Feature extraction of electroencephalogram (EEG) signal - A review

Azlan, Wan Amirah W; Low, Yin Fen

doi:10.1109/iecbes.2014.7047620

Cited by 25 publications

(17 citation statements)

References 31 publications

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“…Besides feature combination/extraction, PCA has also been used to handle missing features [66]. For more applications of PCA and other feature combination/extraction techniques, the interested readers are referred to some review papers on the subject [67][68][69][70].…”

Section: Feature Combination or Extractionmentioning

confidence: 99%

Application of Systems Engineering Principles and Techniques in Biological Big Data Analytics: A Review

Wang

2020

Processes

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In the past few decades, we have witnessed tremendous advancements in biology, life sciences and healthcare. These advancements are due in no small part to the big data made available by various high-throughput technologies, the ever-advancing computing power, and the algorithmic advancements in machine learning. Specifically, big data analytics such as statistical and machine learning has become an essential tool in these rapidly developing fields. As a result, the subject has drawn increased attention and many review papers have been published in just the past few years on the subject. Different from all existing reviews, this work focuses on the application of systems, engineering principles and techniques in addressing some of the common challenges in big data analytics for biological, biomedical and healthcare applications. Specifically, this review focuses on the following three key areas in biological big data analytics where systems engineering principles and techniques have been playing important roles: the principle of parsimony in addressing overfitting, the dynamic analysis of biological data, and the role of domain knowledge in biological data analytics.

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Section: Feature Combination or Extractionmentioning

confidence: 99%

Application of Systems Engineering Principles and Techniques in Biological Big Data Analytics: A Review

Wang

2020

Processes

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“…Popular EEG signal processing tools including EEGLAB provide functionalities to perform ICA (Delorme and Makeig, 2004). Even though multiple ICA algorithms exist, FastICA, Infomax and JADE are being widely used (Azlan and Low, 2014). Several studies report second-order blind identification (SOBI), an ICA algorithm, as a successful technique to remove all types of artefacts from the EEG signal (Urigüen and Garcia-Zapirain, 2015).…”

Section: Independent Component Analysismentioning

confidence: 99%

“…Most of these algorithms split the original signal into multiple components and they can also be used for noise filtering. These techniques only pre-process the signal to facilitate feature extraction but do not extract any features (Lakshmi et al, 2014;Azlan and Low, 2014). Table 3 summarizes different techniques used for feature extraction in the related studies.…”

Section: Feature Extraction Techniquesmentioning

confidence: 99%

EEG-based Processing and Classification Methodologies for Autism Spectrum Disorder: A Review

Brihadiswaran¹,

Haputhanthri²,

Gunathilaka³

et al. 2019

Journal of Computer Science

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Autism Spectrum Disorder is a lifelong neurodevelopmental condition which affects social interaction, communication and behaviour of an individual. The symptoms are diverse with different levels of severity. Recent studies have revealed that early intervention is highly effective for improving the condition. However, current ASD diagnostic criteria are subjective which makes early diagnosis challenging, due to the unavailability of well-defined medical tests to diagnose ASD. Over the years, several objective measures utilizing abnormalities found in EEG signals and statistical analysis have been proposed. Machine learning based approaches provide more flexibility and have produced better results in ASD classification. This paper presents a survey of major EEG-based ASD classification approaches from 2010 to 2018, which adopt machine learning. The methodology is divided into four phases: EEG data collection, pre-processing, feature extraction and classification. This study explores different techniques and tools used for pre-processing, feature extraction and feature selection techniques, classification models and measures for evaluating the model. We analyze the strengths and weaknesses of the techniques and tools. Further, this study summarizes the ASD classification approaches and discusses the existing challenges, limitations and future directions.

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“…The work presented in [6] compares different features that can be extracted for EEG signals in terms of analysis and methods. One of the approach to extract features is called the Hilbert-Huang transform.…”

Section: Related State-of-the-art Techniquesmentioning

confidence: 99%

Predicting the Standard and Deviant Patterns in EEG Signals Based on Deep Learning Model

Sharif

Al-Bayatti

Alfakeeh

2019

2019 International Conference on Computing, Electronics &Amp; Communications Engineering (iCCECE)

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In the recent years, there has been a significant growth in the area of brain computer interference. The main aim of such area is to read the brain activities, formulate a specific/desired output and power a specific approach using such output. Electroencephalography (EEG) may provide an insight into the analysis procedure of the human behavior and the level of the attention. Using the deep learning based neural network has a great success in different applications recently, such as making a decision, classifying a pattern and predicting an outcome by learning from a set of data and build the right weight matrices to represent the prediction outcome or the learning patterns. This research work proposes a novel model based on long short-term memory network to predict the standard and the deviant cases within EEG data sets. The EEG signals are acquired utilizing all the 128 electrodes that represent the 128 channels from infants aged between 5 and 7 months. Statistical approaches, principal component analysis (PCA) and autoregressive (AR) power spectral density estimate have been employed to extract the features from the EEG data sets. The proposed deep learning based model has shown great robustness dealing with different types of features extracted from the processed data sets. Very promising results have been achieved in predicting the standard and deviant cases. The standard case was presented with frequent, repetitive stimulus and the deviant case was presented with infrequent sounds.

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Feature extraction of electroencephalogram (EEG) signal - A review

Cited by 25 publications

References 31 publications

Application of Systems Engineering Principles and Techniques in Biological Big Data Analytics: A Review

Application of Systems Engineering Principles and Techniques in Biological Big Data Analytics: A Review

EEG-based Processing and Classification Methodologies for Autism Spectrum Disorder: A Review

Predicting the Standard and Deviant Patterns in EEG Signals Based on Deep Learning Model

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