Transfer Learning for Non-Invasive BCI EEG Brainwave Decoding

Abstract

Brain-computer interfaces (BCIs) represent a rapidly advancing domain that enables the interpretation of human cognitive states and intentions through brainwave analysis. This technology has demonstrated significant potential in augmenting the quality of life for individuals with conditions such as paralysis by decoding their neural patterns. Electroencephalograms (EEG) are the cornerstone of this progress, providing a non-invasive and secure means of capturing brain activity. The integration of machine learning, particularly deep learning techniques, has considerably enhanced the accuracy of EEG interpretation in the last decade. However, a critical challenge persists in the training of machine learning algorithms on EEG data due to pronounced variability among individual brain activities. Such variability can result in suboptimal model performance when data availability is scarce. Transfer learning, a strategy successful in other domains like computer vision and natural language processing, offers a promising avenue to deal with the variability of heterogeneous EEG datasets. This chapter provides a comprehensive review of the current state of EEG transfer learning methodologies and an outlook on large-scale brainwave decoding.