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IEEE Final Year Project Topics for CSE

Base Paper Title

Accurate EEG-based Emotion Recognition onCombined Features Using Deep ConvolutionalNeural Networks

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IEEE Project Abstract

In order to improve the accuracy of emotional recognition by end-to-end automatic learningof emotional features in spatial and temporal dimensions of electroencephalogram (EEG), an EEG emotionalfeature learning and classification method using deep Convolution Neural Network (CNN) was proposedbased on temporal features, frequential features and their combinations of EEG signals in DEAP dataset. Theshallow machine learning models including Bagging tree (BT), Support vector machine (SVM), lineardiscriminant analysis (LDA) and Bayesian linear discriminant analysis (BLDA) models and deep CNNmodels were used to make emotional binary classification experiments on DEAP datasets in valence andarousal dimensions. The experimental results showed that the deep CNN models which require no featureengineering achieved the best recognition performance on temporal and frequency combined features in bothvalence and arousal dimensions, which is 3.58% higher than the performance of the best traditional BTclassifier in valence dimension and 3.29% higher than that of BT classifier in arousal dimension.

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