  Motor imagery-based BCI systems have been promising and gaining popularity in
rehabilitation and Activities of daily life(ADL). Despite this, the technology
is still emerging and has not yet been outside the laboratory constraints.
Channel reduction is one contributing avenue to make these systems part of ADL.
Although Motor Imagery classification heavily depends on spatial factors,
single channel-based classification remains an avenue to be explored
thoroughly. Since Fisher's ratio and Pearson Correlation are powerful measures
actively used in the domain, we propose an integrated framework (FRPC
integrated framework) that integrates Fisher's Ratio to select the best channel
and Pearson correlation to select optimal filter banks and extract spectral and
temporal features respectively. The framework is tested for a 2-class motor
imagery classification on 2 open-source datasets and 1 collected dataset and
compared with state-of-art work. Apart from implementing the framework, this
study also explores the most optimal channel among all the subjects and later
explores classes where the single-channel framework is efficient.
