Brain-Computer Interfaces

The Challenge Point Framework proposes that optimal learning occurs when task difficulty is adapted to current skill level. Currently, subjective measures are used, but with increasing need for rehabilitation, it is critical to optimize the motor learning process. Physiological signals such as EEG can be used to model cognitive and physical workloads to understand how well patient's can match different task difficulty. The aim of this study is to advance to rehabilitation process by investigating EEG  and other physiological patterns corresponding to cognitive and physical effort, to gauge how and if task difficulty should be adapted.

Hearing aids and cochlear implants struggle in noisy, multi-speaker environments, as they cannot tell who the user is actually trying to listen to. This project tackles that gap by using EEG brain signals to decode the direction of a listener's auditory attention in real time. By identifying the neural markers of spatial attention, the goal is to build a wearable, adaptive system that can steer assistive hearing devices toward the speaker the user intends to hear.