Calibration free meta learning based approach for subject independent EEG emotion recognition

Brain Computer Interfaces (BCI) detect changes in the electrical activity of brain which could be applied in usecases like environmental control, neuro-rehabilitation etc. Prior to the actual usage, the subject has to undergo a lengthy calibration phase and hence prohibits an optimal plug-and-play experience. To quantify the minimum number of samples required for calibration, we propose a few-shot adaptation to the task of recognizing emotion from Electroencephalography (EEG) signals, without requiring any fine-tuning of the pre-trained classification models for every user. Our experiments illustrate the usefulness of various sampling strategies based on the presence or absence of subject dependent and subject independent reference samples during training. In comparison with the existing state-of-the-art model, which is trained in a supervised manner, our approach with only 20 reference samples from subjects under consideration (unseen during training) shows an absolute improvement of 8.56% and 7.53% in accuracy on emotion classification in valence and arousal space, respectively, without any re-training using the samples from the unseen subjects. Moreover, when tested in a zero calibration setup (when reference samples are taken from subjects other than the subject under consideration), our system improves the accuracy over the supervised model by 2.02% and 0.61% for emotion classification in valence and arousal space, respectively.

Automated summary

The proposed few-shot adaptation method for emotion recognition using EEG signals significantly improves accuracy without requiring fine-tuning of pre-trained models for each user. The approach achieves substantial improvement in emotion classification accuracy and outperforms a supervised model, even in zero calibration setup.