An improved fast shapelet selection algorithm and its application to pervasive EEG

Zou, Xiunan; Zheng, Xiangwei; Ji, Cun; Zhang, Yuang

doi:10.1007/s00779-020-01501-4

Cited by 6 publications

(2 citation statements)

References 30 publications

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“…Shapelets are a family of algorithms that focus on finding short patterns, called shapelets, appearing anywhere in the time series. A class is then distinguished by the presence or absence of one or more shapelets somewhere in the series 47 . The MTF and GAF encode times series to 2D images, which can serve as an input to a neural network 46 , 48 .…”

Section: Related Workmentioning

confidence: 99%

Classification of hand and wrist movements via surface electromyogram using the random convolutional kernels transform

Ovadia,

Segal,

Rabin

2024

Sci Rep

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Prosthetic devices are vital for enhancing personal autonomy and the quality of life for amputees. However, the rejection rate for electric upper-limb prostheses remains high at around 30%, often due to issues like functionality, control, reliability, and cost. Thus, developing reliable, robust, and cost-effective human-machine interfaces is crucial for user acceptance. Machine learning algorithms using Surface Electromyography (sEMG) signal classification hold promise for natural prosthetic control. This study aims to enhance hand and wrist movement classification using sEMG signals, treated as time series data. A novel approach is employed, combining a variation of the Random Convolutional Kernel Transform (ROCKET) for feature extraction with a cross-validation ridge classifier. Traditionally, achieving high accuracy in time series classification required complex, computationally intensive methods. However, recent advances show that simple linear classifiers combined with ROCKET can achieve state-of-the-art accuracy with reduced computational complexity. The algorithm was tested on the UCI sEMG hand movement dataset, as well as on the Ninapro DB5 and DB7 datasets. We demonstrate how the proposed approach delivers high discrimination accuracy with minimal parameter tuning requirements, offering a promising solution to improve prosthetic control and user satisfaction.

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Section: Related Workmentioning

confidence: 99%

Classification of hand and wrist movements via surface electromyogram using the random convolutional kernels transform

Ovadia,

Segal,

Rabin

2024

Sci Rep

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show abstract

“…Zhang et al proposed a data preprocessing method with key points of time series, which reduced the shapelet candidate space and improved the efficiency of algorithm [12]. In recent years, several researchers have begun to apply shapelet to deal with nonlinear time series and achieved excellent results [13][14][15][16]. From the perspective of feature extraction, inspired by the above researches, we attempt to apply shapelet technique to automatically extract ERP components, such as N200, P300, N300 and so on, and use them in consciousness emotion recognition.…”

Section: Introductionmentioning

confidence: 99%

A novel consciousness emotion recognition method using ERP components and MMSE

Zheng

Zhang

et al. 2021

J. Neural Eng.

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Objective. Electroencephalogram (EEG) based emotion recognition mainly extracts traditional features from time domain and frequency domain, and the classification accuracy is often low for the complex nature of EEG signals. However, to the best of our knowledge, the fusion of event-related potential (ERP) components and traditional features is not employed in emotion recognition, and the ERP components are only identified and analyzed by the psychology professionals, which is time-consuming and laborious. Approach. In order to recognize the consciousness and unconsciousness emotions, we propose a novel consciousness emotion recognition method using ERP components and modified multi-scale sample entropy (MMSE). Firstly, ERP components such as N200, P300 and N300 are automatically identified and extracted based on shapelet technique. Secondly, variational mode decomposition and wavelet packet decomposition are utilized to process EEG signals for obtaining different levels of emotional variational mode function (VMF), namely V M F β + γ , and then nonlinear feature MMSE of each V M F β + γ are extracted. At last, ERP components and nonlinear feature MMSE are fused to generate a new feature vector, which is fed into random forest to classify the consciousness and unconsciousness emotions. Main results. Experimental results demonstrate that the average classification accuracy of our proposed method reach 94.42%, 94.88%, and 94.95% for happiness, horror and anger, respectively. Significance. Our study indicates that the fusion of ERP components and nonlinear feature MMSE is more effective for the consciousness and unconsciousness emotions recognition, which provides a new research direction and method for the study of nonlinear time series.

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Identifiable Temporal Feature Selection via Horizontal Visibility Graph Towards Smart Medical Applications

Wang

et al. 2021

Interdiscip Sci Comput Life Sci

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An improved fast shapelet selection algorithm and its application to pervasive EEG

Cited by 6 publications

References 30 publications

Classification of hand and wrist movements via surface electromyogram using the random convolutional kernels transform

Classification of hand and wrist movements via surface electromyogram using the random convolutional kernels transform

A novel consciousness emotion recognition method using ERP components and MMSE

Identifiable Temporal Feature Selection via Horizontal Visibility Graph Towards Smart Medical Applications

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