AI-based Emotion Recognition System with Tensor Decomposition Optimized Pre-processing

In this paper, we proposed to optimize a CNN-based emotion recognition system using EEG signal input by integrating a tensor decomposition pre-processing engine. Although CNNs have been proven to be a strong method to solve the classification of emotion accurately, the amount of required computation and memory remains a challenge for edge AI implementation. The tensor decomposition pre-processing engine proposed in this work efficiently identified the key hidden core tensors containing the most contributing features to the classification of emotion while removing the unnecessary redundant data affecting the amount of required computation and memory usage. This pre-processing engine effectively speeds up the AI acceleration by a factor of 3 and reduced the memory usage by 35% while keeping comparable classification performances with an average accuracy slightly higher by 4%.