Extraction of inherent frequency components of multiway EEG data using two-stage neural canonical correlation analysis

This paper presents an algorithm for extracting underlying frequency components of massive Electroencephalogram (EEG) data. Frequency components of these data play a vital role to realize brain-body condition. Usually, a huge amount of time and specially built computers are essential to process these EEG data having different subjects. It also restricts to visualize inherent frequency of EEG for a general practitioner. An algorithm is developed using two-stage cascaded architecture of canonical correlation analysis with neural network named multiway neural canonical correlation analysis (MNCCA) to address three major challenges for extracting frequency components from EEG data, such as: (a) It processes multiway data which are feed sequentially into neural network, rather than feeding whole data at a time, (b) It uses the conventional personal computer instead of special computer built for such application, (c) It spends very short time for a moderate data set consisting of several ways (time, trials and channels). The experimental results are obtained with three different kinds of networks having linear, nonlinear and nonlinear feedback structures. The inherent dominant frequency of 1 Hz having a quite resemblance with EEG landscape has been found. This provides a great opportunity in analyzing brain-body function.