CIS: EEG, Machine Learning, and Artificial Intelligence – Applications and Progress
This talk will explore the intersection of artificial intelligence (AI) and electroencephalography (EEG). The first fundamental question discussed will be why we require the assistance of machines despite existing human capabilities. This will be followed by a necessarily incomplete historical narrative on the application of AI to EEG, looking at the impact of advances in software and hardware on the clinical care of patients. This will lead into a discussion of the pitfalls and obstacles that prevent or slow the adoption of AI for EEG analysis. The talk will conclude with speculation about future developments and their potential impact on clinical care.
Date and Time
Location
Hosts
Registration
- Date: 20 Sep 2023
- Time: 06:00 PM to 07:00 PM
- All times are (UTC-06:00) Mountain Time (US & Canada)
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- 2155 E Wesley Ave
- Denver, Colorado
- United States 80208
- Building: University of Denver Ritchie School of Engineering and Computer Science
- Room Number: 410
- Click here for Map
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- Co-sponsored by James Gowans
- Starts 13 June 2023 07:56 AM
- Ends 20 September 2023 07:56 PM
- All times are (UTC-06:00) Mountain Time (US & Canada)
- No Admission Charge
Speakers
Dr Jeremy Slater of Fellow of the American Academy of Neurology, the American Epilepsy Society, and the American Clinical Neurophysiology
EEG, Machine Learning, and Artificial Intelligence – Applications and Progress
This talk will explore the intersection of artificial intelligence (AI) and electroencephalography (EEG). The first fundamental question discussed will be why we require the assistance of machines despite existing human capabilities. This will be followed by a necessarily incomplete historical narrative on the application of AI to EEG, looking at the impact of advances in software and hardware on the clinical care of patients. This will lead into a discussion of the pitfalls and obstacles that prevent or slow the adoption of AI for EEG analysis. The talk will conclude with speculation about future developments and their potential impact on clinical care.
Parking: Free street parking immediately around the building (mainly north and south). Paid parking at $2/hr is available in lots immediately north (off E Iliff Ave) and south (off E Wesley Ave) of the RSECS building.
Biography:
Dr. Slater holds a Bachelor of Science in Molecular Biophysics and Biochemistry from Yale University, and Doctor of Medicine from the University of Pittsburgh School of Medicine. Dr. Slater completed his postgraduate training in Internal Medicine, Neurology, and Epilepsy, where he assumed the role of Chief Resident of Neurology at the University of Miami School of Medicine. Dr. Slater has held academic appointments at institutions such as the University of Miami School of Medicine, University of North Dakota School of Medicine, and The University of Texas Medical School at Houston, including various directorial and administrative roles across various hospitals and medical institutions, impacting areas from Epilepsy Monitoring to Clinical Neurophysiology. Dr. Slater served as the Director of the Texas Comprehensive Epilepsy Program from 2005 through 2018 and currently serves as the Chief Medical Officer at Stratus, a role he has held since 2018.
Dr. Slater is board certified by the American Board of Psychiatry and Neurology and the American Board of Clinical Neurophysiology and has been recognized as a Fellow of the American Academy of Neurology, the American Epilepsy Society, and the American Clinical Neurophysiology Society. Dr. Slater has published numerous articles in peer-reviewed journals and given many invited lectures at national and international symposiums, served as an investigator for numerous clinical trials of novel anticonvulsants and medical devices. Dr. Slater’s early research work focused on the potential for applying techniques of the developing field of artificial neural networks to classification problems in clinical neurophysiology. More recent research has focused on changes to brain electrical activity related to drowsiness and exploring differing deep learning architectures for classification of EEG signal abnormalities.