COMPUTATIONAL ANALYSIS OF DEEP LEARNING MODELS FOR CLASSIFICATION OF EEG DATA RELATED TO COGNITIVE IMPAIRMENT
Deep Learning models are proven to be computationally extensive and produce good analytical results. EEG (electroencephalography) is the technique by which the electrical activities of the brain can be recorded which when studied properly can give useful insights and can be used to diagnose various neurological diseases. In this paper we have systematically reviewed the literature wherein different deep learning strategies like Convolutional Neural Network, Recurrent Neural Network, Multi layered perceptron, and Deep belief networks are used to classify the EEG signals. This review paper has mainly covered the prominent research work done in the classification of EEG for various purposes like the classification of motor Imagery for BCI, Classifying EEG signals for early detection of (MCI)Mild Cognitive Impairment, AD (Alzheimer Disease) Seizure Epilepsy etc. The comparative analysis of these algorithms and their research work based on their pertinent features is done along with their associated outcomes which are presented in the tabulated format. We showed why EEG is better and efficient than MRI the advantages of EEG over MRI. The field has diverse research records so far but it has to be studied further to make most out of the capabilities of deep learning algorithms. In our study, CNN was found to yield the best results for the classification as compared to other deep learning strategies. Further, we implemented the basic models of CNN, RNN, MLP for the binary classification of a seizure data and did a comparative analysis of their performances. The results showed that 1 D CNN (Model 3) gave the highest accuracy of 98.5% among the other two models. This paper will be very encouraging and helpful to those who are willing to do research in this field wish to look upon the previous research in this field.