EEG Signal Analysis and Classification
Springer International Publishing (Verlag)
978-3-319-47652-0 (ISBN)
Common signal processing methodologies include wavelet transformation and Fourier transformation, but these methods are not capable of managing the size of EEG data. Addressing the issue, this book examines new EEG signal analysis approaches with a combination of statistical techniques (e.g. random sampling, optimum allocation) and machine learning methods. The developed methods provide better results than the existing methods. The book also offers applications of the developedmethodologies that have been tested on several real-time benchmark databases.
This book concludes with thoughts on the future of the field and anticipated research challenges. It gives new direction to the field of analysis and classification of EEG signals through these more efficient methodologies. Researchers and experts will benefit from its suggested improvements to the current computer-aided based diagnostic systems for the precise analysis and management of EEG signals.
Electroencephalogram (EEG) and its background.- Significance of EEG signals in medical and health research.- Objectives and structures of the book.- Random sampling in the detection of epileptic EEG signals.- A novel clustering technique for the detection of epileptic seizures.- A statistical framework for classifying epileptic seizure from multi-category EEG signals.- Injecting principal component analysis with the OA scheme in the epileptic EEG signal classification.- Cross-correlation aided logistic regression model for the identification of motor imagery EEG signals in BCI applications.- Modified CC-LR Algorithm for identification of MI based EEG signals.- Improving prospective performance in the MI recognition: LS-SVM with tuning hyper parameters.- Comparative study: Motor area EEG and All-channels EEG.- Optimum allocation aided Naive Bayes based learning process for the detection of MI tasks.- Summary discussions on the methods, future directions and conclusions.
| Erscheinungsdatum | 24.01.2017 |
|---|---|
| Reihe/Serie | Health Information Science |
| Zusatzinfo | XIII, 256 p. 96 illus. |
| Verlagsort | Cham |
| Sprache | englisch |
| Maße | 155 x 235 mm |
| Themenwelt | Technik ► Elektrotechnik / Energietechnik |
| Schlagworte | Artificial Intelligence • artificial intelligence (incl. robotics) • biomedical engineering • Brain computer interface (BCI) • classification • Clustering technique (CT) • Computer Science • computer vision • Cross-correlation (CC) technique • electroencephalogram (EEG) • Engineering: general • Epileptic seizure • feature extraction • health and safety aspects of IT • Health Informatics • Image Processing • image processing and computer vision • Imaging systems and technology • Information Retrieval • information systems applications (incl. internet) • Internet Searching • Kernal logistic regression (KLR) • k-NN • Least square supper vector machine (LS-SVM) • Logistic regression (LR) • Motor imagery (MI) • Multinomial logistic regression with a ridge estim • Multinomial logistic regression with a ridge estimator • Naive Bayes method • Optimum allocation sampling • Optimum allocation technique • Robotics • Signal, Image and Speech Processing • Signal Processing • Simple random sampling (SRS) • Support Vector Machine (SVM) |
| ISBN-10 | 3-319-47652-1 / 3319476521 |
| ISBN-13 | 978-3-319-47652-0 / 9783319476520 |
| Zustand | Neuware |
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