Human Attention and Communication Mediated via Machine Learning

Explore the intersection of human attention, machine learning, and communication in this 34-minute lecture by Maureen Clerc from INRIA Nice. Delve into the world of wavelets and their applications in modeling biological vision principles and extracting information from brain activity. Discover how machines excel at recognizing repetitive patterns while humans are adept at detecting departures from regularity. Learn about neural markers related to attention across sensory modalities and how machines can be trained to measure these signals, potentially opening new avenues for human-to-human communication. The lecture covers topics such as brain signal types, multitrial matching, computer interfaces, the P300 component, covariate shifts, low-quality EEG, auditory attention detection, and ADHD evaluation, providing a comprehensive overview of cutting-edge research in neuroscience and machine learning.

Intro
Maureens PhD experience
Types of brain signals
Multitrial matching
Attention
Computer interfaces
The P300
Covariate shifts
Low quality EEG
Auditory attention detection
Conclusion
Evaluation of ADHD