Unbiased unsupervised stimulus reconstruction for EEG-based auditory attention decoding

It is possible to decode auditory attention to speech from electrophysiological brain recordings such as electroencephalography (EEG). Such an auditory attention decoding (AAD) allows, e.g., to determine to which person a listener is attending in a multi-talker scenario. The vast majority of research has focused on developing supervised AAD algorithms in which the decoder is trained based on ground truth labels about the attention to each speaker. However, to work optimally, the trained decoders must be subject-specific and adapt over time to track sudden changes in signal statistics (e.g. electrode failures). Since it is often impractical to regularly retrain these decoders with a dedicated calibration session, an unsupervised algorithm has recently emerged as an alternative. In this paper, we show that the state-of-the-art unsupervised AAD algorithm is biased by its initialisation, which leads to a suboptimal convergence. This bias has the largest effect when only a limited amount of data is available to train it, e.g. to train an unsupervised decoder that can quickly adapt to sudden changes. We show that this bias can be easily removed, leading to a better classification accuracy. However, the gain in accuracy reduces as the number of classified segments increases.