Robust and real-time decoding of selective auditory attention from M/EEG: A state-space modeling approach

Humans are able to identify and track a target speaker amid a cacophony of acoustic interference, which is often referred to as the cocktail party phenomenon. Results from several decades of studying this phenomenon have culminated in recent years in various promising attempts to decode the attentional state of a listener in a competing-speaker environment from M/EEG recordings. Most existing approaches operate in an offline fashion and require the entire data duration and multiple trials to provide robust results. Therefore, they cannot be used in emerging applications such as smart hearing aids, where a single trial must be used in real-time to decode the attentional state. In this work, we close this gap by integrating various techniques from state-space modeling paradigm such as adaptive filtering, sparse estimation, and Expectation-Maximization, and devise a framework for robust and real-time decoding of the attentional state from M/EEG recordings. We validate the performance of this framework using comprehensive simulations as well as application to experimentally acquired M/EEG data. Our results reveal that the proposed real-time algorithms perform nearly as accurate as the existing state-of-the-art offline techniques, while providing a significant degree of adaptivity, statistical robustness, and computational savings.