A comparative study of wavelet families for EEG signal classification

Gandhi, Tapan Kumar; Panigrahi, B. K.; Anand, Sneh

doi:10.1016/j.neucom.2011.04.029

Cited by 275 publications

(121 citation statements)

References 30 publications

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“…Selection of suitable wavelet is crucial for the analysis of signals through wavelet transform. Based on the biomedical signal to be analyzed, the mother wavelet is chosen [14] [15].…”

Section: Wavelet Familymentioning

confidence: 99%

“…As mentioned in [15], based on the classification accuracy and computational time obtained in the experiment, it was found that Coiflet of order 1(Coif1) is the best wavelet family for analysis of EEG signal as the support width of the mother wavelet function resembles that of the EEG signal and also has a compact filter length, thus reducing the processing time. This argument is being challenged by many researchers [16][17] and recommends Haar and, second and fourth order Daubechies (Db2, Db4) wavelets for signal preprocessing and feature extraction and were provided better accuracy in the recent classifications.…”

Section: Wavelet Familymentioning

confidence: 99%

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Multiresolution Analysis in EEG Signal Feature Engineering for Epileptic Seizure Detection

Martin¹,

Sujatha²,

Swapna³

2018

IJCA

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In biomedical engineering, many attempts are being reported over the years for automated diagnosis of various brain disorders by classifying EEG (Electroencephalography) signals. Various machine learning algorithms are adopted to address different scenarios in EEG classifications. Feature engineering is playing a vital role in order to enhance the classification efficiency particularly in signal processing applications. This paper elucidates multi-resolution analysis (MRA) of feature engineering and demonstrates how the distinctive features are being engineered in wavelet domain. The implementation results are placed in the form of feature distribution diagrams and provide clear indications in feature selection for epilepsy seizure detection through classification.

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“…Selection of suitable wavelet is crucial for the analysis of signals through wavelet transform. Based on the biomedical signal to be analyzed, the mother wavelet is chosen [14] [15].…”

Section: Wavelet Familymentioning

confidence: 99%

Section: Wavelet Familymentioning

confidence: 99%

Multiresolution Analysis in EEG Signal Feature Engineering for Epileptic Seizure Detection

Martin¹,

Sujatha²,

Swapna³

2018

IJCA

View full text Add to dashboard Cite

show abstract

“…Examples of application domains are speech recognition [6], audio classification [7], texture classification [8], and medical signal classification [9].…”

Section: A Wavelet Transform In Time Series Analysismentioning

confidence: 99%

Multiresolution Analysis Based on Wavelet Transform for Commodity Prices Time Series Forecasting

Bunrit¹,

Kerdprasop²,

Kerdprasop³

2018

IJMLC

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Abstract-This research proposes time series forecasting of commodity prices by using multiresolution analysis from wavelet transform. In this work, discrete wavelet transform based multiresolution decomposition of Deubechies family is used. Firstly, Deubechies wavelet transform is applied to the training set of time series data up to level four. The reconstruction values of the approximation part of wavelet from each level are then used for the forecasting process by ARIMA model. The validation set of data is used to analyze and select the best model from all 4 levels of multiresolution decomposition. Finally, best selected validating model is used for evaluating the remaining testing data set. The forecasting results by using multiresolution analysis are compared to the case where the original data are directly modeled and forecasted by ARIMA. Results based on the mean absolute percentage error evaluation from using multiresolution analysis are better for both of the two studied data including daily gold price and rubber price. By applying multiresolution analysis, the improvement is 10.83% for gold price and 42.68% for rubber price. The variances of errors from the proposed method on both data sets are also much less than directly use the original time series data for forecasting.

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“…You have to deliberately choose from various kinds of wavelet families, such as Haar wavelet, Daubechies wavelet, Symlet wavelet, Coif Wavelet, Bior Wavelet, etc. (Gandhi et al, 2011). In this work we simply choose the Db-4 wavelet to do CWT for each channel signal, which is formulated as Formula 2, where ƒ(t) is the original EEG signal.…”

Section: Wavelet Based Sparse Representationmentioning

confidence: 99%

Deep fusion of multi-channel neurophysiological signal for emotion recognition and monitoring

Song

Zhang

et al. 2017

IJDMB

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How to fuse multi-channel neurophysiological signals for emotion recognition is emerging as a hot research topic in community of Computational Psychophysiology. Nevertheless, prior feature engineering based approaches require extracting various domain knowledge related features at a high time cost. Moreover, traditional fusion method cannot fully utilise correlation 2 X. Li et al.information between different channels and frequency components. In this paper, we design a hybrid deep learning model, in which the 'Convolutional Neural Network (CNN)' is utilised for extracting task-related features, as well as mining inter-channel and inter-frequency correlation, besides, the 'Recurrent Neural Network (RNN)' is concatenated for integrating contextual information from the frame cube sequence. Experiments are carried out in a trial-level emotion recognition task, on the DEAP benchmarking dataset. Experimental results demonstrate that the proposed framework outperforms the classical methods, with regard to both of the emotional dimensions of Valence and Arousal.Keywords: affective computing; CNN; time series data analysis; EEG; emotion recognition; LSTM; multi-channel data fusion; multi-modal data fusion; physiological signal; RNN.Reference to this paper should be made as follows: Li, X., Song, D., Zhang, P., Hou, Y. and Нu, B. (2017) 'Deep fusion of multi-channel neurophysiological signal for emotion recognition and monitoring', Int.

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A comparative study of wavelet families for EEG signal classification

Cited by 275 publications

References 30 publications

Multiresolution Analysis in EEG Signal Feature Engineering for Epileptic Seizure Detection

Multiresolution Analysis in EEG Signal Feature Engineering for Epileptic Seizure Detection

Multiresolution Analysis Based on Wavelet Transform for Commodity Prices Time Series Forecasting

Deep fusion of multi-channel neurophysiological signal for emotion recognition and monitoring

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