Interactive EEG Emotion Recognition with Incremental Gaussian Processes.

Interactivity is crucial for enabling models to adjust and optimize based on user feedback, thereby enhancing overall performance. However, existing electroencephalogram (EEG)-based emotion recognition models rely on static training paradigms, lack interactivity, and struggle to effectively handle uncertainty in predictions. To address this issue, we propose a novel paradigm for interactive emotion recognition based on incremental Gaussian processes (GP). Unlike existing methods, our approach introduces an expert interaction mechanism to correct samples with high predictive uncertainty and incrementally update the model accordingly, thereby optimizing its performance. First, we model the emotion recognition task as a GP-based framework, utilizing the variance of the GP to quantify the model's uncertainty, thereby guiding experts in targeted interactions. Second, within the GP framework, we propose a novel incremental update strategy that allows the GP to incrementally update prediction results and uncertainties based only on new data obtained through expert interactions, without reprocessing all existing data. This effectively overcomes the shortcomings of traditional GP in updating efficiency. Third, to address the high computational complexity of GP, we use a sparse approximation strategy, selecting inducing points and performing variational inference to efficiently approximate the GP posterior, thereby reducing computational complexity. Subject-dependent and subject-independent experiments conducted on the DEAP and DREAMER datasets demonstrate that the proposed method exhibits significant advantages over state-of-the-art (SOTA) methods. In subject-dependent experiments, our method achieved the highest improvement (1.73%) in the Dominance dimension on the DREAMER dataset. In subject-independent experiments, it attained the largest performance improvement (2.96%) in the Arousal dimension on the DEAP dataset. These results further validate the proposed method's effectiveness.