Classification of Motor Imagery Task by Using Novel Ensemble Pruning Approach

Ali, Muhammad Ammar; Üçüncü, Duygu; Ataş, Pınar Karadayı; Özöğür‐Akyüz, Süreyya

doi:10.1109/tfuzz.2019.2900859

Cited by 12 publications

(3 citation statements)

References 32 publications

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“…Ali, Muhammad Ammar Et al. [27] D2D correspondence is quickly advancing into a reasonable strategy for data trade in a cell organization. It has an extremely low start to finish inactivity and can increment ghastly proficiency of a cell organization.…”

Section: Related Workmentioning

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%

“…The design architecture of CNN is feedforwarded for the sequence of feature subset. The proposed CNN algorithm along with ensemble classifier and enhance the rate of classification [27].…”

Section: (E) Cnn (Convolutional Neural Network)mentioning

confidence: 99%

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

Chintamani¹

2021

ITII

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“…Agarwal et al (2015),So et al (2017),Yin and Jianhua (2017),Dagois et al (2019),Gupta et al (2019),Ali et al (2020),Antelis et al (2020), Grissmann et al (2020 3 Power: area under PSD, FFT;Keirn and Aunon (1990),Michel et al (1994),Harmony et al (1996), Laine et al (2002), Scherer et al (2008), Lin et al (2009), Zhang et al (2010), Hogervorst et al (2014), Wang et al (2016), Myrden and Chau (2017), Yin and Jianhua (2017), Fan et al (2018) 4 ARCF: Burg, Yule-Walker; Keirn and Aunon (1990), Anderson et al (1996, 1998), Popivanov et al (1998), Liang et al (2006), Wang and Sourina (2013), Zhang et al (2017), Zeynali et al (2019), Ali et al (2020), Ergün et al (2020) 5 En: approximate entropy (ApEn), Shen, Sampen, LEE, spen; Guo et al (2010), Gupta et al (2015), Zarjam et al (2015), Zhang et al (2017), Alyasseri et al (2019), Tian et al (2019), Yang et al (2019), Zeynali et al (2019), O(N 2 ) ApEn: requires more computation time, strongly dependent on record length Guo et al (2010). 6 Statistical: mean, VAR, KS, SKW, SD; Wang and Sourina (2013), Gupta et al (2015), Zarjam et al (2015), Wang et al (2016), Fan et al (2018), Alyasseri et al (2019), Yang et al (2019), Chakladar et al (2020) Note: L: Local; G: Global; P r : Band power (BP) at right hemisphere electrode; P l : BP at left hemisphere electrode; LEE: log energy entropy; spen: spectral entropy; PDF: probability distribution function.…”

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confidence: 99%

State-of-the-art mental tasks classification based on electroencephalograms: a review

Saini

Satija

2023

Physiol. Meas.

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Electroencephalogram (EEG) plays an important role in analyzing different mental tasks and neurological disorders. Hence, it is a critical component for designing various applications, such as brain-computer interfaces, neurofeedback, etc. Mental task classification (MTC) is one of the research focuses in these applications. Therefore, numerous MTC techniques have been proposed in literary works. Although various literature reviews exist based on EEG signals for different neurological disorders and behavior analysis, there is a lack of a review of state-of-the-art MTC techniques. Therefore, this paper presents a detailed review for the MTC techniques including the classification of mental tasks and mental workload. A brief description of EEG along with its physiological and non-physiological artifacts is also presented. Further, we include information on several publicly available databases, features, classifiers, and performance metrics used in MTC studies. We implement and evaluate some of the commonly used existing MTC techniques in the presence of different artifacts and subjects, based on which the challenges and directions are highlighted for future research in MTC.

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A New Ensemble Pruning Method Based on Margin and Diversity

Shen

Liu

2021

Mobile Multimedia Communications

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Classification of Motor Imagery Task by Using Novel Ensemble Pruning Approach

Cited by 12 publications

References 32 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

State-of-the-art mental tasks classification based on electroencephalograms: a review

A New Ensemble Pruning Method Based on Margin and Diversity

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