Binary movement classification of sEMG signal using linear SVM and Wavelet Packet Transform

Babita,; Kumari, Preeti; Narayan, Yogendra; Mathew, Lini

doi:10.1109/icpeices.2016.7853640

Cited by 14 publications

(7 citation statements)

References 8 publications

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“…EEG data were acquired from 20 healthy subjects at Bio-Medical Laboratory of NITTTR Chandigarh, India [22], [23]. After the raw EEG signal acquisition, EEG data was passed through a 4 th order band-pass Butterworth filter (8Hz to the 30Hz range) for noise elimination [24].…”

Section: Eeg Data Acquisitionmentioning

confidence: 99%

“…In this study, SVM, MLP and LDAclassifier was compared to each other [30], [31], [32]. Threefold cross-validation technique was applied for achieving the classification accuracy [33]. In threefold cross-validation technique, the whole dataset was divided into three equal parts in which two parts were used for training the classifier whereas one part of data was utilized for testing purpose and no part of data was used for validation the classifiers [34], [35].…”

Section: Classifiersmentioning

confidence: 99%

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

Narayan¹

2021

TURCOMAT

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Electroencephalogram (EEG)signals based brain-computer interfacing (BCI) is the current technology trends in the field of rehabilitation robotic. This study compared the performance of support vector machine (SVM), linear discriminant analysis (LDA) and multi-layer perceptron (MLP) classifier with the combination of eight different features as a feature vector. EEG data were acquired from 20 healthy human subjects with predefined protocols. After the EEG signals acquisition, it was pre-processed followed by feature extraction and classification by using SVM MLP and LDA classifiers. The results exhibited that the SVM method was the best approach with 98.8% classification accuracy followed by MLP classifier. Finally, the SVM classifier and Arduino Mega controller was employed for offline controlling of the gripper of the robotic arm prototype. The finding of this study may be useful for online controlling as well as multi-degree of freedom with multi-class EEG dataset.

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Section: Eeg Data Acquisitionmentioning

confidence: 99%

Section: Classifiersmentioning

confidence: 99%

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

Narayan¹

2021

TURCOMAT

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“…are common utilized. Babita et al [36] employed linear SVM and wavelet packet transform to classify binary elbow flexion and extension. A 91.1% classification accuracy was observed for this method.…”

Section: Machine Learning Based Discrete-motion Classificationmentioning

confidence: 99%

sEMG Based Human Motion Intention Recognition

Zhang

Liu

Han

et al. 2019

Journal of Robotics

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Human motion intention recognition is a key to achieve perfect human-machine coordination and wearing comfort of wearable robots. Surface electromyography (sEMG), as a bioelectrical signal, generates prior to the corresponding motion and reflects the human motion intention directly. Thus, a better human-machine interaction can be achieved by using sEMG based motion intention recognition. In this paper, we review and discuss the state of the art of the sEMG based motion intention recognition that is mainly used in detail. According to the method adopted, motion intention recognition is divided into two groups: sEMG-driven musculoskeletal (MS) model based motion intention recognition and machine learning (ML) model based motion intention recognition. The specific models and recognition effects of each study are analyzed and systematically compared. Finally, a discussion of the existing problems in the current studies, major advances, and future challenges is presented.

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“…Then, these features were classified with an average accuracy of 89.4% with the Extreme Learning Machine (ELM) and 94% with the Support Vector Machine (SVM). Kumari et al [2] classified the time domain and time scale properties of EMG signals with SVM to facilitate the life of partly disabled people. In their study, feature extraction was performed with Wavelet Packet Transform (WPT) method using 303 EMG data obtained from 8 participants between the ages of 18-30.…”

Section: Introductionmentioning

confidence: 99%

Normal Cumulative Distribution Function and Dispersion Entropy Based EMG Classification

Aslan

2020

Sakarya University Journal of Computer and Information Sciences

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Electromyography (EMG) is used to measure muscle activity. EMG signals are widely used in many biomedical practices such as motion recognition, prosthetic control, physical rehabilitation, and human-computer interfaces. The effective use of EMG in such practices depends on distinctive feature extraction. In this study, Dispersion Entropy (DisEn) and Normal Cumulative Distribution Function (NCDF) methods are used for feature extraction from EMG signals. The suggested method was tested with a data set containing immersion of six different objects. In the experimental studies, the proposed method distinguished the movements with an accuracy performance of 98%. When compared to other methods using the same data set, the suggested method has about 1.2% better performance.

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Binary movement classification of sEMG signal using linear SVM and Wavelet Packet Transform

Cited by 14 publications

References 8 publications

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

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

sEMG Based Human Motion Intention Recognition

Normal Cumulative Distribution Function and Dispersion Entropy Based EMG Classification

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