Post-processing of EEG-based Auditory Attention Decoding Decisions via Hidden Markov Models
This work addresses the practical use of AAD for listeners in noisy environments, but it is incremental as it builds on existing AAD algorithms with a post-processing enhancement.
The paper tackled the problem of inaccurate predictions in EEG-based auditory attention decoding (AAD) for identifying attended speakers in multi-speaker environments by augmenting AAD with a hidden Markov model (HMM) to model temporal attention structure, resulting in significant improvements in accuracy and responsiveness over existing methods.
Auditory attention decoding (AAD) algorithms exploit brain signals, such as electroencephalography (EEG), to identify which speaker a listener is focusing on in a multi-speaker environment. While state-of-the-art AAD algorithms can identify the attended speaker on short time windows, their predictions are often too inaccurate for practical use. In this work, we propose augmenting AAD with a hidden Markov model (HMM) that models the temporal structure of attention. More specifically, the HMM relies on the fact that a subject is much less likely to switch attention than to keep attending the same speaker at any moment in time. We show how a HMM can significantly improve existing AAD algorithms in both causal (real-time) and non-causal (offline) settings. We further demonstrate that HMMs outperform existing postprocessing approaches in both accuracy and responsiveness, and explore how various factors such as window length, switching frequency, and AAD accuracy influence overall performance. The proposed method is computationally efficient, intuitive to use and applicable in both real-time and offline settings.