Detection of Alzheimer's Disease at Mild Cognitive Impairment Stage using EEG Signals

Alzheimer is a kind of neurological disorder that is difficult to detect at an early stage. Prior detection of Alzheimer's disease by applying EEG signals can improve the condition of patients. For detection of the Alzheimer's disease at Mild Cognitive Impairment (MCI) stage, a set of features from different set of electrodes has been examined. A time domain feature i.e. Pearson correlation coefficient and a feature from frequency domain i.e. coherence has been extracted for normal as well MCI subjects. Interestingly decrease in value of the extracted features has been observed from different pairs of electrodes. Electrode pairs revealing maximum information has also been identified. Power from four frequency bands (theta, alpha, beta and gamma) of each EEG signal have been extracted as these bands represents their own physiological implications in terms of signal interpretation. So, the Alzheimer disease can be identified at MCI stage through features extracted for EEG signals.