Advanced Multimodal Emotion Recognition Using Integrative Analysis of Multilingual Text, EEG, Voice Signals, and Facial Analysis

Emotion recognition with the help of AI is critically important for improving the processes of human computer interaction, and can be effectively implemented in healthcare, robotics, and mental health analysis. Systems that adopt inputs in a single mode only, be it text, voice, or facial expressions all have their issues such as low accuracy, easiness to fake and the inability to comprehend the context. These are overcome by a multimodal method that uses textual, voice, facial and EEG data to be more reliable or accurate Multilingual text preprocessing hence translates the inputs from other regions to English for the ML/DL algorithms to classify it as either having positive, neutral or negative feelings. Voice converter translates into feelings with pitch and rhythm with the help of DL models and face using DeepFace even in brightness variations. The Electroencephalogram or EEG signals are obtained at different times and those with higher layers of neural works are analyzed in real time. Fusion strategies also help in resolving compatibility issues and conflict between the modalities and increases the security against any attempt of adulteration. Through this approach, it is possible to detect accurate real-time emotion with aims of screening of mental health, enhance HCI and designs of empathetic robots. Future work therefore seeks to enhance integration methods, extend its use and investigate other approaches for a higher level of enhancement.