 2022

Cited by 2 | Viewed by 1468

Abstract

An acute stressor can cause sleep disruptions. Electroencephalography (EEG) is one of the major tools to measure sleep. In rats, sleep stages are classified as rapid-eye movement (REM) sleep and non-rapid-eye movement (NREM) sleep, by different characteristics of EEGs. Sleep alterations after exposure [...] Read more.

An acute stressor can cause sleep disruptions. Electroencephalography (EEG) is one of the major tools to measure sleep. In rats, sleep stages are classified as rapid-eye movement (REM) sleep and non-rapid-eye movement (NREM) sleep, by different characteristics of EEGs. Sleep alterations after exposure to an acute stress are regularly determined by the power spectra of brain waves and the changes of vigilance stages, and they all depend on EEG analysis. Herein, we hypothesized that the Shannon entropy can be employed as an indicator to detect stress-induced sleep alterations, since we noticed that an acute stressor, the footshock stimulation, causes certain uniformity changes of the spectrograms during NREM and REM sleep in rats. The present study applied the Shannon entropy on three features of brain waves, including the amplitude, frequency, and oscillation phases, to measure the uniformities in the footshock-induced alterations of sleep EEGs. Our result suggests that the footshock stimuli resulted in a smoother and uniform amplitude as well as varied frequencies of EEG waveforms during REM sleep. In contrast, the EEGs during NREM sleep exhibited a smoother, but less uniform, amplitude after the footshock stimuli. The result depicts the change property of brain waves after exposure to an acute stressor and, also, demonstrates that the Shannon entropy could be used to detect EEG alteration in sleep disorders. Full article

(This article belongs to the Special Issue Application of Electroencephalography (EEG) Signal Analysis in Disease Diagnosis)

► Show Figures

Figure 1

14 pages, 2501 KiB

Open AccessArticle

EEG Oscillatory Power and Complexity for Epileptic Seizure Detection

by Lina Abou-Abbas, Imene Jemal, Khadidja Henni, Youssef Ouakrim, Amar Mitiche and Neila Mezghani

Appl. Sci. 2022, 12(9), 4181; https://doi.org/10.3390/app12094181 - 21 Apr 2022

Cited by 8 | Viewed by 1853

Abstract

Monitoring patients at risk of epileptic seizure is critical for optimal treatment and ensuing the reduction of seizure risk and complications. In general, seizure detection is done manually in hospitals and involves time-consuming visual inspection and interpretation by experts of electroencephalography (EEG) recordings. [...] Read more.

Monitoring patients at risk of epileptic seizure is critical for optimal treatment and ensuing the reduction of seizure risk and complications. In general, seizure detection is done manually in hospitals and involves time-consuming visual inspection and interpretation by experts of electroencephalography (EEG) recordings. The purpose of this study is to investigate the pertinence of band-limited spectral power and signal complexity in order to discriminate between seizure and seizure-free EEG brain activity. The signal complexity and spectral power are evaluated in five frequency intervals, namely, the delta, theta, alpha, beta, and gamma bands, to be used as EEG signal feature representation. Classification of seizure and seizure-free data was performed by prevalent potent classifiers. Substantial comparative performance evaluation experiments were performed on a large EEG data record of 341 patients in the Temple University Hospital EEG seizure database. Based on statistically validated criteria, results show the efficiency of band-limited spectral power and signal complexity when using random forest and gradient-boosting decision tree classifiers (95% of the area under the curve (AUC) and 91% for both F-measure and accuracy). These results support the use of these automatic classification schemes to assist the practicing neurologist interpret EEG records more accurately and without tedious visual inspection. Full article

(This article belongs to the Special Issue Application of Electroencephalography (EEG) Signal Analysis in Disease Diagnosis)

► Show Figures

Figure 1

Review

Jump to: Research, Other

61 pages, 5064 KiB

Open AccessReview

Quantitative Electroencephalogram (qEEG) as a Natural and Non-Invasive Window into Living Brain and Mind in the Functional Continuum of Healthy and Pathological Conditions

by Alexander A. Fingelkurts and Andrew A. Fingelkurts

Appl. Sci. 2022, 12(19), 9560; https://doi.org/10.3390/app12199560 - 23 Sep 2022

Cited by 4 | Viewed by 6973

Abstract

Many practicing clinicians are time-poor and are unaware of the accumulated neuroscience developments. Additionally, given the conservative nature of their field, key insights and findings trickle through into the mainstream clinical zeitgeist rather slowly. Over many decades, clinical, systemic, and cognitive neuroscience have [...] Read more.

Many practicing clinicians are time-poor and are unaware of the accumulated neuroscience developments. Additionally, given the conservative nature of their field, key insights and findings trickle through into the mainstream clinical zeitgeist rather slowly. Over many decades, clinical, systemic, and cognitive neuroscience have produced a large and diverse body of evidence for the potential utility of brain activity (measured by electroencephalogram—EEG) for neurology and psychiatry. Unfortunately, these data are enormous and essential information often gets buried, leaving many researchers stuck with outdated paradigms. Additionally, the lack of a conceptual and unifying theoretical framework, which can bind diverse facts and relate them in a meaningful way, makes the whole situation even more complex. To contribute to the systematization of essential data (from the authors’ point of view), we present an overview of important findings in the fields of electrophysiology and clinical, systemic, and cognitive neuroscience and provide a general theoretical–conceptual framework that is important for any application of EEG signal analysis in neuropsychopathology. In this context, we intentionally omit detailed descriptions of EEG characteristics associated with neuropsychopathology as irrelevant to this theoretical–conceptual review. Full article

(This article belongs to the Special Issue Application of Electroencephalography (EEG) Signal Analysis in Disease Diagnosis)

► Show Figures

Figure 1

Other

Jump to: Research, Review

13 pages, 940 KiB

Open AccessPerspective

Decoding of Processing Preferences from Language Paradigms by Means of EEG-ERP Methodology: Risk Markers of Cognitive Vulnerability for Depression and Protective Indicators of Well-Being? Cerebral Correlates and Mechanisms

by Cornelia Herbert

Appl. Sci. 2022, 12(15), 7740; https://doi.org/10.3390/app12157740 - 01 Aug 2022

Viewed by 1628

Abstract

Depression is a frequent mental affective disorder. Cognitive vulnerability models propose two major cognitive risk factors that favor the onset and severity of depressive symptoms. These include a pronounced self-focus, as well as a negative emotional processing bias. According to two-process models of [...] Read more.

Depression is a frequent mental affective disorder. Cognitive vulnerability models propose two major cognitive risk factors that favor the onset and severity of depressive symptoms. These include a pronounced self-focus, as well as a negative emotional processing bias. According to two-process models of cognitive vulnerability, these two risk factors are not independent from each other, but affect information processing already at an early perceptual processing level. Simultaneously, a processing advantage for self-related positive information including better memory for positive than negative information has been associated with mental health and well-being. This perspective paper introduces a research framework that discusses how EEG-ERP methodology can serve as a standardized tool for the decoding of negative and positive processing biases and their potential use as risk markers of cognitive vulnerability for depression, on the one hand, and as protective indicators of well-being, on the other hand. Previous results from EEG-ERP studies investigating the time-course of self-referential emotional processing are introduced, summarized, and discussed with respect to the specificity of depression-related processing and the importance of EEG-ERP-based experimental testing for well-being and the prevention and treatment of depressive disorders. Full article

(This article belongs to the Special Issue Application of Electroencephalography (EEG) Signal Analysis in Disease Diagnosis)

► Show Figures

Figure 1

Journal Menu

Journal Browser

Application of Electroencephalography (EEG) Signal Analysis in Disease Diagnosis

Share This Special Issue

Special Issue Editors

Special Issue Information

Keywords

Published Papers (16 papers)

Research

Review

Other

Further Information

Guidelines

MDPI Initiatives

Follow MDPI