  The neurons in the brain produces electric signals and a collective firing of
these electric signals gives rise to brainwaves. These brainwave signals are
captured using EEG (Electroencephalogram) devices as micro voltages. These
sequence of signals captured by EEG sensors have embedded features in them that
can be used for classification. The signals can be used as an alternative input
for people suffering from severe locomotive disorder.Classification of
different colors can be mapped for many functions like directional movement. In
this paper, raw EEG signals from NeuroSky Mindwave headset (a single electrode
EEG sensor) have been classified with an attention based Deep Learning Network.
Attention based LSTM Networks have been implemented for classification of two
different colors and four different colors. An accuracy of 93.5\% was obtained
for classification of two colors and an accuracy of 65.75\% was obtained for
classifcation of four signals using the mentioned attention based LSTM network.
