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Spring 2024: Detecting Epileptic Seizures from EEG signals using Neural Networks

Affiliations: STEM Research Leadership
Project Leader: Romil Vikram Sonigra

romils@tamu.edu

Electrical & Computer Engineering

Faculty Mentor: P R Kumar, Ph.D.
Meeting Times:
TBA
Team Size:
3
Open Spots: 3
Special Opportunities:

Your contribution will lead to a co-authorship on any publications we obtain for this research.

Team Needs:
Mandatory requirements are python programming and enthusiasm to learn and explore more about transformer architectures and Convolutional Neural Networks.
Also, there’s going to be weekly report writing as well to ensure accurate drafting of whatever results/progress has been made. This is important for accurate tracking of results obtained (whether positive or negative). Thus willingness to write a brief report is an important requirement.
Description:
We will use different neural networks architectures to detect whether an epileptic seizure is occurring in real time given raw EEG signals as input. We will also explore different preprocessing techniques for the EEG signals to improve overall seizure catching efficacy of the algorithm being implemented. We already have some preliminary results to build upon thus there’s going to be quite a bit of exploration but predominantly a lot of hyperparameter tuning for a few different models and preprocessing techniques so as to optimize the overall performance of the algorithm.

Written by:
América Soto-Arzat
Published on:
May 3, 2024

Categories: FullTags: Spring 2024

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