motor imagery

Integrating EEG and MEG Signals to Improve Motor Imagery Classification in Brain-Computer Interface

We adopted a fusion approach that combines features from simultaneously recorded electroencephalogram (EEG) and magnetoencephalogram (MEG) signals to improve classification performances in motor imagery-based brain-computer interfaces (BCIs). We …

Characterization of Mental States through Node Connectivity between Brain Signals

Discriminating mental states from brain signals is crucial for many applications in cognitive and clinical neuroscience. Most of the studies relied on the feature extraction from the activity of single brain areas, thus neglecting the potential …