Geodesic Mean Threshold Scheme on Riemannian Manifold for EEG Signal Classification

Abstract

Electroencephalogram (EEG) signal classification for neural activity monitoring and cognitive state assessment using Machine Learning (ML) models is a well-established strategy. The usage of ML models with traditional Euclidean vector-based feature extraction is efficient and effective if the behavior and distribution of the activity are normal. In specific abnormal behavioral disorders like depression, schizophrenia, and bipolar disorder, a dedicated non-euclidean manifold implementation can be the coherent strategy to handle the non-stationarity and underlying complexity. Riemannian manifold and Tangent space mapping is one of the efficient differentiable non-euclidean manifold techniques in analyzing higher dimensional data like EEG signals. To introspect the potential of Riemannian manifold learning in understanding neural behavior, this paper introduced the novel Geodesic Mean Threshold Channel (GMTC) selection scheme. The proposed GMTC framework has been validated with publicly available depression and schizophrenia datasets, which are collected with different specifications and strategies. Performance metrics of the proposed methodology have shown a 7% gain in accuracy with stabilized standard deviation.