Daniel D.E. Wong

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 improves stimulus-response models for brain responses to speech using adaptive techniques and recurrent neural networks, achieving better classification performance in a match-vs-mismatch task.

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

The paper examines how the brain processes phonotactic probabilities in speech using EEG, finding that these probabilities modulate neural responses and support a pre-lexical role in speech perception.

This paper reviews multiway canonical correlation analysis (MCCA) for fusing brain signals across subjects to extract common components, with examples and discussion of its limitations.

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