Enhancing Text Using Emotion Detected from EEG Signals

Often people might not be able to express themselves properly on social media, like not being able to think of appropriate words representative of their emotional state. In this paper, we propose an end to end system which aims to enhance user-input sentence according to his/her current emotional state. It works by a) detecting the emotion of the user and b) enhancing the input sentence by inserting emotive words to make the sentence more representative of the emotional state of the user. The emotional state of the user is recognized by analyzing the Electroencephalogram (EEG) signals from the brain. For text enhancement, we modify the words corresponding to the detected emotion using correlation finder scheme. Next, the verification of the sentence correctness has been performed using Long Short Term Memory (LSTM) Networks based Language Modeling framework. An accuracy of 74.95% has been recorded for the classification of five emotional states in a dataset of 25 participants using EEG signals. Similarly, promising results have been obtained for the task text enhancement and overall end-to-end system. To the best of our knowledge, this work is the first of its kind to enhance text according to the emotional state detected by EEG brainwaves. The system also releases an individual from thinking and typing words, which might be a complicated procedure sometimes.