Self-Powered Multimodal Emotion Recognition System

Project Introduction

Developed a wearable device integrating voice and EEG signal analysis for emotion detection.

Personal Contributions

• Completed the model architecture, designed a bimodal binding mechanism to process voice signals, combined modality-invariant and modality-specific features to input into the Transformer; designed a wavelet-transform-based preprocessing method to convert EEG signals into time-frequency image blocks to input into the Vision Transformer.
• Prepared flexible organic photovoltaic devices using PM6:Y6 = 1:1.2 as the organic active layer and ITO-PET as the flexible cathode, achieving a photoelectric conversion efficiency of over 12.39%.

Achievements

• First-author paper accepted at ICDT 2025 (Oral).
• First-author paper accepted at ICASS 2026 (Oral).
• National First Prize in IoT Design Competition 2024.
• National Innovation Training Program (Excellent Rating).
• 4 National Invention Patents (Application Stage).