Enhancing EEG-Based Emotion Recognition with Fast Online Instance Transfer

The Electroencephalogram (EEG)-based emotion detection relies on extensive training data. Generalization from training to testing is accomplished by collecting enormous labeled samples during training, leading to a time-consuming and laborious calibration procedure. In the last few years, numerous papers have proposed different semi-supervised and transfer learning approaches for reducing the demand for training data. However, most of them are iterative methods and require substantial training time, which is unfeasible in practice. To address this issue, we propose Fast Online Instance Transfer (FOIT) for enhancing the affective brain–computer interface (aBCI). FOIT heuristically selects auxiliary data from historical sessions and (or) other subjects, which are subsequently combined with the training data for supervised training. After that, a multi-classifier ensemble makes the predictions on the test trials. During the training, since FOIT is a one-shot algorithm, it avoids time-consuming iterations that satisfy the demand for fast response of BCIs. Experimental results show that FOIT (˜35 s) significantly decreases the time cost than iterative methods (˜45–900 s). Meanwhile, FOIT still maintains the accuracy, even improves ˜1–14% of accuracy in some settings. Our method provides a straightforward, fast, and practically feasible solution for enhancing the effectiveness of EEG-based emotion recognition, allowing for various choices of classifiers without constraints. The code is available at https://​github.​com/​JC-Journal-Club/​FOIT.