Subject-independent meta-learning framework towards optimal training of EEG-based classifiers

Advances in deep learning have shown great promise towards the application of performing high -accuracy Electroencephalography (EEG) signal classification in a variety of tasks. However, many EEG -based datasets are often plagued by the issue of high inter -subject signal variability. Robust deep learning models are notoriously difficult to train under such scenarios, often leading to subpar or widely varying performance across subjects under the leave -one -subject -out paradigm. Recently, the model agnostic meta -learning framework was introduced as a way to increase the model's ability to generalize towards new tasks. While the original framework focused on task -based meta -learning, this research aims to show that the meta -learning methodology can be modified towards subject -based signal classification while maintaining the same task objectives and achieve state-of-the-art performance. Namely, we propose the novel implementation of a few/zero-shot subject -independent meta -learning framework towards multi -class inner speech and binary class motor imagery classification. Compared to current subject -adaptive methods which utilize large number of labels from the target, the proposed framework shows its effectiveness in training zero -calibration and few -shot models for subject -independent EEG classification. The proposed few/zero-shot subject -independent meta -learning mechanism performs well on both small and large datasets and achieves robust, generalized performance across subjects. The results obtained shows a significant improvement over the current state-ofthe-art, with the binary class motor imagery achieving 88.70% and the accuracy of multi -class inner speech achieving an average of 31.15%. Codes will be made available to public upon publication.