Wavelet Transform Enhancement for Drowsiness Classification in EEG Records Using Energy Coefficient Distribution and Neural Network

Boonnak, Naiyana; Kamonsantiroj, Suwatchai; Pipanmaekaporn, Luepol

doi:10.7763/ijmlc.2015.v5.522

Cited by 29 publications

(14 citation statements)

References 14 publications

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“…Each result is considered as a sub-space; however, only the low-pass filter results will be regarded as the input of the next step and then get another layer of low-pass and high-pass filtered results [23]. Different from the WT, WPD will transform the results of low-pass and high-pass filters further; therefore, the results of wavelet packet transform can be regarded as a complete binary tree whose root node is the original signal, and the sub-nodes of the next layer are the results of WT [20,24].…”

Section: Wavelet Packet Decompositionmentioning

confidence: 99%

Adaptive Feature Extraction of Motor Imagery EEG with Optimal Wavelet Packets and SE-Isomap

Zhu

Liu

et al. 2017

Applied Sciences

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Abstract:Motor imagery EEG (MI-EEG), which reflects one's active movement intention, has attracted increasing attention in rehabilitation therapy, and accurate and fast feature extraction is the key problem to successful applications. Based on wavelet packet decomposition (WPD) and SE-isomap, an adaptive feature extraction method is proposed in this paper. The MI-EEG is preprocessed to determine a more effective time interval through average power spectrum analysis. WPD is then applied to the selected segment of MI-EEG, and the subject-based optimal wavelet packets (OWPs) with top mean variance difference are obtained autonomously. The OWP coefficients are further used to calculate the time-frequency features statistically and acquire the nonlinear manifold structure features, as well as the explicit nonlinear mapping, through SE-isomap. The hybrid features are obtained in a serial fusion way and evaluated by a k-nearest neighbor (KNN) classifier. The extensive experiments are conducted on a publicly available dataset, and the experiment results of 10-fold cross-validation show that the proposed method yields relatively higher classification accuracy and computation efficiency simultaneously compared with the commonly-used linear and nonlinear approaches.

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Section: Wavelet Packet Decompositionmentioning

confidence: 99%

Adaptive Feature Extraction of Motor Imagery EEG with Optimal Wavelet Packets and SE-Isomap

Zhu

Liu

et al. 2017

Applied Sciences

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“…Three classifiers are used to evaluate the accuracy rate and found overall accuracy 70%. Another work classifies drowsiness with respect to alertness extracting energy coefficients from WT to train ANN and found 90.27 % accuracy [21]. Authors in [22] classify participant"s 3 states (alert, drowsy and sleep) DWT and ANN classifier which results in satisfactory accuracy rate.…”

Section: Introductionmentioning

confidence: 99%

“…Sleeping state begins with the activation of neurons and brain inhibition. The transformation of awaking or alertness state to unconscious or drowsiness state is described by certain rhythmic changes [8][9][10]: (i) decreased the beta rhythmic (13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30) activity, (ii) increase in alpha rhythm activity (8)(9)(10)(11)(12)(13) but best observable while resting by eyes closed; and (iii) increased theta rhythm activity (4-8 Hz) if consequently alpha rhythm decreased.…”

Section: Introductionmentioning

confidence: 99%

“…Different types of research works regarding the aforementioned problems have been accomplished. In [16][17][18][19][20][21][22][23], researchers proposed a classification method of different states (attentive, inattentive alertness, sleepiness, drowsiness). Authors in [16] propose a method to make the learning process of students more effective by distinguishing attentive and inattentive state.…”

Section: Introductionmentioning

confidence: 99%

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EEG based Brain Alertness Monitoring by Statistical and Artificial Neural Network Approach

Rahman¹,

Rashid²,

Khanam³

et al. 2019

ijacsa

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Since several work requires continuous alertness like efficient driving, learning, etc. efficient measurement of the alertness states through neural activity is a crucial challenge for the researchers. This work reports a practical method to investigate the alertness state from electroencephalography (EEG) of the human brain. Here, we have proposed a novel idea to monitor the brain alertness from EEG signal that can discriminate the alertness state comparing resting state with a simple statistical threshold. We have investigated two different types of mental tasks: alphabet counting & virtual driving to monitor their alertness level. The EEG signals are acquired from several participants regarding alphabet counting and virtual motor driving tasks. A 9-channel wireless EEG system has been used to acquire their EEG signals from frontal, central, and parietal lobe of the brain. With suitable preprocessing, signal dimensions are reduced by principal component analysis and the features of the signals are extracted by the discrete wavelet transformation method. Using the features, alertness states are classified using the artificial neural network. Additionally, the relative power of responsible frequency band to alertness is analyzed with statistical inference. We have found that the beta relative power increases at a significant level due to alertness which is good enough to differentiate the alertness state from the control state. It is also found that the increment of beta relative power for virtual driving is much greater than the alphabet counting mental alertness. We hope that this work will be very helpful to monitor constant alertness for efficient driving and learning.

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“…Thus, the analysis of EEG signals can benefit from WT. Despite this, recent studies do not support the effective use of wavelet features for the discrimination of EEG signals because of the redundant and irrelevant information contained in wavelet coefficients [3]. To this day, these characteristics remain a problem for the extraction of useful features from EEG signals for classification.…”

Section: Introductionmentioning

confidence: 99%

Applying a Locally Linear Embedding Algorithm for Feature Extraction and Visualization of MI-EEG

Luo

Yang

et al. 2016

Journal of Sensors

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Robotic-assisted rehabilitation system based on Brain-Computer Interface (BCI) is an applicable solution for stroke survivors with a poorly functioning hemiparetic arm. The key technique for rehabilitation system is the feature extraction of Motor Imagery Electroencephalography (MI-EEG), which is a nonlinear time-varying and nonstationary signal with remarkable time-frequency characteristic. Though a few people have made efforts to explore the nonlinear nature from the perspective of manifold learning, they hardly take into full account both time-frequency feature and nonlinear nature. In this paper, a novel feature extraction method is proposed based on the Locally Linear Embedding (LLE) algorithm and DWT. The multiscale multiresolution analysis is implemented for MI-EEG by DWT. LLE is applied to the approximation components to extract the nonlinear features, and the statistics of the detail components are calculated to obtain the time-frequency features. Then, the two features are combined serially. A backpropagation neural network is optimized by genetic algorithm and employed as a classifier to evaluate the effectiveness of the proposed method. The experiment results of 10-fold cross validation on a public BCI Competition dataset show that the nonlinear features visually display obvious clustering distribution and the fused features improve the classification accuracy and stability. This paper successfully achieves application of manifold learning in BCI.

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Wavelet Transform Enhancement for Drowsiness Classification in EEG Records Using Energy Coefficient Distribution and Neural Network

Cited by 29 publications

References 14 publications

Adaptive Feature Extraction of Motor Imagery EEG with Optimal Wavelet Packets and SE-Isomap

Adaptive Feature Extraction of Motor Imagery EEG with Optimal Wavelet Packets and SE-Isomap

EEG based Brain Alertness Monitoring by Statistical and Artificial Neural Network Approach

Applying a Locally Linear Embedding Algorithm for Feature Extraction and Visualization of MI-EEG

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