  It has always been a big challenge to identify subtle changes in
Electroencephalogram (EEG) signals. Minor differences often lead to vital
decisions, for example, which grade a certain tumour belong to or whether a
haemorrhage can result in benign blood clots or cancerous ones. In recent
studies on brain computer interfaces (BCIs), one of the biggest challenges is
recovering maximum information for realistic predictions. In order to choose
EEG channels with highest accuracy, a novel notion of including sparsity in a
modified common spatial pattern (CSP) algorithm is introduced here. Being
influenced by the existing concept of compressed sensing, an optimization model
is also developed alongside to recover the cosparse signal and retain maximum
information. The state-of-the-art Joint Sparsity Induced Modified Common
Spatial Pattern Algorithm and Low Rank Optimization Model (SCSP-LROM) developed
here is capable of identifying and describing tumours and lesions in great
detail at an overall accuracy of 96.3%.
