Paper Title
EEG-Based Speech Emotion Recognition with GANS and DRL

Abstract
Emotion recognition is an important component of affective computing, human-computer interaction, and mental health analysis. Traditional approaches make use either of audio signals or electroencephalography (EEG), each of which has their inherent limitations related to noise, availability and intrusiveness. This research suggests a hybrid and extensible framework between such modalities by having the EEG signals synthesized directly from audio features and making use of the synthetic EEG and the audio representations in the classification of emotions. The proposed system receives high-dimensional features of an acoustic signal from speech and uses a progressive conditionally driven generative neural network for generating corresponding EEG- like representations. These synthetic EEG signals are denoise and analyzed using a variety of deep learning classifiers i.e Long Short-Term Memory (LSTM) or Gated Recurrent Unit (GRU) and a LSTM-based deep Q-network classifiers forming an ensemble EEG-based emotion recognition pipeline. In parallel to this, a deep learning model based on audio features is trained which directly predicts the emotional state from speech features, allowing to achieve robust and real-time inference. The implementation of the architecture is unifying the deployment-ready framework that combines Py Torch and Tensor Flow models in an interactive application environment. Experimental Observations Establishment of Feasibility of Audio Conditioned EEG Synthesis and the Complementary Strengths of EEG Based and Audio Based Emotion Recognition. The proposed approach is a scalable basis for multimodal affective computing, especially for situations where direct EEG acquisition will not be feasible. Keywords - Emotion recognition, Affective computing, Audio based emotion analysis, Synthetic EEG generation, Generative adversarial network, LSTM, GRU, Multimodal deep learning, Speech emotion recognition, EEG signal modeling