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

Color-based classification of EEG Signals for people with the severe locomotive disorder