Why Performance Metrics Overpromise in Auditory Attention Decoding: an Information-Theoretic Reappraisal

Auditory attention decoding (AAD) algorithms are predominantly evaluated in a steady state where a listener continuously attends to the same speaker, using metrics such as accuracy and information transfer rate. However, such metrics fail to account for the (in-)dependence of an AAD prediction with respect to previous predictions. In this paper, we argue that failing to take this dependence into account in the algorithm evaluation can lead to severe misrepresentations of the true performance of an AAD algorithm. We therefore introduce the relative Incremental Mutual Information (rIMI); the rate at which a new prediction removes the remaining uncertainty about the identity of the attended speaker. This allows us to track how much new, useful information a prediction actually generates on top of the information already obtained from previous predictions. By investigating the rIMI and the behaviour of AAD models around attention switches, we demonstrate that recent direct-classification AAD algorithms are not superior to traditional AAD algorithms based on stimulus reconstruction, despite what accuracy alone may suggest. We also demonstrate how these direct-classification AAD predictions are severely influenced by irrelevant feature drifts, which artificially inflates accuracies by leaking information across windows, and even across trials.

Unsupervised EEG-based decoding of absolute auditory attention with canonical correlation analysis

We propose a fully unsupervised algorithm that detects from encephalography (EEG) recordings when a subject actively listens to sound, versus when the sound is ignored. This problem is known as absolute auditory attention decoding (aAAD). We propose an unsupervised discriminative CCA model for feature extraction and combine it with an unsupervised classifier called minimally informed linear discriminant analysis (MILDA) for aAAD classification. Remarkably, the proposed unsupervised algorithm performs significantly better than a state-of-the-art supervised model. A key reason is that the unsupervised algorithm can successfully adapt to the non-stationary test data at a low computational cost. This opens the door to the analysis of the auditory attention of a subject using EEG signals with a model that automatically tunes itself to the subject without requiring an arduous supervised training session beforehand.

Minimally Informed Linear Discriminant Analysis: training an LDA model with unlabelled data

Linear Discriminant Analysis (LDA) is one of the oldest and most popular linear methods for supervised classification problems. In this paper, we demonstrate that it is possible to compute the exact projection vector from LDA models based on unlabelled data, if some minimal prior information is available. More precisely, we show that only one of the following three pieces of information is actually sufficient to compute the LDA projection vector if only unlabelled data are available: (1) the class average of one of the two classes, (2) the difference between both class averages (up to a scaling), or (3) the class covariance matrices (up to a scaling). These theoretical results are validated in numerical experiments, demonstrating that this minimally informed Linear Discriminant Analysis (MILDA) model closely matches the performance of a supervised LDA model. Furthermore, we show that the MILDA projection vector can be computed in a closed form with a computational cost comparable to LDA and is able to quickly adapt to non-stationary data, making it well-suited to use as an adaptive classifier.