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Abdulhamit Subasi
Researcher at Effat University
Publications - 188
Citations - 10707
Abdulhamit Subasi is an academic researcher from Effat University. The author has contributed to research in topics: Feature extraction & Support vector machine. The author has an hindex of 39, co-authored 175 publications receiving 7908 citations. Previous affiliations of Abdulhamit Subasi include Kahramanmaraş Sütçü İmam University & University College of Engineering.
Papers
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EEG signal classification using wavelet feature extraction and a mixture of expert model
TL;DR: A double-loop Expectation-Maximization (EM) algorithm has been introduced to the ME network structure for detection of epileptic seizure and the results confirmed that the proposed Me network structure has some potential in detecting epileptic seizures.
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EEG signal classification using PCA, ICA, LDA and support vector machines
TL;DR: In this work, a versatile signal processing and analysis framework for Electroencephalogram (EEG) was proposed and a set of statistical features was extracted from the sub-bands to represent the distribution of wavelet coefficients.
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Classification of EEG signals using neural network and logistic regression.
Abdulhamit Subasi,Ergun Erçelebi +1 more
TL;DR: Two fundamentally different approaches for designing classification models (classifiers) the traditional statistical method based on logistic regression and the emerging computationally powerful techniques based on ANN are introduced.
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Classification of EMG signals using PSO optimized SVM for diagnosis of neuromuscular disorders
TL;DR: A novel PSO-SVM model has been proposed that hybridized the particle swarm optimization (PSO) and SVM to improve the EMG signal classification accuracy and validate the superiority of the SVM method compared to conventional machine learning methods.
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Comparison of signal decomposition methods in classification of EEG signals for motor-imagery BCI system
Jasmin Kevric,Abdulhamit Subasi +1 more
TL;DR: Results indicate that the proposed model has the potential to obtain a reliable classification of motor imagery EEG signals, and can thus be used as a practical system for controlling a wheelchair.