EEG Signal Processing and Supervised Machine Learning to Early Diagnose Alzheimer’s Disease

Advanced Signal Processing and Machine Learning Approaches for EEG

Electroencephalography (EEG) offers a non-invasive brain-imaging technology with potential to extract user intent from brain signals. This can offer a potential method for dispersed soldiers to communicate silently with one another. The usual interface for acquiring EEG signals may house 128 or more electrodes. Each EEG signal may be sampled at KHz sampling rates and may last for a few seconds....

Machine Learning for Signal Processing

The 25th MLSP workshop in the series of workshops organized by the IEEE Signal Processing Society MLSP Technical Committee will present the most recent and exciting advances in machine learning for signal processing through keynote talks, tutorials, as well as special and regular single-track sessions. Prospective authors are invited to submit papers on relevant algorithms and applications incl...

P81: Detection of Epileptic Seizures Using EEG Signal Processing

Epilepsy is the most common brain diseases that cause many problems in the daily life of the patient. In most attempts to automatic detection, the attack used an EEG. In this paper, The complete data set consists of five sets recorded from normal and epileptic patients. Each set containing 100 single-channel EEG segments. Here we used first and last sets (A and E). Set A consisted of segments r...

Financial Signal Processing and Machine Learning

Signal Processing and Machine Learning for Single-trial Analysis of Simultaneously Acquired EEG and fMRI

The simultaneous acquisition of electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) is a potentially powerful multimodal imaging technique for measuring the functional activity of the human brain. Given that EEG measures the electrical activity of neural populations while fMRI measures hemodynamics via a blood oxygenationlevel–dependent (BOLD) signal related to neurona...