Malcolm Slaney

This paper presents a real-time BCI system using EEG attention decoding to control a hearing instrument, enhancing speech perception in noisy environments by steering attention to desired sound sources.

The paper introduces a match-mismatch task to evaluate auditory stimulus-response models, showing state-of-the-art performance and providing a reference implementation.

The paper shows that microsaccade inhibition within 300ms of sound onset is a reliable indicator of auditory salience, validated through crowd-sourced ratings and neural measurements.

This study compares TRF estimation methods for decoding auditory attention from EEG data using a standardized dataset.

This paper introduces Canonical Correlation Analysis (CCA) to decode auditory brain responses and improve stimulus-response mapping for BCI applications.