  Electroencephalogram (EEG) is a very promising and widely implemented
procedure to study brain signals and activities by amplifying and measuring the
post-synaptical potential arising from electrical impulses produced by neurons
and detected by specialized electrodes attached to specific points in the
scalp. It can be studied for detecting brain abnormalities, headaches, and
other conditions. However, there are limited studies performed to establish a
smart decision-making model to identify EEG's relation with the mood of the
subject. In this experiment, EEG signals of 28 healthy human subjects have been
observed with consent and attempts have been made to study and recognise moods.
Savitzky-Golay band-pass filtering and Independent Component Analysis have been
used for data filtration.Different neural network algorithms have been
implemented to analyze and classify the EEG data based on the mood of the
subject. The model is further optimised by the usage of Blackman window-based
Fourier Transformation and extracting the most significant frequencies for each
electrode. Using these techniques, up to 96.01% detection accuracy has been
obtained.
