ABSTRACT
The combination of machine learning (ML) and neuromarkers/biomarkers extraction for the improved prognosis of Alzheimer’s disease (AD) has received increasing attention in the community. This chapter is devoted to reviewing the recently emerged ML algorithms for dementia diagnosis using electroencephalography (EEG) and magnetoencephalography (MEG), including the detection for AD and mild cognitive impairment (MCI). In particular, the ML approaches are systematically categorized into sensor-level and source-level analysis. A few recent works on the diagnosis using EEG and MEG for AD and MCI detection is discussed. A number of influential factors have also been raised and suggested in the discussion section, for careful considerations while evaluating the ML-based diagnosis systems in real-world scenarios.
