Motor-Imagery EEG Signals Classificationusing SVM, MLP and LDA Classifiers

Narayan, Yogendra

doi:10.17762/turcomat.v12i2.2393

Cited by 18 publications

(9 citation statements)

References 30 publications

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“…Table 2 compares the results of the proposed study with similar studies in recent years. The results showed that the OSVM had increased the accuracy of this model in detecting and classifying the type of MI by the differential evolution algorithm, thereby the accuracy of this method has increased compared to similar methods [Bashar SK et al 2015;Chai R et al 2012;Kim HS et al 2013;Temiyasathit C 2014;Chatterjee R, Bandyopadhyay T, 2016;Mebarkia K, Reffad A 2019;Narayan Y 2021]. Y Narayan (2021) showed that the SVM algorithm followed by the multi-layer perceptron (MLP) classifier had a high accuracy (98.8%) [Narayan Y 2021].…”

Section: Discussionmentioning

confidence: 99%

“…The results showed that the OSVM had increased the accuracy of this model in detecting and classifying the type of MI by the differential evolution algorithm, thereby the accuracy of this method has increased compared to similar methods [Bashar SK et al 2015;Chai R et al 2012;Kim HS et al 2013;Temiyasathit C 2014;Chatterjee R, Bandyopadhyay T, 2016;Mebarkia K, Reffad A 2019;Narayan Y 2021]. Y Narayan (2021) showed that the SVM algorithm followed by the multi-layer perceptron (MLP) classifier had a high accuracy (98.8%) [Narayan Y 2021]. These results were close-knit, similar to our OSVM algorithm (F. , and Mebarkia K, Reffad A (2019) [Mebarkia K, Reffad A 2019] studies with 99.6%, 93.13%, and 94.11% accuracy, respectively).…”

Section: Discussionmentioning

confidence: 99%

“…Feature extraction A review of the research background and records related to extracting features from EEG signals in diagnosing neurological diseases showed that nine features were introduced as the most valuable features [Narayan Y 2021]. These characteristics included mean signal intensity, minimum signal value, signal peak value, signal median, signal standard deviation, energy, corticoids, entropy, and signal skewness.…”

Section: Databasementioning

confidence: 99%

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Motor-Imagery EEG Signal Classification using Optimized Support Vector Machine by Differential Evolution Algorithm

Fard,

Jaseb,

Mehdi Safi

2023

NAMJ

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Background: Motor-Imagery (MI) is a mental or cognitive stimulation without actual sensory input that enables the mind to represent perceptual information. This study aims to use the optimized support vector machine (OSVM) by differential evolution algorithm for motor-Imagery EEG signal classification. Methods: A total of three filters were applied to each signal during the preprocessing phase. The bandstop filter was used to remove urban noise and signal recorders, the median filter to remove random sudden peaks in the signal, and finally, the signal was normalized using the mapminmax filter. The most valuable features were extracted including mean signal intensity, minimum signal value, signal peak value, signal median, signal standard deviation, energy, corticoids, entropy, and signal skewness. Results: The accuracy of the SVM for linear, Gaussian, polynomial, and radial base kernels was 67.3%, 55.1%, 63.6%, and 55.1%, respectively, which was optimized after the classification model by differential evolution algorithm; however, the accuracy for OSVM was increased to 99.6%. Conclusion: Examination of the brain signal appearance for uniform motor-Imagery of both hands showed a significant difference between the signal of motor-Imagery mode with OSVM algorithm (99.6% accuracy), which gave promising results for classification motor imagery EEG signal.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Databasementioning

confidence: 99%

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Motor-Imagery EEG Signal Classification using Optimized Support Vector Machine by Differential Evolution Algorithm

Fard,

Jaseb,

Mehdi Safi

2023

NAMJ

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“…Furthermore, substantia researches showed that network properties can capture the synergistic effect of abnormal paroxysmal firing of neurons more effectively than the characteristics of single channel signals (Wang et al, 2015b ; Busche and Hyman, 2020 ; Gao et al, 2021 ). In addition, the combination of multiple features can help to display EEG abnormalities from various angles and improve the recognition efficiency (Mahato and Paul, 2020 ; Pei et al, 2020 ; Narayan, 2021 ; Wang et al, 2022 ). For instance, Narayan ( 2021 ) extracted and combined the features of AAR parameters, Barlow parameters, Hjorth parameters, etc.…”

Section: Introductionmentioning

confidence: 99%

A novel framework of MOPSO-GDM in recognition of Alzheimer's EEG-based functional network

Wang

Shi

et al. 2023

Front. Aging Neurosci.

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BackgroundMost patients with Alzheimer's disease (AD) have an insidious onset and frequently atypical clinical symptoms, which are considered a normal consequence of aging, making it difficult to diagnose AD medically. But then again, accurate diagnosis is critical to prevent degeneration and provide early treatment for AD patients.ObjectiveThis study aims to establish a novel EEG-based classification framework with deep learning methods for AD recognition.MethodsFirst, considering the network interactions in different frequency bands (δ, θ, α, β, and γ), multiplex networks are reconstructed by the phase synchronization index (PSI) method, and fourteen topology features are extracted subsequently, forming a high-dimensional feature vector. However, in feature combination, not all features can provide effective information for recognition. Moreover, combining features by manual selection is time-consuming and laborious. Thus, a feature selection optimization algorithm called MOPSO-GDM was proposed by combining multi-objective particle swarm optimization (MOPSO) algorithm with Gaussian differential mutation (GDM) algorithm. In addition to considering the classification error rates of support vector machine, naive bayes, and discriminant analysis classifiers, our algorithm also considers distance measure as an optimization objective.ResultsFinally, this method proposed achieves an excellent classification error rate of 0.0531 (5.31%) with the feature vector size of 8, by a ten-fold cross-validation strategy.ConclusionThese findings show that our framework can adaptively combine the best brain network features to explore network synchronization, functional interactions, and characterize brain functional abnormalities, which can improve the recognition efficiency of diseases. While improving the classification accuracy of application algorithms, we aim to expand our understanding of the brain function of patients with neurological disorders through the analysis of brain networks.

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“…The main objective of studying the SEMG signals is to discriminate between various muscle movements into classes by MUAP during contractions [4]. These signals are detected and recorded using the surface electrodes which are placed adjacent to the skin superimposed on the muscles acupressure point [5]. The signal is generated during the contraction of the electrical activity of muscle fibers and the generated SEMG signals contain some useful feature which can be analyzed by the way of applying Wavelet Transform (WT) and other powerful techniques like Empirical Mode Decomposition (EMD) for multifunctional myoelectric control [6].…”

mentioning

confidence: 99%

SEMG Signals Identification Using DT And LR Classifier by Wavelet-Based Features

Narayan

Kumari

Ranjan

2022

IJEER

Self Cite

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In the recent era of technology, biomedical signals have been attracted lots of attention regarding the development of rehabilitation robotic technology. The surface electromyography (SEMG) signals are the fabulous signals utilized in the field of robotics. In this context, SEMG signals have been acquired by twenty-five right-hand dominated healthy human subjects to discriminate the various hand gestures. The placement of SEMG electrodes has been done according to the predefined acupressure point of required hand movements. After the SEMG signal acquisition, pre-processing and noise rejection have been performed. The de-noising and four levels of SEMG signal decomposition have been accomplished by discrete wavelet transform (DWT). In this article, the third and fourth-level detail coefficients have been utilized for time-scale feature extractions. The performance of ten time-scale features has been evaluated and compared to each other with the three-fold cross-validation technique by using a Decision Tree (DT) and Linear Regression (LR) classifier. The results demonstrated that the DT classifier classification accuracy was found superior to the LR classifier. By using the DT classifier technique 96.3% accuracy has been achieved, with all combined features as a feature vector.

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Motor-Imagery EEG Signals Classificationusing SVM, MLP and LDA Classifiers

Cited by 18 publications

References 30 publications

Motor-Imagery EEG Signal Classification using Optimized Support Vector Machine by Differential Evolution Algorithm

Motor-Imagery EEG Signal Classification using Optimized Support Vector Machine by Differential Evolution Algorithm

A novel framework of MOPSO-GDM in recognition of Alzheimer's EEG-based functional network

SEMG Signals Identification Using DT And LR Classifier by Wavelet-Based Features

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