MI-EEG classification using Shannon complex wavelet and convolutional neural networks

Wang, Chang; Wu, Yang Chang; Wang, Chen; Zhu, Yu; Wang, Chong; Niu, Yanxiang; Shao, Zhenpeng; Gao, Xudong; Zhao, Zongya; Yu, Yi

doi:10.1016/j.asoc.2022.109685

Cited by 14 publications

(2 citation statements)

References 34 publications

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“…Then, the extracted features were applied to Least Square Support Vector Machine LS-SVM to classify sleep stages. C. Wang et al [8] Applied a new method to enhance classification accuracy by using Shannon Complex Wavelets (SCW) with Convolutional Neural Networks (CNN). The method consists of three stages.…”

Section: Introductionmentioning

confidence: 99%

An Optimized Quadratic Support Vector Machine for EEG Based Brain Computer Interface

Maher

Haikal

Elhosseini

et al. 2023

Int. j. electr. comput. eng. syst. (Online)

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The Brain Computer Interface (BCI) has a great impact on mankind. Many researchers have been trying to employ different classifiers to figure out the human brain's thoughts accurately. In order to overcome the poor performance of a single classifier, some researchers used a combined classifier. Others delete redundant information in some channels before applying the classifier as they thought it might reduce the accuracy of the classifier. BCI helps clinicians to learn more about brain problems and disabilities such as stroke to use in recovery. The main objective of this paper is to propose an optimized High-Performance Support Vector Machines (SVM) based classifier (HPSVM-BCI) using the SelectKBest (SKB). In the proposed HPSVM-BCI, the SKB algorithm is used to select the features of the BCI competition III Dataset IVa subjects. Then, to classify the prepared data from the previous phase, SVM with Quadratic kernel (QSVM) were used in the second phase. As well as enhancing the mean accuracy of the dataset, HPSVM-BCI reduces the computational cost and computational time. A major objective of this research is to improve the classification of the BCI dataset. Furthermore, decreased feature count translates to fewer electrodes, a factor that reduces the risk to the human brain. Comparative studies have been conducted with recent models using the same dataset. The results obtained from the study show that HPSVM-BCI has the highest average accuracy, with 99.24% for each subject with 40 channels only.

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Section: Introductionmentioning

confidence: 99%

An Optimized Quadratic Support Vector Machine for EEG Based Brain Computer Interface

Maher

Haikal

Elhosseini

et al. 2023

Int. j. electr. comput. eng. syst. (Online)

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“…Lee et al proposed a motor imagery classification scheme based on the continuous wavelet transform with three mother wavelets and the convolutional neural network to capture a highly informative EEG image by combining time-frequency and electrode location, to classify motor imagery tasks, and reduce computation complexity [18]. Wang et al proposed a new MI-EEG classification method to improve classification accuracy by combining Shannon complex wavelets and convolutional neural networks [19]. Discrete wavelet transform (DWT) is a traditional discrete version of WT, and many works have proven that combining DWT with machine learning pattern recognition methods can classify MI-EEG signals [20][21][22].…”

Section: Introductionmentioning

confidence: 99%

Feature Extraction of Motor Imagery EEG via Discrete Wavelet Transform and Generalized Maximum Fuzzy Membership Difference Entropy: A Comparative Study

et al. 2023

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Identifying motor imagery (MI) electroencephalogram (EEG) is an important way to achieve brain–computer interface (BCI), but its applicability is heavily dependent on the performance of feature extraction procedure. In this paper, a feature extraction method based on generalized maximum fuzzy membership difference entropy (GMFMDE) and discrete wavelet transform (DWT) was proposed for the feature extraction of EEG signals. The influence of different distance calculation methods, embedding dimensions and tolerances were studied to find the best configuration of GMFMDE for the feature extraction of MI–EEG. The gradient boosting decision tree (GBDT) classifier was used to classify the features extracted from GMFMDE and DWT. The average classification accuracy of 93.71% and the maximum classification accuracy of 96.96% were obtained, which proved the effectiveness of the proposed feature extraction method for EEG signal feature extraction.

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An adaptive session-incremental broad learning system for continuous motor imagery EEG classification

Yang,

Li,

Wang

2024

Med Biol Eng Comput

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MI-EEG classification using Shannon complex wavelet and convolutional neural networks

Cited by 14 publications

References 34 publications

An Optimized Quadratic Support Vector Machine for EEG Based Brain Computer Interface

An Optimized Quadratic Support Vector Machine for EEG Based Brain Computer Interface

Feature Extraction of Motor Imagery EEG via Discrete Wavelet Transform and Generalized Maximum Fuzzy Membership Difference Entropy: A Comparative Study

An adaptive session-incremental broad learning system for continuous motor imagery EEG classification

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