Uncertainty-Guided Incremental Interactive EEG Emotion Recognition with Gaussian Process

Interactivity is crucial for enabling models to adjust and optimize based on user feedback, thereby enhancing overall performance. However, existing methods for emotion recognition lack interactivity, failing to allow experts to provide feedback for interaction and updating prediction results, thus limiting the improvement of recognition accuracy. To address this problem, we propose a new paradigm for uncertainty-guided incremental interactive emotion recognition based on Gaussian process (GP). This paradigm involves interaction by having experts provide correction labels for samples with high prediction uncertainty, enabling incremental updates. Firstly, we model the emotion recognition task as a GP-based framework, utilizing the variance of GP to quantify the model’s uncertainty, thereby guiding experts in targeted interactions. Secondly, within the GP framework, we use an incremental update strategy to optimize prediction results. This method acquires new data through expert interaction, allowing for incremental updates based on new data without reprocessing all existing data. Thirdly, to tackle the computational complexity of inverting the covariance matrix in GP, we use a sparse approximation strategy, selecting inducing points and performing variational inference to efficiently approximate the GP posterior, thereby reducing computational complexity. Our method has been validated through extensive experiments on the DEAP and DREAMER datasets, demonstrating superior recognition performance compared to state-of-the-art methods.