Using genetic algorithms and k-nearest neighbour for automatic frequency band selection for signal classification

Rivero, Daniel; Guo, Ling; Seoane, José A.; Dorado, Julián

doi:10.1049/iet-spr.2010.0215

Cited by 13 publications

(8 citation statements)

References 22 publications

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“…SVM operator supports types C-SVC and nu-SVC for classification tasks; epsilon-SVR and nu-SVR types for regression tasks. Finally, the one-class type is used for distribution estimation [ 13 , 24 , 46 , 49 ]. In this research, SVM configuration is consist of both nu-SVC and radial basis function kernel were used for SVM configurations consist of both classification technique.…”

Section: Methodsmentioning

confidence: 99%

Ensemble Classifier for Epileptic Seizure Detection for Imperfect EEG Data

Abualsaud

Mahmuddin

Saleh

et al. 2015

The Scientific World Journal

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Brain status information is captured by physiological electroencephalogram (EEG) signals, which are extensively used to study different brain activities. This study investigates the use of a new ensemble classifier to detect an epileptic seizure from compressed and noisy EEG signals. This noise-aware signal combination (NSC) ensemble classifier combines four classification models based on their individual performance. The main objective of the proposed classifier is to enhance the classification accuracy in the presence of noisy and incomplete information while preserving a reasonable amount of complexity. The experimental results show the effectiveness of the NSC technique, which yields higher accuracies of 90% for noiseless data compared with 85%, 85.9%, and 89.5% in other experiments. The accuracy for the proposed method is 80% when SNR = 1 dB, 84% when SNR = 5 dB, and 88% when SNR = 10 dB, while the compression ratio (CR) is 85.35% for all of the datasets mentioned.

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Section: Methodsmentioning

confidence: 99%

Ensemble Classifier for Epileptic Seizure Detection for Imperfect EEG Data

Abualsaud

Mahmuddin

Saleh

et al. 2015

The Scientific World Journal

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“…Kumar et al [ 26 ] proposed three methods to optimize the temporal filter parameters, including particle swarm optimization (PSO), genetic algorithm (GA), and artificial bee colony (ABC). Rivero et al [ 27 ] used genetic algorithms and k-nearest neighbor for automatic frequency band selection. The first method uses the time-frequency analysis to obtain frequency information.…”

Section: Introductionmentioning

confidence: 99%

The CSP-Based New Features Plus Non-Convex Log Sparse Feature Selection for Motor Imagery EEG Classification

Zhang

Zhu

Zhang

et al. 2020

Sensors

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The common spatial pattern (CSP) is a very effective feature extraction method in motor imagery based brain computer interface (BCI), but its performance depends on the selection of the optimal frequency band. Although a lot of research works have been proposed to improve CSP, most of these works have the problems of large computation costs and long feature extraction time. To this end, three new feature extraction methods based on CSP and a new feature selection method based on non-convex log regularization are proposed in this paper. Firstly, EEG signals are spatially filtered by CSP, and then three new feature extraction methods are proposed. We called them CSP-wavelet, CSP-WPD and CSP-FB, respectively. For CSP-Wavelet and CSP-WPD, the discrete wavelet transform (DWT) or wavelet packet decomposition (WPD) is used to decompose the spatially filtered signals, and then the energy and standard deviation of the wavelet coefficients are extracted as features. For CSP-FB, the spatially filtered signals are filtered into multiple bands by a filter bank (FB), and then the logarithm of variances of each band are extracted as features. Secondly, a sparse optimization method regularized with a non-convex log function is proposed for the feature selection, which we called LOG, and an optimization algorithm for LOG is given. Finally, ensemble learning is used for secondary feature selection and classification model construction. Combing feature extraction and feature selection methods, a total of three new EEG decoding methods are obtained, namely CSP-Wavelet+LOG, CSP-WPD+LOG, and CSP-FB+LOG. Four public motor imagery datasets are used to verify the performance of the proposed methods. Compared to existing methods, the proposed methods achieved the highest average classification accuracy of 88.86, 83.40, 81.53, and 80.83 in datasets 1–4, respectively. The feature extraction time of CSP-FB is the shortest. The experimental results show that the proposed methods can effectively improve the classification accuracy and reduce the feature extraction time. With comprehensive consideration of classification accuracy and feature extraction time, CSP-FB+LOG has the best performance and can be used for the real-time BCI system.

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“…The use of wavelet-based decompositions has also been applied to the development of features for speech and emotion recognition [17,18]. Other interesting proposals involve the use of evolutionary computing for the optimisation of over-complete decompositions for signal approximation [19], for the design of finite impulse response filters [20] and for the extraction frequency-domain features [21]. Also, in [22] a genetic algorithm (GA) was employed for the selection of an appropriate wavelet packet basis for image watermarking.…”

Section: Introductionmentioning

confidence: 99%

Multi‐objective optimisation of wavelet features for phoneme recognition

Vignolo

Rufiner

Milone

2016

IET signal process.

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One of the most important issues in speech applications involves the preprocessing stage, which is meant to produce a manageable set of significant features, exploiting the capabilities of the classification phase [5]. The most widely used features for speech recognition, and also applied for different tasks involving speech and music signals, are the mel-frequency cepstral coefficients (MFCCs) [1,5]. These are based on the linear model of voice production and a psychoacoustic scale [5]. Even though MFCCs provide acceptable performance under laboratory conditions, recognition rates degrade significantly in presence of noise. This has motivated many advances in the development of robust feature extraction approaches, like perceptual linear prediction (PLP) and relative spectra [1]. More recently, speech processing techniques based on computational intelligence tools have been developed. For example, several approaches based on evolutionary computation have been proposed for the search of optimal speech representations [8]. Wavelet based processing provides useful tools for the analysis of nonstationary signals, which have been found suitable for speech feature extraction [6]. In order to build a representation based on the wavelet packet transform (WPT), frequently a particular orthogonal basis is selected among all the available basis [6]. However, for speech recognition there is no evidence showing the convenience of the use of orthogonal basis. Therefore, removing the orthogonality restriction the complete WPT decomposition offers a highly redundant set of coefficients, some of which can be selected to build an optimal representation.The optimisation of wavelet decompositions for feature extraction has been studied in many different ways, though it is still an open challenge in speech processing. For example, the optimisation of wavelet decompositions by means of evolutionary algorithms was proposed for image watermarking [4] and for signal denoising [2]. In [9] we proposed a novel approach for the optimisation of over-complete decompositions from a WPT dictionary based on a multi-objective genetic algorithm (MOGA). The MOGA allows to maximise the classification accuracy while minimising the number of features. For the purpose of obtaining appropriate features for state of the art speech recognizers, a classifier based on hidden Markov models (HMM) is used to estimate the capability of candidate solutions, using on a set of English phonemes. The proposed method, which we refer to as evolutionary wavelet packets (EWP), exploits the benefits provided

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Using genetic algorithms and k-nearest neighbour for automatic frequency band selection for signal classification

Cited by 13 publications

References 22 publications

Ensemble Classifier for Epileptic Seizure Detection for Imperfect EEG Data

Ensemble Classifier for Epileptic Seizure Detection for Imperfect EEG Data

The CSP-Based New Features Plus Non-Convex Log Sparse Feature Selection for Motor Imagery EEG Classification

Multi‐objective optimisation of wavelet features for phoneme recognition

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