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Research Article Open Access
Understanding the cognitive states of human brain and hence deduce the thinking patterns of human beings has been an area of greater interest since the development of neuro-imaging technologies. With fMR images the accuracy of the features extracted provide us with a clear understanding of the brain state. Currently, numerous Machine Learning approaches, for fMRI feature extraction, are available, such as color, histogram, texture features and Machine Learning Classifiers are available, such as Gaussian Naïve Bayes (GNB),k-Nearest Neighbor (kNN), Generalized Linear Models (GLM), Artificial Neural Networks classifiers such as Self Organizing Maps (SOMs) and Kernel based approaches. In this paper, we adapted a novel technique that uses Coiflet wavelet transform for feature extraction and a Support Vector Classifier for classifying various brain states. The main objective of this work is to infer the cognitive state of the subject under study by comparing his/her fMR image with stored classes of the brain states from the training data set.The results obtained were 90% accurate. Coiflet transform plays a vital in yielding accurate results for feature extraction. The literature shows that Support Vector Machine Classifiers are better than GNB, k-NN and Artificial Neural Network classifiers.