Data Augmentation For Ambulatory Eeg Based Cognitive State Taxonomy System With Rnn-Lstm

Emotion detection is an important step for recognizing a person's mental state. A physiological signal, Electroencephalogram (EEG) is analyzed to detect human emotion with promising results. The cost of information gathering and lack of number of participants incur a limitation on the size of EEG data set. The deficiency in acquired EEG data set makes it difficult to estimate mental states with deep learning models as it requires a larger size of the training data set. In this paper, we propose a novel data augmentation method to address challenges due to scarcity of EEG data for training deep learning models such as Recurrent Neural Network - Long Short Term Memory (RNN-LSTM). To find the performance of mental state estimator such models are applied before and after proposed data augmentation. Experimental results demonstrate that data augmentation improves the performance of mental state estimator with an accuracy of 98%.