Selection of Symlets wavelet function order for EEG signal feature extraction in children with dyslexia

Mahmoodin, Z.; Jalalludin, N. S.; Mansor, W.; Lee, Khuan Y.; Mohamad, N. B.

doi:10.1109/issbes.2015.7435879

Cited by 7 publications

(4 citation statements)

References 14 publications

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“…The CWT is a mathematical transformation that gives the signal a complete two-dimensional representation of time and scaling using a wavelet function that receives a continuously changing scale value [ 20 ]. We have used the Symlet (e.g., [ 21 , 22 ]) wavelet filter with a scaling factor ranging from 1 to 32. In addition, to obtain optimal results, we examined filters of the order 2, 4, 6, 8, and 10, and the best one according to the cross-entropy (CE) loss function (see Equations (3) and (4)) turned out to be a sixth-order Symlet filter (see Appendix A ).…”

Section: Methodsmentioning

confidence: 99%

Level-K Classification from EEG Signals Using Transfer Learning

Mizrahi

Zuckerman

Laufer

2021

Sensors

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Tacit coordination games are games in which communication between the players is not allowed or not possible. In these games, the more salient solutions, that are often perceived as more prominent, are referred to as focal points. The level-k model states that players’ decisions in tacit coordination games are a consequence of applying different decision rules at different depths of reasoning (level-k). A player at Lk=0 will randomly pick a solution, whereas a Lk≥1 player will apply their strategy based on their beliefs regarding the actions of the other players. The goal of this study was to examine, for the first time, the neural correlates of different reasoning levels in tacit coordination games. To that end, we have designed a combined behavioral-electrophysiological study with 3 different conditions, each resembling a different depth reasoning state: (1) resting state, (2) picking, and (3) coordination. By utilizing transfer learning and deep learning, we were able to achieve a precision of almost 100% (99.49%) for the resting-state condition, while for the picking and coordination conditions, the precision was 69.53% and 72.44%, respectively. The application of these findings and related future research options are discussed.

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Section: Methodsmentioning

confidence: 99%

Level-K Classification from EEG Signals Using Transfer Learning

Mizrahi

Zuckerman

Laufer

2021

Sensors

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“…Many researchers use these mother wavelet function for extracting the subbands from EEG signal. Some researchers specifies sym9 is best among sym1 to sym20 [95], some other researcher specifies that sym7 is best as compared to the sym of order 5,7,8,and 9 for extracting the features from EEG to identify the dyslexia [106]. In our observation up to "sym35" DWT perform decomposition in less time.…”

Section: Symlet Waveletsmentioning

confidence: 87%

A Brief Review on EEG Signal Pre-processing Techniques for Real-Time Brain-Computer Interface Applications

Balam¹,

Pławiak²,

Prakash³

2021

Preprint

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<div>Electro Encephalo Gram (EEG) is a monitoring method used in biomedical and computer science to understand brain activity. Therefore, the analysis and classification of these signals play a prominent role in estimating a person’s behavior to certain events. Manually analyzing these signals is very tedious and time-consuming, so an automated scientific tool is required to analyze the brain signals. In this work, the authors are explored various pre-processing segmentation techniques that are helpful in an automatic machine and deep learning-based classification methods available for EEG signal processing. Most of the machine and deep learning methods are followed pre-processing as a common step in classification. Extraction of the basic sub-band components from EEG signals such as delta (δ), theta (θ), alpha (α), beta (β), and gamma (γ) is very important in the pre-processing stage. These sub bands of EEG signal have extraordinary evidence related to multiple neurophysiological processes, which are useful for further prediction & diagnosis of diseases and other emotion-based applications. This review paper elaborates various elementary ideas of extracting EEG sub-bands and the role of EEG in Brain-Computer Interface (BCI) in the classification. <b> (Submitted To IEEE reviews in Biomedical Engineering)</b></div>

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“…The support width of the wavelet is 2N − 1, the vanishing moment is N , and it also has good regularity. Compared with dbN wavelet basis function, this wavelet basis function is consistent with dbN wavelet basis function in terms of continuity, compact support width, and filter length, but symN wavelet has better symmetry, and it can reduce the phase distortion during signal analysis and reconstruction to some extent [37].…”

Section: B Wavelet Basis Function Parameters and Characteristics Analysismentioning

confidence: 90%

The Optimal Wavelet Basis Function Selection in Feature Extraction of Motor Imagery Electroencephalogram Based on Wavelet Packet Transformation

Cheng

Li³

et al. 2019

IEEE Access

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To solve the problem of optimal wavelet basis function selection in feature extraction of motor imagery electroencephalogram (MI-EEG) by wavelet packet transformation (WPT), based on the analysis of wavelet packet transformation and wavelet basis parameters, combine with the characteristics of MI-EEG, the characteristics of wavelet basis function suitable for feature extraction of MI-EEG are summarized. On the basis of processing and analyzing of two BCI competition data sets, signal to noise ratio (SNR), root mean squared error (RMSE), classification accuracy, and kappa value are introduced as evaluation criteria for feature extraction effect, it is concluded that the rbio2.2 wavelet basis function is the optimal wavelet basis function for feature extraction of MI-EEG. Finally, the MI-EEG collected in the laboratory is processed and analyzed, further proving that the rbio2.2 wavelet basis function is the optimal wavelet basis function for feature extraction of MI-EEG. INDEX TERMSMotor imagery electroencephalogram, signal to noise ratio, root mean squared error, wavelet basis function, deep belief networks.

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Selection of Symlets wavelet function order for EEG signal feature extraction in children with dyslexia

Cited by 7 publications

References 14 publications

Level-K Classification from EEG Signals Using Transfer Learning

Level-K Classification from EEG Signals Using Transfer Learning

A Brief Review on EEG Signal Pre-processing Techniques for Real-Time Brain-Computer Interface Applications

The Optimal Wavelet Basis Function Selection in Feature Extraction of Motor Imagery Electroencephalogram Based on Wavelet Packet Transformation

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