Hand rotate EEG signal feature extraction by second order Daubechies wavelet transform (DWT)

Dyslexia which causes learning deficiencies in reading and writing is due to a neurological disorder where the brain processes information differently. This paper describes the feature extraction of (EEG) signal using Daubechies wavelet transform. The EEG signals were recorded from capable and poor dyslexic children during writing activities of non-words. Brain learning pathway theories for reading and writing were used to localize electrode placement to 8 positions, namely C3, C4, P3, P4, T7, T8, FC5 and FC6. Daubechies provide the wavelet function shape that represent the type of features in an EEG signal well, detecting variations in frequencies that corresponds to activation of areas in relation to activities. Results showed that capable dyslexic subjects exhibit higher beta band power feature of the frontal (FC6) and parietal (P4) right hemisphere if compared to poor dyslexics, where the normal left hemisphere processing center was utilized. This indicates that the brain of dyslexic is compensating its deficiencies of the left brain with activation of areas to the right.

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Classification of Sleep Stages Using Multi-wavelet Time Frequency Entropy and LDA

Methods of Information in Medicine ◽

10.3414/me09-01-0054 ◽

2010 ◽

Vol 49 (03) ◽

pp. 230-237 ◽

Cited By ~ 44

Author(s):

K. Lweesy ◽

N. Khasawneh ◽

M. Fraiwan ◽

H. Wenz ◽

H. Dickhaus ◽

...

Keyword(s):

Feature Extraction ◽

Wavelet Transform ◽

Discriminant Analysis ◽

Sleep Stage ◽

Continuous Wavelet ◽

Eeg Signal ◽

Linear Discriminant ◽

Time Frequency ◽

Mother Wavelets ◽

Sleep Stage Scoring

Summary Background: The process of automatic sleep stage scoring consists of two major parts: feature extraction and classification. Features are normally extracted from the polysomno-graphic recordings, mainly electroencephalograph (EEG) signals. The EEG is considered a non-stationary signal which increases the complexity of the detection of different waves in it. Objectives: This work presents a new technique for automatic sleep stage scoring based on employing continuous wavelet transform (CWT) and linear discriminant analysis (LDA) using different mother wavelets to detect different waves embedded in the EEG signal. Methods: The use of different mother wave-lets increases the ability to detect waves in the EEG signal. The extracted features were formed based on CWT time frequency entropy using three mother wavelets, and the classification was performed using the linear discriminant analysis. Thirty-two data sets from the MIT-BIH database were used to evaluate the performance of the proposed method. Results: Features of a single EEG signal were extracted successfully based on the time frequency entropy using the continuous wavelet transform with three mother wavelets. The proposed method has shown to outperform the classification based on a CWT using a single mother wavelet. The accuracy was found to be 0.84, while the kappa coefficient was 0.78. Conclusions: This work has shown that wavelet time frequency entropy provides a powerful tool for feature extraction for the non-stationary EEG signal; the accuracy of the classification procedure improved when using multiple wavelets compared to the use of single wavelet time frequency entropy.

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P300 Feature Extraction of Visual and Auditory Evoked EEG Signal

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.490-491.1374 ◽

2014 ◽

Vol 490-491 ◽

pp. 1374-1377 ◽

Cited By ~ 1

Author(s):

Xiao Yan Qiao ◽

Jia Hui Peng

Keyword(s):

Feature Extraction ◽

Wavelet Transform ◽

Evoked Potentials ◽

Brain Computer Interface ◽

Auditory Stimulation ◽

Computer Interface ◽

Eeg Signal ◽

Significant Issue ◽

Eeg Signals ◽

Electroencephalogram Eeg

It is a significant issue to accurately and quickly extract brain evoked potentials under strong noise in the research of brain-computer interface technology. Considering the non-stationary and nonlinearity of the electroencephalogram (EEG) signal, the method of wavelet transform is adopted to extract P300 feature from visual, auditory and visual-auditory evoked EEG signal. Firstly, the imperative pretreatment to EEG acquisition signals was performed. Secondly, respectivly obtained approximate and detail coefficients of each layer, by decomposing the pretreated signals for five layers using wavelet transform. Finally, the approximate coefficients of the fifth layer were reconstructed to extract P300 feature. The results have shown that the method can effectively extract the P300 feature under the different visual-auditory stimulation modes and lay a foundation for processing visual-auditory evoked EEG signals under the different mental tasks.

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Feature extraction of P300s in EEG signal with discrete wavelet transform and fisher criterion

2015 8th International Conference on Biomedical Engineering and Informatics (BMEI) ◽

10.1109/bmei.2015.7401500 ◽

2015 ◽

Cited By ~ 6

Author(s):

Shunying Guo ◽

Suyun Lin ◽

Zhihua Huang

Keyword(s):

Feature Extraction ◽

Wavelet Transform ◽

Discrete Wavelet Transform ◽

Discrete Wavelet ◽

Eeg Signal ◽

Fisher Criterion

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Penentuan Notasi Gamelan Rindik Menggunakan Metode Transformasi Wavelet

Majalah Ilmiah Teknologi Elektro ◽

10.24843/mite.2018.v17i03.p03 ◽

2018 ◽

Vol 17 (3) ◽

pp. 319

Author(s):

I Gusti Made Meri Utama Yasa ◽

Linawati Linawati ◽

N Paramaita

Keyword(s):

Neural Network ◽

Feature Extraction ◽

Wavelet Transform ◽

Probabilistic Neural Network ◽

Wavelet Function ◽

Discrete Wavelet ◽

Daubechies Wavelet ◽

Data Process ◽

Average Accuracy ◽

Accuracy Level

Abstract—This paper present about recognition of gamelan rindik pattern using wavelet transform. Wavelet transform is used to find the special characteristic of gamelan rindik, which had previously been recorded and stored in computer with format *.wav. The data was subsequently used as training and tested data, Probabilistic Neural Network (PNN) was used to recognize gamelan rindik pattern using. The training and tasted data process used four different rindics, consisting 0f 240 gamelan rindik data. Discrete Wavelet Transform (DWT) was used as the method of feature extraction, with Symlet, Haar, and Daubechies Wavelet function. Those three functions of the wavelet shows the average accuracy level for Symlet 94.58%, Haar 93.33%, and wavelet Daubechies 94.58%.

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