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Brain-computer interfaces (BCIs) hold great promise in biomedical engineering, particularly for diagnosing critical diseases. Motor imagery (MI) EEG classification, a key BCI process, faces challenges due to the complexity and non-stationary nature of EEG signals. These signals, recorded via electrodes, are digitized and analyzed using feature extraction techniques like FFT, STFT, CSP, and wavelet transforms, with wavelet transform being the most effective.This study proposes a deep neural network-based classification algorithm with teacher-learning-based optimization for feature refinement. Tested on a standard BCI dataset in MATLAB, the algorithm surpasses Bayesian and ensemble machine learning classifiers, enhancing classification accuracy and BCI system performance.
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Feature Optimization of Motor Imagery EEG Classification using ML
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LAP LAMBERT Academic Publishing, 2024
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Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Singh PriyankaPriyanka Singh is an assistant professor in the Department of Computer Science and Engineering at Lakshmi Narain College of Technology Excellence, Bhopal. She holds an M.Tech. in Software Systems and is pursuing a Ph.D. Codice articolo 2098218191
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Feature Optimization of Motor Imagery EEG Classification using ML
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ISBN 13: 9786205492451
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Feature Optimization of Motor Imagery EEG Classification using ML
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ISBN 13: 9786205492451
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Feature Optimization of Motor Imagery EEG Classification using ML
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Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware 72 pp. Englisch. Codice articolo 9786205492451
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Feature Optimization of Motor Imagery EEG Classification using ML
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LAP LAMBERT Academic Publishing Dez 2024, 2024
ISBN 10: 6205492458
ISBN 13: 9786205492451
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Taschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Brain-computer interfaces (BCIs) hold great promise in biomedical engineering, particularly for diagnosing critical diseases. Motor imagery (MI) EEG classification, a key BCI process, faces challenges due to the complexity and non-stationary nature of EEG signals. These signals, recorded via electrodes, are digitized and analyzed using feature extraction techniques like FFT, STFT, CSP, and wavelet transforms, with wavelet transform being the most effective.This study proposes a deep neural network-based classification algorithm with teacher-learning-based optimization for feature refinement. Tested on a standard BCI dataset in MATLAB, the algorithm surpasses Bayesian and ensemble machine learning classifiers, enhancing classification accuracy and BCI system performance.Books on Demand GmbH, Überseering 33, 22297 Hamburg 72 pp. Englisch. Codice articolo 9786205492451
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Feature Optimization of Motor Imagery EEG Classification using ML
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LAP LAMBERT Academic Publishing, 2024
ISBN 10: 6205492458
ISBN 13: 9786205492451
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Taschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Brain-computer interfaces (BCIs) hold great promise in biomedical engineering, particularly for diagnosing critical diseases. Motor imagery (MI) EEG classification, a key BCI process, faces challenges due to the complexity and non-stationary nature of EEG signals. These signals, recorded via electrodes, are digitized and analyzed using feature extraction techniques like FFT, STFT, CSP, and wavelet transforms, with wavelet transform being the most effective.This study proposes a deep neural network-based classification algorithm with teacher-learning-based optimization for feature refinement. Tested on a standard BCI dataset in MATLAB, the algorithm surpasses Bayesian and ensemble machine learning classifiers, enhancing classification accuracy and BCI system performance. Codice articolo 9786205492451
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Feature Optimization of Motor Imagery EEG Classification using ML
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LAP LAMBERT Academic Publishing, 2024
ISBN 10: 6205492458
ISBN 13: 9786205492451
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Feature Optimization of Motor Imagery EEG Classification using ML (Paperback)
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Paperback. Condizione: new. Paperback. Brain-computer interfaces (BCIs) hold great promise in biomedical engineering, particularly for diagnosing critical diseases. Motor imagery (MI) EEG classification, a key BCI process, faces challenges due to the complexity and non-stationary nature of EEG signals. These signals, recorded via electrodes, are digitized and analyzed using feature extraction techniques like FFT, STFT, CSP, and wavelet transforms, with wavelet transform being the most effective.This study proposes a deep neural network-based classification algorithm with teacher-learning-based optimization for feature refinement. Tested on a standard BCI dataset in MATLAB, the algorithm surpasses Bayesian and ensemble machine learning classifiers, enhancing classification accuracy and BCI system performance. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability. Codice articolo 9786205492451
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Feature Optimization of Motor Imagery EEG Classification using ML
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LAP LAMBERT Academic Publishing, 2024
ISBN 10: 6205492458
ISBN 13: 9786205492451
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Feature Optimization of Motor Imagery EEG Classification using ML
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ISBN 13: 9786205492451
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