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Full Description
The book provides details regarding the application of various signal processing and artificial intelligence-based methods for electroencephalography data analysis. It will help readers in understanding the use of electroencephalography signals for different neural information processing and cognitive neuroscience applications. The book:
Covers topics related to the application of signal processing and machine learning-based techniques for the analysis and classification of electroencephalography signals
Presents automated methods for detection of neurological disorders and other applications such as cognitive task recognition, and brain-computer interface
Highlights the latest machine learning and deep learning methods for neural signal processing
Discusses mathematical details for the signal processing and machine learning algorithms applied for electroencephalography data analysis
Showcases the detection of dementia from electroencephalography signals using signal processing and machine learning-based techniques
It is primarily written for senior undergraduates, graduate students, and researchers in the fields of electrical engineering, electronics and communications engineering, and biomedical engineering.
Contents
1. Introduction to Neural Signals and Information Systems. 2. Artificial intelligence (AI)-enabled Signal Processing-based Methods for the Automated Detection of Epileptic Seizures using EEG Signals. 3. Machine Learning or Deep Learning Combined with Signal Processing for the Automated Classification of Sleep Stages using EEG Signals. 4. Classification of Normal and Alcoholic EEG Signals using Signal Processing and Machine Learning Models. 5. Artificial Intelligence-enabled Signal Processing-based Approach for the Automated Detection of Depression using EEG Signals. 6. Detection of Sleep Disorders from EEG Signals using Signal Processing and Machine Learning Techniques. 7. Automated Emotion Recognition from EEG Signals using Signal Processing and Machine Learning Techniques. 8. Signal Processing and Machine Learning-based Automated Approach for the Detection of Parkinson's Disease using EEG Signals. 9. Automated Detection of Alzheimer's Disease from EEG Signal using Signal Processing and Machine Learning-based Methods. 10. Artificial Intelligence-enabled Signal Processing-based Method for Brain-Computer Interface (BCI) Applications using EEG Signals. 11. Detection of Dementia from EEG Signals using Signal Processing and Machine Learning-based Techniques. 12. Signal Processing enabled Machine Learning based Approach for Automated Recognition of Cognitive tasks using EEG Signals.
