Feature extraction and classification for EEG signals using wavelet transform and machine learning techniques

Amin, Hafeez Ullah; Malik, Aamir Saeed; Ahmad, Rana Fayyaz; Badruddin, Nasreen; Kamel, Nidal; Hussain, Muhammad; Chooi, Weng-Tink

doi:10.1007/s13246-015-0333-x

Cited by 269 publications

(123 citation statements)

References 47 publications

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“…A direct comparison of the previous studies that using EEG signals is hard due to the variety of EEG datasets, wavelet types, decomposition levels and also the variety of the statistical features used in the classification process (15). Previously, many researchers used the same EEG data which included five sets (named as A-E) described by Andrzejak, et al (38).…”

Section: Discussionmentioning

confidence: 99%

“…Due to its high success the Daubechies wavelet order in 4 (Db4) was used to construct the feature vectors (13,15). Since the EEG signals do not have any useful frequency components above 30 Hz, the number of levels was chosen to be 6.…”

Section: Methodsmentioning

confidence: 99%

“…In many study, extracted signals are derived at the time of seizure and at normal periods. So, classification performance of these studies were quite high (9,(11)(12)(13)(14)(15)(16)(17)(18)(19)(20)(21). In fact, visual assessment might be sufficient at that time.…”

Section: Introductionmentioning

confidence: 91%

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The Comparison of Different Dimension Reduction and Classification Methods in Electroencephalogram Signals

Öztürk¹,

Türe²,

Kıylıoğlu³

et al. 2018

meandros

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Objective: Electroencephalogram (EEG) signals have been broadly utilized for the diagnosis of epilepsy. Expert physicians must monitor long-term EEG signals that is sometimes difficult and time consuming process for epilepsy diagnosis. In this study, classification performances of Support Vector Machine (SVM) and Linear Discriminant Analysis (LDA), which are widely used in computer supported epilepsy diagnosis, were compared by using wavelet-based features of extracted from EEG signals which were derived in either normal or inter-ictal periods. In addition, Principal Component Analysis (PCA) and Independent Component Analysis (ICA) were used to determine the effects of dimension reduction on classification success. Materials and Methods:The EEG data were sampled from the EEG laboratory of the Department of Neurology and Clinical Neurophysiology in Adnan Menderes University. Study was approved by local ethics committee with protocol number 2016/873. Ten patients with epilepsy (male -female, age range) and 10 normal (male -female, age range) were the study group. EEG signals of patients with epilepsy were contains only seizure free-epochs. EEG signals were first decomposed into frequency sub-bands by using Discrete Wavelet Transform (DWT) and then some statistical features were calculated from those to classify it's as normal or epileptic. Results: In classification of the EEG signals, it's as normal or epileptic, we achieved %88.9 accuracy rate using SVM with Radial Basis Function (RBF) kernel without dimension reduction. Conclusion: Results showed that, SVM was a powerful tool in classifying EEG signals if it's normal or epileptic.Amaç: Bu çalışmada, epileptik ve epileptik olmayan EEG (Elektroensefelografi) sinyallerinden elde edilen özniteliklerin boyutlarının Temel Bileşenler Analizi (TBA) ve Bağımsız Bileşenler Analizi (BBA) yöntemleri ile indirgenmesinin sınıflandırma başarısı üzerine etkilerinin belirlenmesi ve Doğrusal Ayırma Analizi (DAA) ile Destek Vektör Makinesi (DVM) yöntemlerinin sınıflandırma performanslarının karşılaştırılması amaçlandı. Gereç ve Yöntem: Çalışmaya 10 kontrol ve uzman hekim tarafından epilepsi tanısı konmuş 10 hasta olmak üzere toplam 20 kişi dahil edildi. Epilepsi tanısı konmuş hastalardan alınan EEG kayıtları nöbet geçirmedikleri sırada alınan kayıtlardı. Epileptik

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

The Comparison of Different Dimension Reduction and Classification Methods in Electroencephalogram Signals

Öztürk¹,

Türe²,

Kıylıoğlu³

et al. 2018

meandros

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show abstract

“…For improvement in the accuracy of epilepsy detection researchers have also studied the application of machine learning and optimization algorithms. Amin et al [14] compared the classification accuracy rate of SVM, MLP, k-NN, and Naïve Bayes (NB) classifiers for epilepsy detection. Nunes et al [15] used the optimum path forest classifier for seizure identification.…”

Section: Feature Classification Methodsmentioning

confidence: 99%

A comparative study on feature extraction and classification of mind waves for brain computerinterface (BCI)

Manjula¹,

Anandaraju²

2018

IJET

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Brain Computer Interfacing (BCI) is a methodology which imparts a path for communication from external world using brain signals through computer. BCI identifies the specific patterns in a person's changing brain activity to initiate control which relates to the person's intention. The BCI system paraphrases these signal patterns into meaningful control command. In evolving BCI system, numerous signal processing algorithms are proposed. Non-invasive Electroencephalogram (EEG) signals or mind waves are used to extract the distinguished features and further they are classified choosing an appropriate classifier. A study on different feature extraction & Classification algorithms is used in EEG-based BCI exploration and to identify their distinct properties. This paper proposes different methodologies of feature extraction and feature Classification. It also addresses the methods and technology adapted in every phase of the EEG signal processing. This comparative survey also helps in selecting suitable algorithm for the development and accomplishment of further classification of signals.

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“…It's convenient to observe the local characteristics of the signals, and get their temporal and frequency information at the same time. It is suitable for detecting the abnormal phenomena of inclusion in normal signal, and it can effectively distinguish the mutation part and noise [9].…”

Section: Wavelet Transform (Wt)mentioning

confidence: 99%

Analysis of Feature Extraction Algorithms Used in Brain-Computer Interfaces

Tan¹,

Zhao²,

Sun³

et al. 2017

dtetr

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Abstract. A brain-computer interface (BCI) is a system for communication between humans or animals and computers, which sends messages or commands from brain activities to the external devices without peripheral nerves and muscles activities. Feature extraction is crucial in a BCI system, for it determines whether the user's intent can be interpreted as an accurate command. We examined the performance of the representative algorithms including Fast Fourier transform (FFT), Wavelet transform (WT) and Independent component analysis (ICA). Experimental results show that these algorithms are effective to identify the target characteristics. Improvements of these methods or their integrations can contribute to enhance the efficiency of the BCI systems.

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Feature extraction and classification for EEG signals using wavelet transform and machine learning techniques

Cited by 269 publications

References 47 publications

The Comparison of Different Dimension Reduction and Classification Methods in Electroencephalogram Signals

The Comparison of Different Dimension Reduction and Classification Methods in Electroencephalogram Signals

A comparative study on feature extraction and classification of mind waves for brain computerinterface (BCI)

Analysis of Feature Extraction Algorithms Used in Brain-Computer Interfaces

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