An EEG Analysis Framework for Brain Disorder Classification Using Convolved Connectivity Features

Rajkumar Saini, Richa Upadhyay, Vibha Gupta, Prakash Chandra Chhipa, Sumit Rakesh, Hamam Mokayed, Kanjar De, Foteini Simistira Liwicki, Debashis Das Chakladar

Abstract: Electroencephalography (EEG) is a fundamental tool in the non-invasive evaluation of brain activity, providing insights into the intricate dynamics at play within neurode-generative disorders. Conventional methodologies often lack in effectively capturing the temporal and intricate intra- and inter-channel dynamics, leading to diminished predictive accuracy. To address this problem, we present an innovative framework that effectively captures temporal along with intra- and inter-channel dynamics for EEG analysis aimed at predicting neu-rodegenerative disorders, explicitly targeting Alzheimer's and dementia. The proposed method involves constructing aggregated recurrence matrices from EEG channels followed by kernel formation and convolution operation, effectively encapsulating intra- and inter-channel spatiotemporal patterns, thereby achieving a more comprehensive representation of neural dynamics. The proposed approach was validated using public datasets, revealing competitive performance. Implementation details with codes will be accessible on GitHub.