eRAD-Fe: Emotion Recognition-Assisted Deep Learning Framework

Abstract

With recent advancements in artificial intelligence technologies and human-computer interaction, strategies to identify the inner emotional states of humans through physiological signals such as electroencephalography (EEG) have been actively investigated and applied in various fields. Thus, there is an increasing demand for emotion analysis and recognition via EEG signals in real time. In this article, we proposed a new framework, emotion recognition-assisted deep learning framework from eeg signal (eRAD-Fe), to achieve the best recognition result from EEG signals. eRAD-Fe integrates three aspects by exploiting sliding-window segmentation method to enlarge the size of the training dataset, configuring the energy threshold-based multiclass common spatial patterns to extract the prominent features, and improving emotional state recognition performance based on a long short-term memory model. With our proposed recognitionassisted framework, the emotional classification accuracies were 82%, 72%, and 81% on three publicly available EEG datasets, such as SEED, DEAP, and DREAMER, respectively.