Predicting word from brain activity using joint sparse embedding with domain adaptation

Abstract

In the proposed work machine learning algorithm is applied on Functional Magnetic Resonance Imaging (fMRI) data to analyze the human brain activity and then predicting the word that the subject was thinking. The algorithm that can learn to identify and track the cognitive processes and gives rise to predict the word from observed fMRI data is developed. The major problem here is that we have limited data in very high dimensional feature space. Thereby, making the model susceptible to overfit the data. Also, the data is highly noisy through most of the dimensions, leaving only a few features that are discriminative. Due to high noise domain shift problem is very likely to occur. Most of the previous approach focused only on feature selection and learning the embedding space. Here our main objective is to learn the robust embedding space and handling the domain shift problem [11] in an efficient way. Unlike the previous approach instead of learning the dictionary that projects the visual space to the word embedding space, we are using the joint dictionary learning approach based on the matrix factorization. Our experiment shows that the proposed approach based on the joint dictionary learning and domain adaptation method has the significant advantage over the previous approaches.