Resampling eeg trial data

A system, comprising:

• one or more processors;
• one or more tangible, non-transitory media operably connectable to the one or more processors and storing instructions that, when executed, cause the one or more processors to perform operations comprising:
• identifying training data comprising a plurality of embeddings, wherein each embedding represents EEG trial data for a particular individual;
• selecting, from the training data and for inclusion in one or more subsets of the training data, particular embeddings based on a random probability distribution associated with a weighting factor assigned to each embedding;
• generating an augmented set of training data by combining two or more subsets, wherein the augmented set of training data comprises more embeddings than the training data;
• and
• providing the augmented set of training data as training input to a machine learning model.

A non-transitory computer readable storage medium storing instructions that, when executed by at least one processor, cause the at least one processor to perform operations comprising:

• identifying training data comprising a plurality of embeddings, wherein each embedding represents EEG trial data for a particular individual;
• selecting, from the training data and for inclusion in one or more subsets of the training data, particular embeddings based on a random probability distribution associated with a weighting factor assigned to each embedding;
• generating an augmented set of training data by combining two or more subsets, wherein the augmented set of training data comprises more embeddings than the training data;
• and
• providing the augmented set of training data as training input to a machine learning model.