no code implementations • 16 Feb 2022 • Vahid Khalkhali, Nabila Shawki, Vinit Shah, Meysam Golmohammadi, Iyad Obeid, Joseph Picone
Scalp electroencephalogram (EEG) signals inherently have a low signal-to-noise ratio due to the way the signal is electrically transduced.
no code implementations • 3 Jan 2018 • Scott Yang, Silvia Lopez, Meysam Golmohammadi, Iyad Obeid, Joseph Picone
In this study, we investigated the effectiveness of using an active learning algorithm to automatically annotate a large EEG corpus.
no code implementations • 3 Jan 2018 • Vinit Shah, Meysam Golmohammadi, Saeedeh Ziyabari, Eva Von Weltin, Iyad Obeid, Joseph Picone
However, when fewer electrodes are used, less spatial information is available, making it harder to detect artifacts.
no code implementations • 3 Jan 2018 • Meysam Golmohammadi, Saeedeh Ziyabari, Vinit Shah, Eva Von Weltin, Christopher Campbell, Iyad Obeid, Joseph Picone
Recurrent Neural Networks (RNNs) with sophisticated units that implement a gating mechanism have emerged as powerful technique for modeling sequential signals such as speech or electroencephalography (EEG).
no code implementations • 3 Jan 2018 • Amir Harati, Meysam Golmohammadi, Silvia Lopez, Iyad Obeid, Joseph Picone
Feature extraction for automatic classification of EEG signals typically relies on time frequency representations of the signal.
no code implementations • 3 Jan 2018 • Vinit Shah, Eva von Weltin, Silvia Lopez, James Riley McHugh, Lily Veloso, Meysam Golmohammadi, Iyad Obeid, Joseph Picone
We introduce the TUH EEG Seizure Corpus (TUSZ), which is the largest open source corpus of its type, and represents an accurate characterization of clinical conditions.
no code implementations • 3 Jan 2018 • Silvia Lopez, Aaron Gross, Scott Yang, Meysam Golmohammadi, Iyad Obeid, Joseph Picone
In this study, we explore the impact this variability has on machine learning performance.
no code implementations • 29 Dec 2017 • Saeedeh Ziyabari, Vinit Shah, Meysam Golmohammadi, Iyad Obeid, Joseph Picone
In this paper, we discuss the deficiencies of existing metrics for a seizure detection task and propose several new metrics that offer a more balanced view of performance.
no code implementations • 28 Dec 2017 • Meysam Golmohammadi, Saeedeh Ziyabari, Vinit Shah, Silvia Lopez de Diego, Iyad Obeid, Joseph Picone
We have also evaluated our system on a held-out evaluation set based on the Duke University Seizure Corpus and demonstrate that performance trends are similar to the TUH EEG Seizure Corpus.
no code implementations • 28 Dec 2017 • Meysam Golmohammadi, Amir Hossein Harati Nejad Torbati, Silvia Lopez de Diego, Iyad Obeid, Joseph Picone
Significance: The TUH EEG Corpus enables application of highly data consumptive machine learning algorithms to EEG analysis.