An evaluation of the effects of wavelet coefficient quantisation in transform based EEG compression

Higgins, Garry; McGinley, Brian; Jones, Edward; Glavin, Martin

doi:10.1016/j.compbiomed.2013.02.011

Cited by 29 publications

(12 citation statements)

References 36 publications

(57 reference statements)

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“…Out of these techniques, WT showed very good performance as compared to other methods due to its compliance with the EEG brain signals which having non-stationary behavior. The WT features which are considered for analysis are statistical features (standard deviations, mean and median) (Yazdani et al, 2009; Garry et al, 2013), wavelet entropy (Rosso et al, 2001) and wavelet coefficients (Orhan et al, 2011). These features have been used for analysis in EEG and clinical applications.…”

Section: Literature Reviewmentioning

confidence: 99%

Single Trial EEG Patterns for the Prediction of Individual Differences in Fluid Intelligence

Qazi

Hussain

Aboalsamh

et al. 2017

Front. Hum. Neurosci.

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Assessing a person's intelligence level is required in many situations, such as career counseling and clinical applications. EEG evoked potentials in oddball task and fluid intelligence score are correlated because both reflect the cognitive processing and attention. A system for prediction of an individual's fluid intelligence level using single trial Electroencephalography (EEG) signals has been proposed. For this purpose, we employed 2D and 3D contents and 34 subjects each for 2D and 3D, which were divided into low-ability (LA) and high-ability (HA) groups using Raven's Advanced Progressive Matrices (RAPM) test. Using visual oddball cognitive task, neural activity of each group was measured and analyzed over three midline electrodes (Fz, Cz, and Pz). To predict whether an individual belongs to LA or HA group, features were extracted using wavelet decomposition of EEG signals recorded in visual oddball task and support vector machine (SVM) was used as a classifier. Two different types of Haar wavelet transform based features have been extracted from the band (0.3 to 30 Hz) of EEG signals. Statistical wavelet features and wavelet coefficient features from the frequency bands 0.0–1.875 Hz (delta low) and 1.875–3.75 Hz (delta high), resulted in the 100 and 98% prediction accuracies, respectively, both for 2D and 3D contents. The analysis of these frequency bands showed clear difference between LA and HA groups. Further, discriminative values of the features have been validated using statistical significance tests and inter-class and intra-class variation analysis. Also, statistical test showed that there was no effect of 2D and 3D content on the assessment of fluid intelligence level. Comparisons with state-of-the-art techniques showed the superiority of the proposed system.

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Section: Literature Reviewmentioning

confidence: 99%

Single Trial EEG Patterns for the Prediction of Individual Differences in Fluid Intelligence

Qazi

Hussain

Aboalsamh

et al. 2017

Front. Hum. Neurosci.

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“…However, WT-based analysis is highly effective, because it deals better with the non-stationary behavior of EEG signals than other methods. Wavelet-based features, including wavelet entropy [5], wavelet coefficients [2], and wavelet statistical features (mean, median, and standard deviations) have been reported for normal EEG analysis as well as in clinical applications [6,7]. Details on the performance of time domain, frequency domain and wavelet-based techniques employed in EEG classification for cognitive tasks and/or BCI applications are provided in the related work section and the classification accuracy of these techniques are provided in the discussion section.…”

Section: Introductionmentioning

confidence: 99%

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

Amin

Malik

Ahmad

et al. 2015

Australas Phys Eng Sci Med

262

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This paper describes a discrete wavelet transform-based feature extraction scheme for the classification of EEG signals. In this scheme, the discrete wavelet transform is applied on EEG signals and the relative wavelet energy is calculated in terms of detailed coefficients and the approximation coefficients of the last decomposition level. The extracted relative wavelet energy features are passed to classifiers for the classification purpose. The EEG dataset employed for the validation of the proposed method consisted of two classes: (1) the EEG signals recorded during the complex cognitive task--Raven's advance progressive metric test and (2) the EEG signals recorded in rest condition--eyes open. The performance of four different classifiers was evaluated with four performance measures, i.e., accuracy, sensitivity, specificity and precision values. The accuracy was achieved above 98 % by the support vector machine, multi-layer perceptron and the K-nearest neighbor classifiers with approximation (A4) and detailed coefficients (D4), which represent the frequency range of 0.53-3.06 and 3.06-6.12 Hz, respectively. The findings of this study demonstrated that the proposed feature extraction approach has the potential to classify the EEG signals recorded during a complex cognitive task by achieving a high accuracy rate.

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“…WT-based analysis is highly effective for non-stationary EEG signals compared to the short-time Fourier transformation (STFT). Moreover, wavelet-based features, including wavelet entropy (Rosso et al, 2001 ), wavelet coefficients (Orhan et al, 2011 ), and wavelet statistical features (mean, median, and standard deviations) have been reported for the evaluation of normal EEG patterns and for clinical applications (Yazdani et al, 2009 ; Garry et al, 2013 ). However, significant gaps in the literature exist regarding cognitive load studies and approaches to pattern recognition.…”

Section: Introductionmentioning

confidence: 99%

Classification of EEG Signals Based on Pattern Recognition Approach

Amin

Mumtaz

Subhani

et al. 2017

Front. Comput. Neurosci.

158

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Feature extraction is an important step in the process of electroencephalogram (EEG) signal classification. The authors propose a “pattern recognition” approach that discriminates EEG signals recorded during different cognitive conditions. Wavelet based feature extraction such as, multi-resolution decompositions into detailed and approximate coefficients as well as relative wavelet energy were computed. Extracted relative wavelet energy features were normalized to zero mean and unit variance and then optimized using Fisher's discriminant ratio (FDR) and principal component analysis (PCA). A high density EEG dataset validated the proposed method (128-channels) by identifying two classifications: (1) EEG signals recorded during complex cognitive tasks using Raven's Advance Progressive Metric (RAPM) test; (2) EEG signals recorded during a baseline task (eyes open). Classifiers such as, K-nearest neighbors (KNN), Support Vector Machine (SVM), Multi-layer Perceptron (MLP), and Naïve Bayes (NB) were then employed. Outcomes yielded 99.11% accuracy via SVM classifier for coefficient approximations (A5) of low frequencies ranging from 0 to 3.90 Hz. Accuracy rates for detailed coefficients were 98.57 and 98.39% for SVM and KNN, respectively; and for detailed coefficients (D5) deriving from the sub-band range (3.90–7.81 Hz). Accuracy rates for MLP and NB classifiers were comparable at 97.11–89.63% and 91.60–81.07% for A5 and D5 coefficients, respectively. In addition, the proposed approach was also applied on public dataset for classification of two cognitive tasks and achieved comparable classification results, i.e., 93.33% accuracy with KNN. The proposed scheme yielded significantly higher classification performances using machine learning classifiers compared to extant quantitative feature extraction. These results suggest the proposed feature extraction method reliably classifies EEG signals recorded during cognitive tasks with a higher degree of accuracy.

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An evaluation of the effects of wavelet coefficient quantisation in transform based EEG compression

Cited by 29 publications

References 36 publications

Single Trial EEG Patterns for the Prediction of Individual Differences in Fluid Intelligence

Single Trial EEG Patterns for the Prediction of Individual Differences in Fluid Intelligence

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

Classification of EEG Signals Based on Pattern Recognition Approach

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