Identification of Alzheimer’s disease through Feature Extraction using Latent Factor of Multi-Channel EEG

One of the most prevalent and rapidly spreading neurological disorders in the world is Alzheimer's disease. There are several biomarkers available for the evaluation &growth of Alzheimer's disease, and these tools might be seen as crucial for therapeutic applications. EEG signals produce a potent and reasonably priced tool for the diagnosis of many neurological diseases. In this instance, it is necessary to increase the EEG signals' diagnostic precision. A novel approach for utilising EEG signals to diagnose Alzheimer's disease is suggested in this study, which will improve the diagnostic efficacy of the EEG signals and diagnosis. EEG Spectro-temporal Modulation Energy Based aspects, Spectral Features, Coherence Features, and other aspects of EEG Signals are examined in detail.It may be inferred from the characteristics of the EEG signal that were previously mentioned that it can be useful in the diagnosis of dementia and Alzheimer's disease. The main goal of the suggested strategy is to increase the precision of the patient monitoring system, remove variations in the EEG signal, and increase the precision of the procedure.