Items related to Feature Optimization of Motor Imagery EEG Classification...
Synopsis
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.
"synopsis" may belong to another edition of this title.
Search results for Feature Optimization of Motor Imagery EEG Classification...
Stock Image
Feature Optimization of Motor Imagery EEG Classification using ML
Published by
LAP LAMBERT Academic Publishing, 2024
ISBN 10: 6205492458
ISBN 13: 9786205492451
New
Softcover
Seller: Ria Christie Collections, Uxbridge, United Kingdom
Seller rating 5 out of 5 stars
Condition: New. In. Seller Inventory # ria9786205492451_new
Quantity: Over 20 available
Stock Image
Feature Optimization of Motor Imagery EEG Classification using ML
Published by
LAP LAMBERT Academic Publishing, 2024
ISBN 10: 6205492458
ISBN 13: 9786205492451
New
Softcover
Seller: California Books, Miami, FL, U.S.A.
Seller rating 5 out of 5 stars
Condition: New. Seller Inventory # I-9786205492451
Quantity: Over 20 available
Stock Image
Feature Optimization of Motor Imagery EEG Classification using ML (Paperback)
Published by
LAP Lambert Academic Publishing, 2024
ISBN 10: 6205492458
ISBN 13: 9786205492451
New
Paperback
Seller: CitiRetail, Stevenage, United Kingdom
Seller rating 5 out of 5 stars
Paperback. Condition: 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 UK warehouse or from our Australian or US warehouses, depending on stock availability. Seller Inventory # 9786205492451
Quantity: 1 available
Stock Image
Feature Optimization of Motor Imagery EEG Classification using ML
Published by
LAP LAMBERT Academic Publishing Dez 2024, 2024
ISBN 10: 6205492458
ISBN 13: 9786205492451
New
Taschenbuch
Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germany
Seller rating 5 out of 5 stars
Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware 72 pp. Englisch. Seller Inventory # 9786205492451
Quantity: 2 available
Seller Image
Feature Optimization of Motor Imagery EEG Classification using ML
Published by
LAP LAMBERT Academic Publishing, 2024
ISBN 10: 6205492458
ISBN 13: 9786205492451
New
Taschenbuch
Seller: AHA-BUCH GmbH, Einbeck, Germany
Seller rating 5 out of 5 stars
Taschenbuch. Condition: 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. Seller Inventory # 9786205492451
Quantity: 1 available
Seller Image
Feature Optimization of Motor Imagery EEG Classification using ML
Published by
LAP LAMBERT Academic Publishing, 2024
ISBN 10: 6205492458
ISBN 13: 9786205492451
New
Softcover
Seller: moluna, Greven, Germany
Seller rating 5 out of 5 stars
Condition: 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. Seller Inventory # 2098218191
Quantity: Over 20 available
Stock Image
Feature Optimization of Motor Imagery EEG Classification using ML
Published by
LAP LAMBERT Academic Publishing, 2024
ISBN 10: 6205492458
ISBN 13: 9786205492451
New
Softcover
Seller: Majestic Books, Hounslow, United Kingdom
Seller rating 5 out of 5 stars
Condition: New. Print on Demand. Seller Inventory # 408981965
Quantity: 4 available
Stock Image
Feature Optimization of Motor Imagery EEG Classification using ML
Published by
LAP LAMBERT Academic Publishing, 2024
ISBN 10: 6205492458
ISBN 13: 9786205492451
New
Softcover
Seller: Books Puddle, New York, NY, U.S.A.
Seller rating 4 out of 5 stars
Condition: New. Seller Inventory # 26404172306
Quantity: 4 available
Stock Image
Feature Optimization of Motor Imagery EEG Classification using ML
Published by
LAP LAMBERT Academic Publishing, 2024
ISBN 10: 6205492458
ISBN 13: 9786205492451
New
Softcover
Seller: Biblios, Frankfurt am main, HESSE, Germany
Seller rating 5 out of 5 stars
Condition: New. PRINT ON DEMAND. Seller Inventory # 18404172312
Quantity: 4 available
Seller Image
Feature Optimization of Motor Imagery EEG Classification using ML
Published by
LAP LAMBERT Academic Publishing Dez 2024, 2024
ISBN 10: 6205492458
ISBN 13: 9786205492451
New
Taschenbuch
Seller: buchversandmimpf2000, Emtmannsberg, BAYE, Germany
Seller rating 5 out of 5 stars
Taschenbuch. Condition: 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. Seller Inventory # 9786205492451
Quantity: 1 available