Motor imagery EEG classification based on ensemble support vector learning

Luo, Jiebo; Gao, Xing; Zhu, Xiaobei; Wang, Bin; Lü, Na; Wang, Jie

doi:10.1016/j.cmpb.2020.105464

Cited by 64 publications

(33 citation statements)

References 34 publications

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“…Luo, Jing Et al. [1] to planning to improve the presentation of engine symbolism-based EEG classification in a couple channel circumstance, an ESVL-based methodology is talked about to join the benefits of the ERD/ERS-based highlights and the occasion related potential-based highlights in engine symbolism-based EEG classification. ESVL is a troupe learning calculation dependent on help vector machine classifier.…”

Section: Related Workmentioning

confidence: 99%

“…The processing of common spatial pattern deal with maximize the variance of data stemming for the classes. The process of signal represents as signal S of an EEG sample S=WM,…………………… (1) The value of M is a Nc X T matrix of EEG data and W is sample value of L X Nc represents the projection of CSP. The source of CSP algorithm develop for the two-class problem, further it explodes in multi-class problem extension.…”

Section: Feature Extraction and Machine Learning Algorithmmentioning

confidence: 99%

“…The brain-computer interface (BCI) is key components to communicate the human brain to the outside world. Currently, two types of BCI application are used invasive and non-invasive [1]. The non-invasive approach is primarily used due to low cost and convenient operation of the system.…”

Section: Introductionmentioning

confidence: 99%

“…Figure 2: Comparison performance analysis of sensitivity used techniques are ESVL [1], LSTM [6], KNN [7], ELM [11] and CNN [13] classification with BC4 competition dataset subject 1 to subject 9. [6], KNN [7], ELM [11] and CNN [13] classification with BC4 competition dataset subject 1 to subject 9.…”

mentioning

confidence: 99%

“…Figure3: Comparison performance analysis of specificity used techniques are ESVL[1], LSTM[6], KNN[7], ELM[11] and CNN[13] classification with BC4 competition dataset subject 1 to subject 9.…”

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confidence: 99%

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Analysis of Motor Imagery EEG Classification Based on Feature Extraction and Machine Learning Algorithm

Chintamani¹

2021

ITII

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The brain-computer interface provides the excellent potential to address nervous system-related activity. The function of the nervous system work between internal brain control and external human body physical structure. Some parts of the human body cannot generate the signal for the processing of the human brain, cannot recognize and identify human body parts' activity—the motor imagery EEG classification approach helps resolve such types of critical illness cause of death. The dimension and structure of motor imagery-based EEG data are very high and unsupported behaviors. The machine learning and another classification algorithm cannot handle these variants of EEG data directly. For the process of better classification of motor imagery, EEG needs transformation and extraction. The transform-based feature extraction process such as DCT, DWT, SFTF and some other harmonic frequency-based applied. In this paper presents the details analysis of feature extraction and classification algorithms for motor imagery EEG classification. The machine learning provides three types of an algorithm for classification, supervised, unsupervised and semi-supervised. This paper mainly focuses on the supervised machine learning algorithm. For the analysis of machine learning algorithm use BC competition-IV dataset. MATLAB software is used as a tool for the code of algorithms and measures standard parameters such as accuracy, sensitivity and specificity.

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Section: Related Workmentioning

confidence: 99%

Section: Feature Extraction and Machine Learning Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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confidence: 99%

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confidence: 99%

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Analysis of Motor Imagery EEG Classification Based on Feature Extraction and Machine Learning Algorithm

Chintamani¹

2021

ITII

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The application of hybrid feature based on local mean decomposition for motor imagery electroencephalogram signal classification

Chen

2023

Asian Journal of Control

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This paper proposed a hybrid feature extraction algorithm based on local mean decomposition (LMD), which has better solved the existing problems of low classification performance and adaptability limitation. LMD is employed to decompose the electroencephalogram (EEG) signal into multiple components, and then, the hybrid features based on instantaneous energy, fuzzy entropy, and mathematical morphological features are extracted on specific components, and the optimal feature combination is selected by analysis of variance (ANOVA). Finally, the classification result is output by the linear discriminant analysis (LDA) classifier. The results show that the maximum accuracy of the subjects in Data Set III of BCI‐II by the method in this paper is 92.14%, and the maximum mutual information value is 0.8. The number of novel features used in this paper is small, and the complexity of the algorithm is reduced. It can adaptively select effective features according to individual differences and has good robustness.

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Review on Motor Imagery Based EEG Signal Classification for BCI Using Deep Learning Techniques

Venu¹,

Natesan²

2021

Advances in Robotics, Automation and Data Analytics

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Motor imagery EEG classification based on ensemble support vector learning

Cited by 64 publications

References 34 publications

Analysis of Motor Imagery EEG Classification Based on Feature Extraction and Machine Learning Algorithm

Analysis of Motor Imagery EEG Classification Based on Feature Extraction and Machine Learning Algorithm

The application of hybrid feature based on local mean decomposition for motor imagery electroencephalogram signal classification

Review on Motor Imagery Based EEG Signal Classification for BCI Using Deep Learning Techniques

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