Comparative Study of Time Frequency Analysis Application on Abnormal EEG Signals

Ridouh, Abdelhakim; Boutana, Daoud; Benidir, M.

doi:10.1007/978-3-319-48929-2_28

Cited by 3 publications

(2 citation statements)

References 21 publications

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“…Neurons send electrical signals to control the human body and can be measured using electroencephalography (EEG), which measures the electrical activity of the brain by recording it via electrodes placed either on the cortex or the scalp. The signal generated by this electrical activity is non-stationary and complex random signals [1,2]. The EEG signal contains a lot of information about the human brain functions, so the EEG analysis and information extraction are very complicated.…”

Section: Introductionmentioning

confidence: 99%

Finding the discriminative frequencies of motor electroencephalography signal using genetic algorithm

Najeeb¹,

Rikabi²,

Ali³

2021

TELKOMNIKA

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A crucial part of the brain-computer interface is a classification of electroencephalography (EEG) motor tasks. Artifacts such as eye and muscle movements corrupt EEG signal and reduce the classification performance. Many studies try to extract not redundant and discriminative features from EEG signals. Therefore, this study proposed a signal preprocessing and feature extraction method for EEG classification. It consists of removing the artifacts by using discrete fourier transform (DFT) as an ideal filter for specific frequencies. It also cross-correlates the EEG channels with the effective channels to emphases the EEG motor signals. Then the resultant from cross correlation are statistical calculated to extract feature for classifying a left and right finger movements using support vector machine (SVM). The genetic algorithm was applied to find the discriminative frequencies of DFT for the two EEG classes signal. The performance of the proposed method was determined by finger movement classification of 13 subjects and the experiments show that the average accuracy is above 93 percent.

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Section: Introductionmentioning

confidence: 99%

Finding the discriminative frequencies of motor electroencephalography signal using genetic algorithm

Najeeb¹,

Rikabi²,

Ali³

2021

TELKOMNIKA

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“…These time-varying records produce potential differences because of the electrical cerebral activity. The signal generated by this electrical activity is a complex random signal and is non-stationary [1]. Signals are normally composed of four brainwave groups: Delta, Theta, Alpha, and Beta [2].…”

Section: Introductionmentioning

confidence: 99%

Analysis of EEG Signal Processing Techniques based on Spectrograms

Ramos¹,

Olvera²,

Olmos³

2017

RCS

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Current approaches for the processing and analysis of EEG signals consist mainly of three phases: preprocessing, feature extraction, and classification. The analysis of EEG signals is through different domains: time, frequency, or time-frequency; the former is the most common, while the latter shows competitive results, implementing different techniques with several advantages in analysis. This paper aims to present a general description of works and methodologies of EEG signal analysis in time-frequency, using Short Time Fourier Transform (STFT) as a representation or a spectrogram to analyze EEG signals.

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Novel quadratic time-frequency features in EEG signals for robust detection of epileptic seizure

Ghembaza

Djebbari

2023

Res. Biomed. Eng.

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Comparative Study of Time Frequency Analysis Application on Abnormal EEG Signals

Cited by 3 publications

References 21 publications

Finding the discriminative frequencies of motor electroencephalography signal using genetic algorithm

Finding the discriminative frequencies of motor electroencephalography signal using genetic algorithm

Analysis of EEG Signal Processing Techniques based on Spectrograms

Novel quadratic time-frequency features in EEG signals for robust detection of epileptic seizure

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