Artificial Intelligence Enabled Signal Processing based Models for Neural Information Processing

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.

? 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 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.

The book presents automated methods for the detection of neurological disorders, and other applications such as cognitive task recognition, and brain-computer interface. It highlights the latest machine learning and deep learning methods for neural signal processing and discusses mathematical details for the signal processing and machine learning algorithms applied for electroencephalography data analysis. It further presents the automated detection of Alzheimer's disease from electroencephalography signals using signal processing and machine learning-based methods.

It is primarily written for senior undergraduates, graduate students, and researchers in the fields of electrical engineering, electronics and communications engineering, and biomedical engineering.