Analysis of EEG signals during epileptic and alcoholic states using AR modeling techniques

Faust, Oliver; Acharya, U. Rajendra; Allen, Alastair Robert; Lin, Chin-Chiuan

doi:10.1016/j.rbmret.2007.11.003

Cited by 112 publications

(60 citation statements)

References 22 publications

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“…These features are fed to the classifier for automated classification. Faust et al [92] calculated the difference in the power distribution of EEG signals from healthy controls and people with alcoholism using different frequency domain parameters and classified the significant features using the ROC curve. They reported that Burg's method produced the most significant features for classification.…”

Section: Alcoholism Related-disorders Diagnosismentioning

confidence: 99%

Medical Big Data: Neurological Diseases Diagnosis Through Medical Data Analysis

2016

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Section: Alcoholism Related-disorders Diagnosismentioning

confidence: 99%

Medical Big Data: Neurological Diseases Diagnosis Through Medical Data Analysis

2016

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“…Most studies in the literature are related to the analysis of EEG signals associated with some pathology (Bennys et al, 2001;Bonanni et al, 2008;Dauwels et al, 2011;Faust et al, 2007;Natarajan et al, 2004) which demonstrates the lack of studies with the purpose of understanding the complex relationship between the EEG signal of healthy subjects and the aging. This study included only healthy subjects.…”

Section: Resultsmentioning

confidence: 99%

Analysis of the relationship between EEG signal and aging through Linear Discriminant Analysis (LDA)

Paiva¹,

Pereira²,

Almeida³

et al. 2012

RBEB

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This paper aims to establish the correlation between statistical parameters and Electroencephalographic (EEG) signals as a function of age, in subjects without neurological disorders. EEG signals were recorded during the task of following an Archimedes spiral. There were 59 healthy subjects who voluntarily participated in this study which were divided into 7 groups, aging between 20 to 86 years from both gender, in order to identify differences and allow discrimination between the features of each group. Initially, comparisons were made among several features (F20, F50, F80, F95, Mean Frequency, Root Mean Square value, Zero Crossings, Square of the Power Spectrum, Kurtosis, Skewness, Variance, Standard Deviation and Approximate Entropy) seeking separation between young and elderly groups. Furthermore, it was sought to correlate the statistical parameters and the entire age range. For this purpose it was used Linear Discriminant Analysis (LDA). The data were processed with MATLAB ® software. Through the LDA, significant differences were observed over the distinct age ranges. The tool has satisfactorily performed the separation of discriminant features by classifying groups of subjects in function of their age range.Keywords Electroencephalography, Linear Discriminant Analysis, Aging. Análise das relações entre o sinal EEG e o envelhecimento através da Análise Discriminante Linear (ADL)Resumo

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“…It is assumed to be a random process which is independent of the previous value of the signal. The Burg method is one of the AR spectral estimation methods which have been used in the EEG application [9]. The PSD from the calculation of the Burg method (ˆ( ) BURG P  ) is as follows:…”

Section: Features Extractionmentioning

confidence: 99%

“…Rifai The functional basic components of EEG-based system classification consists of several elements including signal measurement using the EEG and computational intelligence of the EEG signal which includes signal pre-processing, features extraction and classification processes with the output the classifier [6,7]. For the features extraction in the EEG analysis, power spectral density (PSD) has been used widely in the EEG analysis especially in fatigue study [8,9]. The power spectrum estimation is calculated from the Fast Fourier Transform (FFT) which converts the time based EEG data into the following frequency bands of EEG rhythms: delta (δ), theta (θ), alpha (α) and beta (β).…”

Section: Introductionmentioning

confidence: 99%

Enhancing accuracy of mental fatigue classification using advanced computational intelligence in an electroencephalography system

Chai

Tran

Craig

et al. 2014

2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society

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Abstract-A system using electroencephalography (EEG) signals could enhance the detection of mental fatigue while driving a vehicle. This paper examines the classification between fatigue and alert states using an autoregressive (AR) model-based power spectral density (PSD) as the features extraction method and fuzzy particle swarm optimization with cross mutated of artificial neural network (FPSOCM-ANN) as the classification method. Using 32-EEG channels, results indicated an improved overall specificity from 76.99% to 82.02%, an improved sensitivity from 74.92 to 78.99% and an improved accuracy from 75.95% to 80.51% when compared to previous studies. The classification using fewer EEG channels, with eleven frontal sites resulted in 77.52% for specificity, 73.78% for sensitivity and 75.65% accuracy being achieved. For ergonomic reasons, the configuration with fewer EEG channels will enhance capacity to monitor fatigue as there is less set-up time required.

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Analysis of EEG signals during epileptic and alcoholic states using AR modeling techniques

Cited by 112 publications

References 22 publications

Medical Big Data: Neurological Diseases Diagnosis Through Medical Data Analysis

Medical Big Data: Neurological Diseases Diagnosis Through Medical Data Analysis

Analysis of the relationship between EEG signal and aging through Linear Discriminant Analysis (LDA)

Enhancing accuracy of mental fatigue classification using advanced computational intelligence in an electroencephalography system

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