On the Credibility Judgment of Eyewitness Statements using Tension Index according to Electroencephalogram (EEG) Analysis

2021 ◽  
Vol 15 (4) ◽  
pp. 274-280
Author(s):  
Seong-Kweon Shin
Keyword(s):  
Eeg Analysis ◽  
Tension Index ◽  
Electroencephalogram Eeg

scholarly journals Special Issue on Brain Machine/Computer Interface and its Application

2020 ◽  
Vol 32 (4) ◽  
pp. 723-723
Author(s):  
Shoichiro Fujisawa ◽  
Minoru Fukumi ◽  
Jianting Cao ◽  
Yasue Mitsukura ◽  
Shin-ichi Ito
Keyword(s):  
Brain Activity ◽  
Computer Interface ◽  
Eeg Analysis ◽  
Special Issue ◽  
Eeg Signals ◽  
Auditory P300 ◽  
Environment Control ◽  
Electroencephalogram Eeg ◽  
Human Support

Brain machine/computer interface (BMI/BCI) technologies are based on analyzing brain activity to control machines and support the communication of commands and messages. To sense brain activities, a functional NIRS and electroencephalogram (EEG) that has been developed for that purpose is often employed. Analysis techniques and algorithms for the NIRS and EEG signals have also been created, and human support systems in the form of BMI/BCI applications have been developed. In the field of rehabilitation, BMI/BCI is used to control environment control systems and electric wheelchairs. In medicine, BMI/BCI is used to assist in communications for patient support. In industry, BMI/BCI is used to analyze sensibility and develop novel games. This special issue on Brain Machine/Computer Interface and its Application includes six interesting papers that cover the following topics: an EEG analysis method for human-wants detection, cognitive function using EEG analysis, auditory P300 detection, a wheelchair control BCI using SSVEP, a drone control BMI based on SSVEP that uses deep learning, and an improved CMAC model. We thank all authors and reviewers of the papers and the Editorial Board of Journal of Robotics and Mechatronics for its help with this special issue.

EEG Analysis of Imagined Speech

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.

EEG Analysis of Imagined Speech

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

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.

Electroencephalographic Evaluation in Fibromyalgia: A Systematic Review

2021 ◽  
pp. 155005942199712
Author(s):  
Géssika Araújo de Melo ◽  
Marcela Laís Lima Holmes Madruga ◽  
Nelson Torro
Keyword(s):  
Systematic Review ◽  
Systematic Reviews ◽  
Database Search ◽  
Promising Method ◽  
Eeg Analysis ◽  
Analysis Method ◽  
Exclusion Criteria ◽  
Meta Analyses ◽  
Electroencephalogram Eeg

Introduction. The evaluation of individuals with fibromyalgia is challenging. Electroencephalography is a promising resource for identifying physiological biomarkers in fibromyalgia, contributing to its diagnosis. Objective. To review studies involving the use of electroencephalography to evaluate individuals with fibromyalgia. Method. A systematic review of studies published in the PubMed, Lilacs, and SciELO databases from 2001 to 2020 was conducted. The keywords used were electroencephalogram, electroencephalography, and fibromyalgia. The database search complied with the preferred reporting items for systematic reviews and meta-analyses (PRISMA) criteria. Results. A total of 136 articles were identified after a database search using the keywords “fibromyalgia” AND “electroencephalography”, and 131 articles were found using the keywords “fibromyalgia” AND “electroencephalogram” (EEG). In the end, 20 articles remained after applying the exclusion criteria. The data was organized into subcategories related to the form of use, protocols, electroencephalographic findings in patients with fibromyalgia, and the EEG analysis method. Conclusion. Electroencephalography is a promising method for identifying and characterizing biomarkers for fibromyalgia.

scholarly journals Mental Workload Detection Based on EEG Analysis

2021 ◽  
Author(s):  
José Yauri ◽  
Aura Hernández-Sabaté ◽  
Paul Folch ◽  
Débora Gil
Keyword(s):  
Mental Workload ◽  
Mental States ◽  
Health Conditions ◽  
Eeg Analysis ◽  
Proposed Model ◽  
Binary Neural Network ◽  
High Level ◽  
Leave One Out ◽  
Electroencephalogram Eeg ◽  
Well Models

The study of mental workload becomes essential for human work efficiency, health conditions and to avoid accidents, since workload compromises both performance and awareness. Although workload has been widely studied using several physiological measures, minimising the sensor network as much as possible remains both a challenge and a requirement. Electroencephalogram (EEG) signals have shown a high correlation to specific cognitive and mental states like workload. However, there is not enough evidence in the literature to validate how well models generalize in case of new subjects performing tasks of a workload similar to the ones included during model’s training. In this paper we propose a binary neural network to classify EEG features across different mental workloads. Two workloads, low and medium, are induced using two variants of the N-Back Test. The proposed model was validated in a dataset collected from 16 subjects and shown a high level of generalization capability: model reported an average recall of 81.81% in a leave-one-out subject evaluation.

scholarly journals Nonlinear Electroencephalogram (EEG) Analysis in Sleep Medicine

2021 ◽  
Author(s):  
Morteza Zangeneh Soroush
Keyword(s):  
Sleep Medicine ◽  
Eeg Analysis ◽  
Electroencephalogram Eeg

The article's abstract is not available.  

scholarly journals Rodent EEG: Expanding the Spectrum of Analysis

2020 ◽  
Vol 20 (3) ◽  
pp. 149-153 ◽  
Author(s):  
Atul Maheshwari
Keyword(s):  
Background Activity ◽  
Great Promise ◽  
Rodent Models ◽  
Eeg Analysis ◽  
Computational Power ◽  
Epilepsy Research ◽  
Eeg Changes ◽  
Software Platforms ◽  
Electroencephalogram Eeg ◽  
Models Of Epilepsy

In epilepsy research, the analysis of rodent electroencephalogram (EEG) has been performed by many laboratories with a variety of techniques. However, the acquisition and basic analysis of rodent EEG have only recently been standardized. Since a number of software platforms and increased computational power have become widely available, advanced rodent EEG analysis is now more accessible to investigators working with rodent models of epilepsy. In this review, the approach to the analysis of rodent EEG will be examined, including the evaluation of both epileptiform and background activity. Major caveats when employing these analyses, cellular and circuit-level correlates of EEG changes, and important differences between rodent and human EEG are also reviewed. The currently available techniques show great promise in gaining a deeper understanding of the complexities hidden within the EEG in rodent models of epilepsy.

scholarly journals ANALYSIS OF PEAK-WAVE DISCHARGES OF EEG WITH THE USE OF WAVELET TRANSFORMATIONS

2020 ◽  
pp. 224-234
Author(s):  
G.V. Kit
Keyword(s):  
Wavelet Transforms ◽  
Neurological Diseases ◽  
Absence Epilepsy ◽  
Eeg Analysis ◽  
Eeg Signals ◽  
Time Intervals ◽  
Signal Quantization ◽  
Traditional Approaches ◽  
Electroencephalogram Eeg ◽  
Wavelet Transformations

The method of analysis of electroencephalograms (EEG) on the basis of wavelet transformations is offered. Electroencephalogram (EEG) analysis is widely used in clinical practice for diagnosing such neurological diseases as epilepsy, Parkinson's disease and others. Traditional approaches to EEG analysis, generally accepted in the clinical diagnosis of diseases, are due to the fact that for a certain time after the stimulus, the EEG amplitudes are calculated at time intervals that depend on the frequency of signal quantization. Therefore, it is important to develop algorithms for classifying EEG signals using wavelet transforms. The analysis of peak-wave EEG discharges, which are indicators of the presence or absence of absence epilepsy, was performed. The EEG recording areas were decomposed into the main EEG frequency bands. Wavelet transform in combination with artificial neural networks makes it possible to implement a classifier based on the energy distribution of the components of the EEG signal. Determining the activity of individual components of EEG signals, as well as the materiality of the processes that take place in the sources of these waves, may be the subject of further research.

scholarly journals SEPARATION OF ELECTROENCEPHALOGRAM LOW-FREQUENCY COMPONENTS ON THE BASIS OF THE STOCHASTIC RESONANCE EFFECT

2021 ◽  
pp. 135-137
Author(s):  
O.I. Kharchenko ◽  
Yu.F. Lonin ◽  
L.P. Zabrodina ◽  
V.M. Kartashov
Keyword(s):  
Numerical Computation ◽  
Stochastic Resonance ◽  
Resonance Effect ◽  
Low Frequency ◽  
Eeg Analysis ◽  
Low Frequencies ◽  
Frequency Components ◽  
Electroencephalogram Eeg ◽  
Δ Rhythm ◽  
Good Agreement

The paper describes the method for electroencephalogram (EEG) analysis based on the stochastic resonance (SR) effect. The numerical computation has provided the separation of low frequency components that fall within the δ-rhythm band. This is currently central in the neuropathology diagnostics, because the presence of low frequencies in the EEG is abnormal and bears witness to the disease. For verification, the data obtained with the use of the SR effect have been compared with the computations based on the autocorrelation function (ACF) processing. The comparison has shown their good agreement.

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