Detecting Noise: Using Weakly Supervised Algorithms with EEG Data

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Agenda
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6:30 PM -- Networking & Food

7:00 PM -- Greetings

7:05 PM -- Two Algorithms for Weakly Supervised Denoising of EEG Data - Tim Oates

Location
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UMBC
Seminar Room
UMBC Technology Center, 1450 S Rolling Road, Halethorpe, MD 21227

https://www.google.com/maps/place/UMBC+Technology+Center/@39.232245,-76.716379,16z/data=!4m5!3m4!1s0x0:0xaab221e555b29819!8m2!3d39.2345392!4d-76.714056

Parking
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Once on UMBC's South Campus, continue to the building complex on the top of the hill. Turn left into the Visitor Parking lot just past the main entrance. The main building is across the street from the parking lot. Enter at the revolving glass doors at the main entrance.

Talks
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Two Algorithms for Weakly Supervised Denoising of EEG Data
Electroencephalogram (EEG) data is used for a variety of purposes, including brain-computer interfaces, disease diagnosis, and determining cognitive states. Yet EEG signals are susceptible to noise from many sources, such as muscle and eye movements, and motion of electrodes and cables. Traditional approaches to this problem involve supervised training to identify signal components corresponding to noise so that they can be removed. However, these approaches are artifact specific. In this talk, I will discuss two algorithms for solving this problem that uses a weak supervisory signal to indicate that some noise is occurring, but not what the source of the noise is or how it is manifested in the EEG signal. In the first algorithm, the EEG data is decomposed into independent components using Independent Components Analysis, and these components form bags that are labeled and classified by a multi-instance learning algorithm that can identify the noise components for removal to reconstruct a clean EEG signal. The second algorithm is a novel Generative Adversarial Network (GAN) formulation. I’ll present empirical results on EEG data gathered by the Army Research Lab, and discuss pros and cons of both algorithms.

Speakers
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Dr. Tim Oates is an Oros Family Professor in the Computer Science Department at the University of Maryland, Baltimore County. His Ph.D. from the University of Massachusetts Amherst was in the areas of artificial intelligence and machine learning with a focus on situated
language learning. After working as a postdoctoral researcher in the MIT Artificial Intelligence Lab, he joined UMBC where he has taught extensively in core areas of Computer Science, including data structures, discrete math, compiler design, artificial intelligence, machine learning, and robotics. Dr. Oates has published more than 150 peer-reviewed papers in areas such as time series analysis, natural language processing, relational learning, and social media analysis. He has developed systems to determine operating room state from video streams, predict the need for blood transfusions and emergency surgery for traumatic brain injury patients based on vital signs data, detect seizures from scalp EEG, and find story chains (causal connections) joining news articles, among many others. Recently Dr. Oates served as the Chief Scientist of a Virgina-based startup where he developed architectures and algorithms for managing contact data, including entity linking, fuzzy record matching, and connected components on billion node graphs stored in a columnar database. He has extensive knowledge of machine learning algorithms, implementations, and usage. Dr. Oates can be contacted at [masked]

Company
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Dr. Oates is the co-founder and Chief Data Scientist at synaptiq.ai, a data science consulting firm. https://www.synaptiq.ai