MEG data classification for healthy and epileptic subjects using linear discriminant analysis

Abstract

Electroencephalogram (EEG) is the most commonly used clinical tool for the early diagnosis of epilepsy. However, with the recent advances in the magnetoencephalography (MEG) technology, a new source of information for the analysis of brain signals has been established. Epileptologists often spend considerable amount of time to review MEG recordings to determine whether or not a particular subject can be classified as an epileptic patient. This paper proposes a new algorithm for automatic classification of MEG data into two classes: data that belongs to healthy subjects and data that belongs to epileptic subjects. The classifier makes use of linear discriminant analysis (LDA) and considers features extracted from the signals of eight regions in the brain. The effectiveness of proposed classifier has been tested using real MEG data obtained from 15 healthy subjects and 18 epilepsy patients. The results obtained show good promise, which make the proposed classifier a valuable tool for analyzing brain signals in the initial assessment phases of subjects under epileptic symptoms.