  The perception of color is an important cognitive feature of the human brain.
The variety of colors that impinge upon the human eye can trigger changes in
brain activity which can be captured using electroencephalography (EEG). In
this work, we have designed a multiclass classification model to detect the
primary colors from the features of raw EEG signals. In contrast to previous
research, our method employs spectral power features, statistical features as
well as correlation features from the signal band power obtained from
continuous Morlet wavelet transform instead of raw EEG, for the classification
task. We have applied dimensionality reduction techniques such as Forward
Feature Selection and Stacked Autoencoders to reduce the dimension of data
eventually increasing the model's efficiency. Our proposed methodology using
Forward Selection and Random Forest Classifier gave the best overall accuracy
of 80.6\% for intra-subject classification. Our approach shows promise in
developing techniques for cognitive tasks using color cues such as controlling
Internet of Thing (IoT) devices by looking at primary colors for individuals
with restricted motor abilities.
