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

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.

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EEG Analysis of Imagined Speech

Disruptive Technology ◽

10.4018/978-1-5225-9273-0.ch033 ◽

2020 ◽

pp. 679-692

Author(s):

Sadaf Iqbal ◽

Muhammed Shanir P.P. ◽

Yusuf Uzzaman Khan ◽

Omar Farooq

Keyword(s):

Mean Absolute Deviation ◽

Eeg Analysis ◽

Absolute Deviation ◽

Scalp Eeg ◽

Computer Interfaces ◽

Eeg Data ◽

Scalp Electroencephalogram ◽

Electroencephalogram Eeg ◽

Control State ◽

Excellent Tool

Scalp electroencephalogram (EEG) is one of the most commonly used methods to acquire EEG data for brain-computer interfaces (BCIs). Worldwide a large number of people suffer from disabilities which impair normal communication. Communication BCIs are an excellent tool which helps the affected patients communicate with others. In this paper scalp EEG data is analysed to discriminate between the imagined vowel sounds /a/, /u/ and no action or rest as control state. Mean absolute deviation (MAD) and Arithmetic mean are used as features to classify data into one of the classes /a/, /u/ or rest. With high classification accuracies of 87.5-100% for two class problem and 78.33-96.67% for three class problem that have been obtained in this work, this algorithm can be used in communication BCIs, to develop speech prosthesis and in synthetic telepathy systems.

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EEG Analysis of Imagined Speech

International Journal of Rough Sets and Data Analysis ◽

10.4018/ijrsda.2016040103 ◽

2016 ◽

Vol 3 (2) ◽

pp. 32-44

Author(s):

Sadaf Iqbal ◽

Muhammed Shanir P.P. ◽

Yusuf Uzzaman Khan ◽

Omar Farooq

Keyword(s):

Mean Absolute Deviation ◽

Eeg Analysis ◽

Absolute Deviation ◽

Scalp Eeg ◽

Computer Interfaces ◽

Eeg Data ◽

Scalp Electroencephalogram ◽

Electroencephalogram Eeg ◽

Control State ◽

Excellent Tool

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

10.1101/2021.12.21.472324 ◽

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.

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Authentication Based on Eyes-Closed EEG

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.811.426 ◽

2013 ◽

Vol 811 ◽

pp. 426-429

Author(s):

Xue Min Zhang ◽

Zeng Gang Xiong ◽

Zhen Dong Mu

Keyword(s):

Feature Extraction ◽

Data Analysis ◽

Evoked Potentials ◽

Phase Synchronization ◽

Recognition Rate ◽

Self Control ◽

Synchronization Method ◽

Eyes Closed ◽

Eeg Data ◽

Sample Set

Authentication more prominent characteristics of self-control, identify the relative characteristics and identification, data analysis more difficult, phase synchronization method of this paper, a set of EEG data collected for analysis to each experimenter as a sample set for feature extraction, and then in a group of experimenters internal identification, the recognition rate up to 45%, and the use of event evoked potentials for the accuracy of the identification has a large gap, but in some less demanding recognition rate under a certain value.

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Identifying Mild Cognitive Impairment in Parkinson’s Disease With Electroencephalogram Functional Connectivity

Frontiers in Aging Neuroscience ◽

10.3389/fnagi.2021.701499 ◽

2021 ◽

Vol 13 ◽

Author(s):

Min Cai ◽

Ge Dang ◽

Xiaolin Su ◽

Lin Zhu ◽

Xue Shi ◽

...

Keyword(s):

Parkinson’S Disease ◽

Parkinson's Disease ◽

Cognitive Impairment ◽

Functional Connectivity ◽

Pathophysiological Mechanism ◽

Frequency Bands ◽

Eyes Closed ◽

Theta Frequency ◽

Connectivity Patterns ◽

Left And Right

ObjectiveCognitive impairment occurs frequently in Parkinson’s disease (PD) and negatively impacts the patient’s quality of life. However, its pathophysiological mechanism remains unclear, hindering the development of new therapies. Changes in brain connectivity are related to cognitive impairment in patients with PD, with the dorsolateral prefrontal cortex (DLPFC) being considered the essential region related to PD cognitive impairment. Nevertheless, few studies have focused on the global connectivity responsible for communication with the DLPFC node, the posterior division of the middle frontal gyrus (PMFG) in patients with PD; this was the focus of this study.MethodsWe applied resting-state electroencephalography (EEG) and calculated a reliable functional connectivity measurement, the debiased weighted phase lag index (dWPLI), to examine inter-regional functional connectivity in 68 patients with PD who were classified into two groups according to their cognitive condition.ResultsWe observed that altered left and right PMFG-based functional connectivity associated with cognitive impairment in patients with PD in the theta frequency bands under the eyes closed condition (r = −0.426, p < 0.001 and r = −0.437, p < 0.001, respectively). Exploratory results based on the MoCA subdomains indicated that poorer visuospatial function was associated with higher right PMFG-based functional connectivity (r = −0.335, p = 0.005), and poorer attention function was associated with higher left and right PMFG-based functional connectivity (r = −0.380, p = 0.001 and r = −0.256, p = 0.035, respectively). Further analysis using logistic regression and receiver operating characteristic (ROC) curves found that this abnormal functional connectivity was an independent risk factor for cognitive impairment [odds ratio (OR): 2.949, 95% confidence interval (CI): 1.294–6.725, p = 0.01 for left PMFG; OR: 11.278, 95% CI: 2.578–49.335, p = 0.001 for right PMFG, per 0.1 U], and provided moderate classification power to discriminate between cognitive abilities in patients with PD [area under the ROC curve (AUC) = 0.770 for left PMFG; AUC = 0.809 for right PMFG].ConclusionThese preliminary findings indicate that abnormal PMFG-based functional connectivity patterns associated with cognitive impairment in the theta frequency bands under the eyes closed condition and altered functional connectivity patterns have the potential to act as reliable biomarkers for identifying cognitive impairment in patients with PD.

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RESEARCH OF HUMAN POSTURAL BALANCE PARAMETERS

Mokslas - Lietuvos ateitis ◽

10.3846/mla.2010.065 ◽

2010 ◽

Vol 2 (4) ◽

pp. 35-38

Author(s):

Julius Griškevičius ◽

Edita Jarmalienė ◽

Inga Maskeliūnaitė

Keyword(s):

Pressure Distribution ◽

Present Article ◽

Postural Balance ◽

Load Distribution ◽

Healthy Subjects ◽

Experimental Measurements ◽

Centre Of Mass ◽

Eyes Closed ◽

Left And Right

In present article postural balance between subjects with stroke and healthy subjects, is being investigated with eyes opened and eyes closed. In the research participated 30 healthy subjects and 15 subjects with stroke. At the same time two experimental measurements were performed – postural balance was measured using balance platform and oscillations of the centre of mass were observed using two-axial accelerometer. It was noted, that amplitudes of subjects with stroke were larger almost two times than control group’s of healthy subjects. It was find out, that ratios of pressure distribution on both left and right legs are in range from 1 to 0.9 for healthy subjects, and ratios below 0.9 are common for subjects with stroke. When subjects were standing with eyes closed, sway amplitudes were higher and the ratios of load distribution on left and right legs were lower.

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Decoding the covert shift of spatial attention from electroencephalographic signals permits reliable control of a brain-computer interface

10.1101/2020.07.29.226456 ◽

2020 ◽

Author(s):

Christoph Reichert ◽

Stefan Dürschmid ◽

Mandy V. Bartsch ◽

Jens-Max Hopf ◽

Hans-Jochen Heinze ◽

...

Keyword(s):

Event Related Potentials ◽

Right Visual Field ◽

Spatial Filters ◽

Computer Interfaces ◽

Eeg Data ◽

Related Potentials ◽

Left And Right ◽

Viable Approach ◽

Number Of Classes ◽

Electroencephalogram Eeg

AbstractObjectiveOne of the main goals of brain-computer interfaces (BCI) is to restore communication abilities in patients. BCIs often use event-related potentials (ERPs) like the P300 which signals the presence of a target in a stream of stimuli. The P300 and related approaches, however, are inherently limited, as they require many stimulus presentations to obtain a usable control signal. Many approaches depend on gaze-direction to focus the target, which is also not a viable approach in many cases, because eye movements might be impaired in potential users. Here we report on a BCI that avoids both shortcomings by decoding spatial target information, independent of gaze shifts.ApproachWe present a new method to decode from the electroencephalogram (EEG) covert shifts of attention to one out of four targets simultaneously presented in the left and right visual field. The task is designed to evoke the N2pc component – a hemisphere lateralized response, elicited over the occipital scalp contralateral to the attended target. The decoding approach involves decoding of the N2pc based on data-driven estimation of spatial filters and a correlation measure.Main resultsDespite variability of decoding performance across subjects, 22 out of 24 subjects performed well above chance level. Six subjects even exceeded 80% (cross-validated: 89%) correct predictions in a four-class discrimination task. Hence, the single-trial N2pc proves to be a component that allows for reliable BCI control. An offline analysis of the EEG data with respect to their dependence on stimulation time and number of classes demonstrates that the present method is also a workable approach for two-class tasks.SignificanceOur method extends the range of strategies for gaze-independent BCI control. The proposed decoding approach has the potential to be efficient in similar applications intended to decode ERPs.

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Diagnosing Schizophrenia Using Effective Connectivity of Resting-State EEG Data

Algorithms ◽

10.3390/a14050139 ◽

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.

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Approximate Entropy of the Electroencephalogram in Healthy Awake Subjects and Absence Epilepsy Patients

Clinical EEG and Neuroscience ◽

10.1177/155005940503600309 ◽

2005 ◽

Vol 36 (3) ◽

pp. 188-193 ◽

Cited By ~ 29

Author(s):

Naoto Burioka ◽

Germaine Cornélissen ◽

Yoshihiro Maegaki ◽

Franz Halberg ◽

Daniel T. Kaplan ◽

...

Keyword(s):

Healthy Subjects ◽

Brain Activity ◽

Epileptic Seizures ◽

Approximate Entropy ◽

Surrogate Data ◽

Absence Epilepsy ◽

Eeg Signals ◽

Eyes Closed ◽

The Mean ◽

Electroencephalogram Eeg

The approximate entropy (ApEn) of signals in the electroencephalogram (EEG) was evaluated in 8 healthy volunteers and in 10 patients with absence epilepsy, both during seizure-free and seizure intervals. We estimated the nonlinearity of each 3-sec EEG segment using surrogate data methods. The mean (± SD) ApEn in EEG was 0.83 ± 0.22 in healthy subjects awake with eyes closed. It was significantly lower during epileptic seizures (0.48 ± 0.05) than during seizure-free intervals (0.80 ± 0.13) (P<0.001). Nonlinearity was clearly detected in EEG signals from epileptic patients during seizures but not during seizure-free intervals or in EEG signals from healthy subjects. The ApEn of EEG signals estimated over consecutive intervals could serve to determine pathological brain activity such as that occurring during absence epilepsy.

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Entropy as Measure of Brain Networks’ Complexity in Eyes Open and Closed Conditions

Symmetry ◽

10.3390/sym13112178 ◽

2021 ◽

Vol 13 (11) ◽

pp. 2178

Author(s):

Fabrizio Vecchio ◽

Francesca Miraglia ◽

Chiara Pappalettera ◽

Alessandro Orticoni ◽

Francesca Alù ◽

...

Keyword(s):

Chaotic Behavior ◽

Brain Networks ◽

Approximate Entropy ◽

Brain State ◽

Eyes Closed ◽

Eeg Data ◽

Eyes Open ◽

Brain States ◽

System Disorder ◽

Electroencephalogram Eeg

Brain complexity can be revealed even through a comparison between two trivial conditions, such as eyes open and eyes closed (EO and EC respectively) during resting. Electroencephalogram (EEG) has been widely used to investigate brain networks, and several non-linear approaches have been applied to investigate EO and EC signals modulation, both symmetric and not. Entropy is one of the approaches used to evaluate the system disorder. This study explores the differences in the EO and EC awake brain dynamics by measuring entropy. In particular, an approximate entropy (ApEn) was measured, focusing on the specific cerebral areas (frontal, central, parietal, occipital, temporal) on EEG data of 37 adult healthy subjects while resting. Each participant was submitted to an EO and an EC resting EEG recording in two separate sessions. The results showed that in the EO condition the cerebral networks of the subjects are characterized by higher values of entropy than in the EC condition. All the cerebral regions are subjected to this chaotic behavior, symmetrically in both hemispheres, proving the complexity of networks dynamics dependence from the subject brain state. Remarkable dynamics regarding cerebral networks during simple resting and awake brain states are shown by entropy. The application of this parameter can be also extended to neurological conditions, to establish and monitor personalized rehabilitation treatments.

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