  In this work we propose a novel symmetric square matrix representation of one
or more digital signals of finite equal length. For appropriate window length
and sliding paradigm this matrix contains useful information about the signals
in a two dimensional image form. Then this representation can be treated either
as an algebraic matrix or as a geometric image. We have shown applications of
both on human multichannel intracranial electroencephalogram (iEEG). In the
first application we have shown that for certain patients the highest
eigenvalue of the matrix obtained from the epileptic focal channels goes up
during a seizure. The focus of this paper is on an application of the second
concept, by which we have come up with an automatic seizure detection algorithm
on a publicly available benchmark data. Except for delay in detection in all
other aspects the new algorithm outperformed the detection performance based on
a support vector machine based algorithm. We have also indicated how this
sparse random matrix representation of brain electrical signals can encode the
activities of the brain.
