M/EEG Task Classification by Means of Brain Dynamic Analysis

This study focuses on a feature extraction algorithm based on the analysis of dynamic spectral components of magnetoencephalography (MEG) and electroencephalography (EEG) data. During its activity, the brain produces electromagnetic fields that can be sensed outside the head by means of appropriate sensors (e.g., SQUIDs in the case of Magnetoencephalography and electrodes in the case of Electroencephalography). These recordings are commonly acquired and processed to obtain estimates of brain activity with high spatial and temporal resolution. When an individual performs a task, brain activity exhibits task-dependent variations in the measured signals. Consequently, in recent years, the scientific community has increasingly focused on the study of brain signals to detect and classify features associated with specific tasks. Within this framework, we propose a novel paradigm whose core consists of a synthetic feature extraction approach based on the time-varying contribution of brain spectral components. These features are subsequently used as inputs to a machine learning classifier for task discrimination. The proposed strategy is designed to provide a novel and lightweight solution for human–machine interaction pipelines.