Literature Review on EEG Preprocessing, Feature Extraction, and Classifications Techniques

Dessouky, Mohamed M.; El-Sherbeny, A. S.; El‐Sayed, Ayman

doi:10.21608/mjeer.2019.64927

Cited by 34 publications

(13 citation statements)

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“…The signals forming each datapoint of these datasets are recorded either intracranial or from the scalp of humans or animals. Table 1 provides the supplementary information on each dataset, and also, the types of EEG datasets for epileptic seizures diagnosis using DL are listed in table 2. data noises that should be eliminated from the signals in the preprocessing step (Shoka et al, 2019;Kim, 2018;Peng, 2019;Jiang et al, 2019b). In some cases, the presence of multiple artifacts begets loss of EEG signals' substantial information between various noises and makes it challenging to diagnose epileptic seizures.…”

Section: Epileptic Seizures Datasetsmentioning

confidence: 99%

An overview of deep learning techniques for epileptic seizures detection and prediction based on neuroimaging modalities: Methods, challenges, and future works

Shoeibi,

Moridian,

Khodatars

et al. 2021

Preprint

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Epileptic seizures are a type of neurological disorder that affect many people worldwide. Specialist physicians and neurologists take advantage of structural and functional neuroimaging modalities to diagnose various types of epileptic seizures. Neuroimaging modalities assist specialist physicians considerably in analyzing brain tissue and the changes made in it. One method to accelerate the accurate and fast diagnosis of epileptic seizures is to employ computer aided diagnosis systems (CADS) based on artificial intelligence (AI) and functional

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Section: Epileptic Seizures Datasetsmentioning

confidence: 99%

An overview of deep learning techniques for epileptic seizures detection and prediction based on neuroimaging modalities: Methods, challenges, and future works

Shoeibi,

Moridian,

Khodatars

et al. 2021

Preprint

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“…The power spectral density is defined as the Fourier transform (FT) of the signal's autocorrelation function. In this paper, the Welch method was applied along with a Hamming window [19]. The Welch approach splits the times series into overlapping chunks, computing a modified periodogram of each chunk, and the PSD estimates are then averaged.…”

Section: Eeg Feature Extractionmentioning

confidence: 99%

A Comprehensive Machine-Learning-Based Software Pipeline to Classify EEG Signals: A Case Study on PNES vs. Control Subjects

Varone

Gasparini

Ferlazzo

et al. 2020

Sensors

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The diagnosis of psychogenic nonepileptic seizures (PNES) by means of electroencephalography (EEG) is not a trivial task during clinical practice for neurologists. No clear PNES electrophysiological biomarker has yet been found, and the only tool available for diagnosis is video EEG monitoring with recording of a typical episode and clinical history of the subject. In this paper, a data-driven machine learning (ML) pipeline for classifying EEG segments (i.e., epochs) of PNES and healthy controls (CNT) is introduced. This software pipeline consists of a semiautomatic signal processing technique and a supervised ML classifier to aid clinical discriminative diagnosis of PNES by means of an EEG time series. In our ML pipeline, statistical features like the mean, standard deviation, kurtosis, and skewness are extracted in a power spectral density (PSD) map split up in five conventional EEG rhythms (delta, theta, alpha, beta, and the whole band, i.e., 1–32 Hz). Then, the feature vector is fed into three different supervised ML algorithms, namely, the support vector machine (SVM), linear discriminant analysis (LDA), and Bayesian network (BN), to perform EEG segment classification tasks for CNT vs. PNES. The performance of the pipeline algorithm was evaluated on a dataset of 20 EEG signals (10 PNES and 10 CNT) that was recorded in eyes-closed resting condition at the Regional Epilepsy Centre, Great Metropolitan Hospital of Reggio Calabria, University of Catanzaro, Italy. The experimental results showed that PNES vs. CNT discrimination tasks performed via the ML algorithm and validated with random split (RS) achieved an average accuracy of 0.97 ± 0.013 (RS-SVM), 0.99 ± 0.02 (RS-LDA), and 0.82 ± 0.109 (RS-BN). Meanwhile, with leave-one-out (LOO) validation, an average accuracy of 0.98 ± 0.0233 (LOO-SVM), 0.98 ± 0.124 (LOO-LDA), and 0.81 ± 0.109 (LOO-BN) was achieved. Our findings showed that BN was outperformed by SVM and LDA. The promising results of the proposed software pipeline suggest that it may be a valuable tool to support existing clinical diagnosis.

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“…In the original EEG data, due to factors such as errors collected by the device, there may exist data noise and artifacts in the original dataset [15]. Although EEG data is used to record the brain's wave activity, it also records some other weak currents.…”

Section: A Electroencephalographymentioning

confidence: 99%

Empirical Evaluation on the Impact of Class Overlap for EEG-Based Early Epileptic Seizure Detection

Chen

Li³

et al. 2020

IEEE Access

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Important physiological information is hidden in electroencephalography (EEG), which can reflect the human brain's activity. EEG, which is a kind of complicated signal, can be used for epileptic seizure detection and epilepsy diagnosis via machine learning. A large amount of effort, including raw signal preprocessing and data preprocessing for machine learning, is required for constructing high-quality training datasets because the classification performance highly depends on high-quality data. Feature extraction has been widely used in EEG-based early epileptic seizure detection. Due to the complexity of data collection and labeling, some of the training instances are inevitably mislabeled. That means some similar instances have different labels. This is called the issue of class overlap, which leads to a poor class boundary for classification models and makes constructing a high-quality classification model more difficult. However, the previous studies investigating the impact of the class overlap for EEG data is quite limited. Our goal is to investigate the impact of the class overlap on EEG-based early epileptic seizure detection. We propose a special neighborhood cleaning rule (SNCR) to solve the class overlap issue. To alleviate the class overlap issue, we conduct large-scale experiments on two widely-used EEG datasets and compare our proposed SNCR strategy with a state-of-the-art data clean strategy, i.e., the improved k-means clustering cleaning approach (IKMCCA). The experimental results show that the classification model can achieve significantly better performance in terms of AUC, recall, and F1 metrics when using our proposed SNCR strategy. Therefore, for EEG-based early epileptic seizure detection, we recommend researchers to apply the SNCR strategy to mitigate the class overlap issue and use the SNCR strategy to perform data preprocessing in a future related study.

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Literature Review on EEG Preprocessing, Feature Extraction, and Classifications Techniques

Cited by 34 publications

References 0 publications

An overview of deep learning techniques for epileptic seizures detection and prediction based on neuroimaging modalities: Methods, challenges, and future works

An overview of deep learning techniques for epileptic seizures detection and prediction based on neuroimaging modalities: Methods, challenges, and future works

A Comprehensive Machine-Learning-Based Software Pipeline to Classify EEG Signals: A Case Study on PNES vs. Control Subjects

Empirical Evaluation on the Impact of Class Overlap for EEG-Based Early Epileptic Seizure Detection

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