Feature extraction for EEG-based brain–computer interfaces by wavelet packet best basis decomposition

Yang, Banghua; Yan, Guozheng; Yan, Rongguo; Wu, Ting

doi:10.1088/1741-2560/3/4/001

Cited by 25 publications

(15 citation statements)

References 20 publications

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“…Therefore, WPT can provide a better multi-resolution and time-frequency analysis for non-stationary data. Many literatures have demonstrated that WPT is one of the most promising methods for the feature extraction from EEG signals (Adeli et al 2003;Ocak et al 2009;Yang et al 2006). For n-level decomposition, WPT generates a full decomposition tree, as depicted in Fig.…”

Section: Wavelet Packet Entropy Methodsmentioning

confidence: 99%

Investigation of EEG abnormalities in the early stage of Parkinson’s disease

et al. 2013

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The objective of the present study was to investigate brain activity abnormalities in the early stage of Parkinson's disease (PD). To achieve this goal, eyes-closed resting state electroencephalography (EEG) signals were recorded from 15 early-stage PD patients and 15 agematched healthy controls. The AR Burg method and the wavelet packet entropy (WPE) method were used to characterize EEG signals in different frequency bands between the groups, respectively. In the case of the AR Burg method, an increase of relative powers in the d-and h-band, and a decrease of relative powers in the a-and b-band were observed for patients compared with controls. For the WPE method, EEG signals from patients showed significant higher entropy over the global frequency domain. Furthermore, WPE in the c-band of patients was higher than that of controls, while WPE in the d-, h-, a-and b-band were all lower. All of these changes in EEG dynamics may represent early signs of cortical dysfunction, which have potential use as biomarkers of PD in the early stage. Our findings may be further used for early intervention and early diagnosis of PD.

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Section: Wavelet Packet Entropy Methodsmentioning

confidence: 99%

Investigation of EEG abnormalities in the early stage of Parkinson’s disease

et al. 2013

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“…Given a non-stationary signal applying these transforms emphasizing the low pass and high pass filtering operations yields the sub-band tree decomposition to some desired level. It is well-known fact that the WT decomposes only approximation coefficients of the signal and successive detail are never considered and taking information located at higher frequency will be lost on the other hand WPT decomposes successive details and approximation, taking good and adjustable frequency resolutions at high frequencies (Adeli et al 2003;Yang et al 2006;Ocak 2009). It retains the key information located in higher frequency than WT for certain applications.…”

Section: Wavelet Packet Decompositionmentioning

confidence: 99%

Classification of epileptic seizures using wavelet packet log energy and norm entropies with recurrent Elman neural network classifier

Raghu¹,

Kumar

2016

Cogn Neurodyn

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Electroencephalogram shortly termed as EEG is considered as the fundamental segment for the assessment of the neural activities in the brain. In cognitive neuroscience domain, EEG-based assessment method is found to be superior due to its non-invasive ability to detect deep brain structure while exhibiting superior spatial resolutions. Especially for studying the neurodynamic behavior of epileptic seizures, EEG recordings reflect the neuronal activity of the brain and thus provide required clinical diagnostic information for the neurologist. This specific proposed study makes use of wavelet packet based log and norm entropies with a recurrent Elman neural network (REN) for the automated detection of epileptic seizures. Three conditions, normal, pre-ictal and epileptic EEG recordings were considered for the proposed study. An adaptive Weiner filter was initially applied to remove the power line noise of 50 Hz from raw EEG recordings. Raw EEGs were segmented into 1 s patterns to ensure stationarity of the signal. Then wavelet packet using Haar wavelet with a five level decomposition was introduced and two entropies, log and norm were estimated and were applied to REN classifier to perform binary classification. The nonlinear Wilcoxon statistical test was applied to observe the variation in the features under these conditions. The effect of log energy entropy (without wavelets) was also studied. It was found from the simulation results that the wavelet packet log entropy with REN classifier yielded a classification accuracy of 99.70 % for normal-pre-ictal, 99.70 % for normal-epileptic and 99.85 % for pre-ictalepileptic.

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“…It should be noted that as we are handling several channels, the transform within a segment is calculated for each of them, and the cost function is applied taking all the information into account. Although in the BCI domain this approach has focused mainly on LCT [25], other techniques may be applied such as wavelet-packets [27]. We are not interested in the latter as it does not allow the exploration of the signal with variable segment lengths.…”

Section: Local Discriminant Basesmentioning

confidence: 99%

Extracting optimal tempo-spatial features using local discriminant bases and common spatial patterns for brain computer interfacing

Asensio-Cubero

Gan

Palaniappan

2013

Biomedical Signal Processing and Control

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(2013) Extracting optimal tempo-spatial features using local discriminant bases and common spatial patterns for brain computer interfacing. Biomedical Signal Processing and Control, 8 (6) AbstractBrain computer interfaces (BCI) provide a new approach to human computer communication, where the control is realised via performing mental tasks such as motor imagery (MI). In this study, we investigate a novel method to automatically segment electroencephalographic (EEG) data within a trial and extract features accordingly in order to improve the performance of classifying MI data. A new local discriminant bases (LDB) algorithm using common spatial patterns (CSP) projection as transform function is proposed for automatic trial segmentation. CSP is also used for feature extraction following trial segmentation. This new technique also allows to obtain a more accurate picture of the most relevant temporal-spatial points in the EEG during the MI.The results are compared with other standard temporal segmentation techniques such as sliding window and LDB based on the local cosine transform (LCT).

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Feature extraction for EEG-based brain–computer interfaces by wavelet packet best basis decomposition

Cited by 25 publications

References 20 publications

Investigation of EEG abnormalities in the early stage of Parkinson’s disease

Investigation of EEG abnormalities in the early stage of Parkinson’s disease

Classification of epileptic seizures using wavelet packet log energy and norm entropies with recurrent Elman neural network classifier

Extracting optimal tempo-spatial features using local discriminant bases and common spatial patterns for brain computer interfacing

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