Joint SCSP-LROM: A novel approach to detect Cerebrovascular Anomalies from EEG signals

Electroencephalography (EEG) gained popularity over similar modalities like Functional Magnetic Resonance Imaging (fMRI) or Functional Near-Infrared Spectroscopy (fNRIS), for being simplistic and non-invasive. One of the biggest challenges of any Brain Computer Interfacing (BCI) techniques, is recovering maximum information from minimal input channels for realistic predictions. To choose EEG channels with highest accuracy, a novel concept of introducing sparsity in a Convolutional Neural Network (CNN) induced modified Common Spatial Pattern (CSP) algorithm is introduced in this paper. This approach helps developing optimized confusion matrices, which can extensively label the feature map in significantly lower number of iterations, to predict trends of growth of symptoms. The concept of compressed sensing is utilized to develop an optimization model for recovering the cosparse signal and retaining maximum information. The state-of-the-art Joint Sparsity Induced Modified Common Spatial Pattern Algorithm and Low Rank Optimization Model (SCSP-LROM) can detect the stage and extent of growth of malignant cells, hemorrhages and lesions.