EEG Signal Analysis and Classification Techniques and Applications /
This book presents advanced methodologies in two areas related to electroencephalogram (EEG) signals: detection of epileptic seizures and identification of mental states in brain computer interface (BCI) systems. The proposed methods enable the extraction of this vital information from EEG signals i...
| Main Authors: | , , |
|---|---|
| Corporate Author: | |
| Language: | English |
| Published: |
Cham :
Springer International Publishing : Imprint: Springer,
2016.
|
| Edition: | 1st ed. 2016. |
| Series: | Health Information Science,
|
| Subjects: | |
| Online Access: | https://doi.org/10.1007/978-3-319-47653-7 |
Table of Contents:
- 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.