scholarly journals Diagnosing Schizophrenia Using Effective Connectivity of Resting-State EEG Data

Algorithms ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 139
Author(s):  
Claudio Ciprian ◽  
Kirill Masychev ◽  
Maryam Ravan ◽  
Akshaya Manimaran ◽  
AnkitaAmol Deshmukh
Keyword(s):  
Resting State ◽  
Learning Algorithm ◽  
Effective Connectivity ◽  
Eyes Closed ◽  
Promising Tool ◽  
Eeg Data ◽  
Symbolic Transfer Entropy ◽  
Schizophrenic Patients ◽  
External Stimuli ◽  
Electroencephalogram Eeg

Schizophrenia is a serious mental illness associated with neurobiological deficits. Even though the brain activities during tasks (i.e., P300 activities) are considered as biomarkers to diagnose schizophrenia, brain activities at rest have the potential to show an inherent dysfunctionality in schizophrenia and can be used to understand the cognitive deficits in these patients. In this study, we developed a machine learning algorithm (MLA) based on eyes closed resting-state electroencephalogram (EEG) datasets, which record the neural activity in the absence of any tasks or external stimuli given to the subjects, aiming to distinguish schizophrenic patients (SCZs) from healthy controls (HCs). The MLA has two steps. In the first step, symbolic transfer entropy (STE), which is a measure of effective connectivity, is applied to resting-state EEG data. In the second step, the MLA uses the STE matrix to find a set of features that can successfully discriminate SCZ from HC. From the results, we found that the MLA could achieve a total accuracy of 96.92%, with a sensitivity of 95%, a specificity of 98.57%, precision of 98.33%, F1-score of 0.97, and Matthews correlation coefficient (MCC) of 0.94 using only 10 out of 1900 STE features, which implies that the STE matrix extracted from resting-state EEG data may be a promising tool for the clinical diagnosis of schizophrenia.

scholarly journals Prediction of Individual User’s Dynamic Ranges of EEG Features from Resting-State EEG Data for Evaluating Their Suitability for Passive Brain–Computer Interface Applications

Sensors ◽  
2020 ◽  
Vol 20 (4) ◽  
pp. 988
Author(s):  
Ho-Seung Cha ◽  
Chang-Hee Han ◽  
Chang-Hwan Im
Keyword(s):  
Resting State ◽  
Brain Computer Interface ◽  
Low Cost ◽  
Mental States ◽  
Computer Interface ◽  
Practical Applications ◽  
Eeg Data ◽  
Brain States ◽  
Electroencephalogram Eeg ◽  
Eeg Features

With the recent development of low-cost wearable electroencephalogram (EEG) recording systems, passive brain–computer interface (pBCI) applications are being actively studied for a variety of application areas, such as education, entertainment, and healthcare. Various EEG features have been employed for the implementation of pBCI applications; however, it is frequently reported that some individuals have difficulty fully enjoying the pBCI applications because the dynamic ranges of their EEG features (i.e., its amplitude variability over time) were too small to be used in the practical applications. Conducting preliminary experiments to search for the individualized EEG features associated with different mental states can partly circumvent this issue; however, these time-consuming experiments were not necessary for the majority of users whose dynamic ranges of EEG features are large enough to be used for pBCI applications. In this study, we tried to predict an individual user’s dynamic ranges of the EEG features that are most widely employed for pBCI applications from resting-state EEG (RS-EEG), with the ultimate goal of identifying individuals who might need additional calibration to become suitable for the pBCI applications. We employed a machine learning-based regression model to predict the dynamic ranges of three widely used EEG features known to be associated with the brain states of valence, relaxation, and concentration. Our results showed that the dynamic ranges of EEG features could be predicted with normalized root mean squared errors of 0.2323, 0.1820, and 0.1562, respectively, demonstrating the possibility of predicting the dynamic ranges of the EEG features for pBCI applications using short resting EEG data.

SYNCHRONIZATION MEASURES OF THE SCALP ELECTROENCEPHALOGRAM CAN DISCRIMINATE HEALTHY FROM ALZHEIMER'S SUBJECTS

2007 ◽  
Vol 17 (02) ◽  
pp. 61-69 ◽  
Author(s):  
MARK A. KRAMER ◽  
FEN-LEI CHANG ◽  
MAURICE E. COHEN ◽  
DONNA HUDSON ◽  
ANDREW J. SZERI
Keyword(s):  
Cognitive Impairment ◽  
Healthy Subjects ◽  
Phase Synchronization ◽  
Eyes Closed ◽  
Eeg Data ◽  
Two Measures ◽  
Left And Right ◽  
Scalp Electroencephalogram ◽  
Occipital Lobes ◽  
Electroencephalogram Eeg

Three synchronization measures are applied to scalp electroencephalogram (EEG) data collected from 20 patients diagnosed to have either: (1) no dementia, (2) mild cognitive impairment (MCI), or (3) Alzheimer's disease (AD). We apply the three synchronization measures — the phase synchronization, and two measures of nonlinear interdependency — to the data collected from awake patients resting with eyes closed. We show that the synchronization in potential between electrodes near the left and right occipital lobes provides a statistically significant discriminant between the healthy and AD subjects, and the MCI and AD subjects. None of the three measures appears able to distinguish between the healthy and MCI subjects, although MCI subjects show synchronization values intermediate between healthy subjects (with high synchronization values) and AD subjects (with low synchronization values) on average.

scholarly journals CameraEEG: Synchronized Recording of Video with Electroencephalogram data on an Android Application

2021 ◽  
Author(s):  
Srihari Madhavan ◽  
Doli Hazarika ◽  
Cota Navin Gupta
Keyword(s):  
Button Press ◽  
Android Application ◽  
Eyes Closed ◽  
Android Os ◽  
Eeg Data ◽  
Spectrum Density ◽  
Recorded Data ◽  
The Subject ◽  
Electroencephalogram Eeg ◽  
Everyday Life Activities

We present a novel android application named CameraEEG that enables synchronized acquisition of Electroencephalogram(EEG) and camera data using a smartphone. Audio-visual events of interest experienced by the subject were also recorded using a button press on the CameraEEG app. Unlike lab-restricted experiments, which usually constrain the subject's mobility, this wearable solution enables monitoring of the human brain during everyday life activities. The app was built using Android SDK version 28 and Smarting mobi SDK from mbraintrain. It works on all android devices having a minimum Android OS - Lollipop. We successfully recorded thirty minutes of synchronized Video and EEG during eyes closed and walking tasks using the app. Event markers enabled by the subject using the app during walking tasks were also recorded. Timing tests showed that temporal synchronization of video and EEG data was good. We analysed the recorded data and were able to identify the task performed by the subject from the event markers. The power spectrum density of the two tasks showed different power spectrums with a peak in the alpha band for eyes closed task. We also provide android studio codes for download and detailed help documentation for the community to test the developed application.

scholarly journals Classification of Resting-State Status Based on Sample Entropy and Power Spectrum of Electroencephalography (EEG)

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Ahmed M. A. Mohamed ◽  
Osman N. Uçan ◽  
Oğuz Bayat ◽  
Adil Deniz Duru
Keyword(s):  
Resting State ◽  
Sample Entropy ◽  
Support Vector ◽  
K Nearest Neighbors ◽  
Linear Discriminant ◽  
Frequency Bands ◽  
Eyes Closed ◽  
Eeg Data ◽  
Eyes Open ◽  
The Brain

An electroencephalogram (EEG) is a significant source of diagnosing brain issues. It is also a mediator between the external world and the brain, especially in the case of any mental illness; however, it has been widely used to monitor the dynamics of the brain in healthy subjects. This paper discusses the resting state of the brain with eyes open (EO) and eyes closed (EC) by using sixteen channels by the use of conventional frequency bands and entropy of the EEG signal. The Fast Fourier Transform (FFT) and sample entropy (SE) of each sensor are computed as methods of feature extraction. Six classifiers, including logistic regression (LR), K-Nearest Neighbors (KNN), linear discriminant (LD), decision tree (DT), support vector machine (SVM), and Gaussian Naive Bayes (GNB) are used to discriminate the resting states of the brain based on the extracted features. EEG data were epoched with one-second-length windows, and they were used to compute the features to classify EO and EC conditions. Results showed that the LR and SVM classifiers had the highest average classification accuracy (97%). Accuracies of LD, KNN, and DT were 95%, 93%, and 92%, respectively. GNB gained the least accuracy (86%) when conventional frequency bands were used. On the other hand, when SE was used, the average accuracies of SVM, LD, LR, GNB, KNN, and DT algorithms were 92% 90%, 89%, 89%, 86%, and 86%, respectively.

scholarly journals Atypical Electrophysiological Indices of Eyes-Open and Eyes-Closed Resting-State in Children and Adolescents with ADHD and Autism

2020 ◽  
Vol 10 (5) ◽  
pp. 272
Author(s):  
Alessio Bellato ◽  
Iti Arora ◽  
Puja Kochhar ◽  
Chris Hollis ◽  
Madeleine J. Groom
Keyword(s):  
Resting State ◽  
Spectral Power ◽  
Children With Autism ◽  
Alpha Power ◽  
Brain Functioning ◽  
Hyperactivity Disorder ◽  
Eyes Closed ◽  
Eeg Data ◽  
Eyes Open ◽  
The Difference

Investigating electrophysiological measures during resting-state might be useful to investigate brain functioning and responsivity in individuals under diagnostic assessment for attention deficit hyperactivity disorder (ADHD) and autism. EEG was recorded in 43 children with or without ADHD and autism, during a 4-min-long resting-state session which included an eyes-closed and an eyes-open condition. We calculated and analyzed occipital absolute and relative spectral power in the alpha frequency band (8–12 Hz), and alpha reactivity, conceptualized as the difference in alpha power between eyes-closed and eyes-open conditions. Alpha power was increased during eyes-closed compared to eyes-open resting-state. While absolute alpha power was reduced in children with autism, relative alpha power was reduced in children with ADHD, especially during the eyes-closed condition. Reduced relative alpha reactivity was mainly associated with lower IQ and not with ADHD or autism. Atypical brain functioning during resting-state seems differently associated with ADHD and autism, however further studies replicating these results are needed; we therefore suggest involving research groups worldwide by creating a shared and publicly available repository of resting-state EEG data collected in people with different psychological, psychiatric, or neurodevelopmental conditions, including ADHD and autism.

scholarly journals Safety and data quality of simultaneous EEG-fMRI using multi-band fMRI imaging

2020 ◽  
Author(s):  
Maximillian K. Egan ◽  
Ryan Larsen ◽  
Jonathan Wirsich ◽  
Brad P. Sutton ◽  
Sepideh Sadaghiani
Keyword(s):  
Data Quality ◽  
Resting State ◽  
Human Subjects ◽  
Rf Power ◽  
Power Deposition ◽  
Eyes Closed ◽  
Eeg Data ◽  
Magnetic Field Magnitude ◽  
Rf Power Deposition ◽  
Multi Band

AbstractPurposeSimultaneously recorded electroencephalography and functional magnetic resonance imaging (EEG-fMRI) is highly informative yet technically challenging. Until recently, there has been little information about the data quality and safety when used with newer multi-band (MB) fMRI sequences. Here, we assessed heating-related safety of a MB protocol on a phantom, then evaluated EEG quality recorded concurrently with the MB protocol on humans.Materials and MethodsWe compared radiofrequency (RF)-related heating and magnetic field magnitude () of a fast MB fMRI sequence with whole-brain coverage (TR=440ms, MB factor=4) against a previously recommended, safe single-band (SB) sequence using a phantom outfitted with a 64-channel EEG cap. Temperatures were recorded at the ECG and TP7 electrodes using a fluoroptic thermometer. Next, 6 human subjects underwent eyes-closed resting state EEG-fMRI with the MB sequence. EEG data quality was assessed by the ability to remove gradient and cardioballistic artifacts and a clean spectrogram.ResultsRF induced heating was lower at both electrodes in the MB sequence compared to the SB sequence at ratios of 0.7 and 0.8, respectively. These ratios are slightly greater than the ratio of RF power deposition of the sequences, which is 0.64. However, our results are consistent with the use of RF power deposition, characterized by , in predicting less heating in the MB sequence than the SB sequence. In the resting state EEG data, gradient and cardioballistic artifacts were successfully removed using traditional template subtraction. All subjects showed an individual alpha peak in the spectrogram with a posterior topography characteristic of eyes-closed EEG.ConclusionsOur study shows that is a useful indication of the relative heating of fMRI protocols. This observation indicates that simultaneous EEG-fMRI recordings using this MB sequence can be safe in terms of RF-related heating, and that EEG data recorded using this sequence is of acceptable quality.

scholarly journals Spectral Power Density analysis of the resting-state as a marker of the central effects of opioid use in fibromyalgia

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Maxciel Zortea ◽  
Gerardo Beltran ◽  
Rael Lopes Alves ◽  
Paul Vicuña ◽  
Iraci L. S. Torres ◽  
...  
Keyword(s):  
Power Density ◽  
Resting State ◽  
Spectral Power ◽  
Surrogate Marker ◽  
Spectral Power Density ◽  
Beta Activity ◽  
Opioid Use ◽  
Eyes Closed ◽  
Promising Tool ◽  
Opioid Users

AbstractSpectral power density (SPD) indexed by electroencephalogram (EEG) recordings has recently gained attention in elucidating neural mechanisms of chronic pain syndromes and medication use. We compared SPD variations between 15 fibromyalgia (FM) women in use of opioid in the last three months (73.33% used tramadol) with 32 non-users. EEG data were obtained with Eyes Open (EO) and Eyes Closed (EC) resting state. SPD peak amplitudes between EO-EC were smaller in opioid users in central theta, central beta, and parietal beta, and at parietal delta. However, these variations were positive for opioid users. Multivariate analyses of variance (ANOVAs) revealed that EO-EC variations in parietal delta were negatively correlated with the disability due to pain, and central and parietal beta activity variations were positively correlated with worse sleep quality. These clinical variables explained from 12.5 to 17.2% of SPD variance. In addition, central beta showed 67% sensitivity / 72% specificity and parietal beta showed 73% sensitivity/62% specificity in discriminating opioid users from non-users. These findings suggest oscillations in EEG might be a sensitive surrogate marker to screen FM opioid users and a promising tool to understand the effects of opioid use and how these effects relate to functional and sleep-related symptoms.

scholarly journals Use of artificial intelligence on Electroencephalogram (EEG) waveforms to predict failure in early school grades in children from a rural cohort in Pakistan

PLoS ONE ◽  
2021 ◽  
Vol 16 (2) ◽  
pp. e0246236
Author(s):  
Muneera A. Rasheed ◽  
Prem Chand ◽  
Saad Ahmed ◽  
Hamza Sharif ◽  
Zahra Hoodbhoy ◽  
...  
Keyword(s):  
Machine Learning ◽  
Academic Achievement ◽  
Sensitivity And Specificity ◽  
Home Environment ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Screening Measure ◽  
Early Grades ◽  
Eeg Data ◽  
Electroencephalogram Eeg

Universal primary education is critical for individual academic growth and overall adult productivity of nations. Estimates indicate that 25% of 59 million primary age out of school children drop out and early grade failure is one of the factors. An objective and feasible screening measure to identify at-risk children in the early grades can help to design appropriate interventions. The objective of this study was to use a Machine Learning algorithm to evaluate the power of Electroencephalogram (EEG) data collected at age 4 in predicting academic achievement at age 8 among rural children in Pakistan. Demographic and EEG data from 96 children of a cohort along with their academic achievement in grade 1–2 measured using an academic achievement test of Math and language at the age of 7–8 years was used to develop the machine learning algorithm. K- Nearest Neighbor (KNN) classifier was used on different model combinations of EEG, sociodemographic and home environment variables. KNN model was evaluated using 5 Stratified Folds based on the sensitivity and specificity. In the current dataset, 55% and 74% failed in the mathematics and language test respectively. On testing data across each fold, the mean sensitivity and specificity was calculated. Sensitivity was similar when EEG variables were combined with sociodemographic, and home environment (Math = 58.7%, Language = 66.3%) variables but specificity improved (Math = 43.4% to 50.6% and Language = 32% to 60%). The model requires further validation for EEG to be used as a screening measure with adequate sensitivity and specificity to identify children in their preschool age who may be at high risk of failure in early grades.

scholarly journals Resting state EEG as a biomarker of Parkinson’s disease: Influence of measurement conditions

2020 ◽  
Author(s):  
Henry Railo ◽  
Ilkka Suuronen ◽  
Valtteri Kaasinen ◽  
Mika Murtojärvi ◽  
Tapio Pahikkala ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Resting State ◽  
Search Algorithm ◽  
Effective Means ◽  
Cost Effective ◽  
Eyes Closed ◽  
Promising Tool ◽  
Eyes Open ◽  
Healthy Control

AbstractResting state electroencephalographic (EEG) recording could provide cost-effective means to aid in the detection of neurological disorders such as Parkinson’s disease (PD). We examined how many electrodes are needed for classification of PD based on EEG, which electrode locations provide most value for classification, and whether data recorded eyes open or closed yield comparable results. We used a nested cross-validated classifier which included a budget-based search algorithm for selecting the optimal electrodes for classification. By iterating over variable budgets, we show that with eyes open recording, only 10 electrodes, localized over motor and occipital areas enable relatively accurate classification (AUC = .82) between PD patients (N=20) and age-matched healthy control participants (N=20). Classification accuracy only slightly increased when all 64 electrodes were included (AUC = .85). With the data recorded eyes closed, classification was not statistically significantly above chance even with full set of 64 electrodes (AUC = .55). These results show that classification based on small number of EEG electrodes is a promising tool for classifying PD, but measurement conditions and electrode locations can have a significant effect on classifier performance.

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