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040 a| EREb| enge| rdac| ERE;

035 a| (OCoLC)1609158861;

049 a| EREE;

100 1 a| Polishchuk, Simone| author;

245 10 a| TRANSFORMER MODELS FOR ROBUST EEG SOURCE LOCALIZATION;

264 1 a| [Greenville, N.C.] :b| [East Carolina Universityc| 2026.];

300 a| 44 pages;

336 a| textb| txt2| rdacontent;

337 a| computerb| c2| rdamedia;

338 a| online resourceb| cr2| rdacarrier;

347 a| text fileb| application/pdfc| 1.69 MB2| rda;

538 a| System requirements: Adobe Reader.;

538 a| Mode of access: World Wide Web.;

502 c| East Carolina Universityd| 2026.;

504 a| Includes bibliographical references.;

520 3 a| Electroencephalography (EEG) measures brain activity using electrodes placed on the scalp. Estimating the underlying source locations from these measurements is an ill-posed inverse problem, and traditional methods often struggle in the presence of noise and complex source configurations. Recent approaches have applied neural networks to this task, but many rely on assumptions that limit their ability to generalize across different data settings. This work presents a transformer-based model for EEG source localization that supports both single-source and multi-source prediction. The model is designed to handle challenges commonly found in EEG data, including measurement noise, varying source configurations, and missing electrodes. Training is performed on large-scale simulated EEG data generated from known source locations.A structured evaluation framework is introduced to assess performance under different conditions, including varying noise levels, source region densities, and electrode dropout sce- narios. Across these settings, the transformer model consistently achieves lower localization error compared to both classical methods and existing neural network approaches. In addition, a set of ablation studies is conducted to analyze the impact of architectural and training design choices. The code and data used in this work will be made publicly available.;

653 a| EEG, Transformers, Machine Learning;

700 1 a| Hart, Dr.e| degree supervisor.;

710 2 a| East Carolina University;

856 40 z| Access via ScholarShipu| http://hdl.handle.net/10342/14786;

949 a| Click on web addressw| ASISh| JOYNER101;

949 a| Click on web addressw| ASISh| HSL111;

994 a| C0b| ERE;

596 a| 1 4;

998 a| 6795469;

999 a| CLICK ON WEB ADDRESSw| ASISc| 1i| 6795469-1001l| JNETm| JOYNERr| Ys| Yt| JNE3ETDu| 7/29/2026x| ETDz| JERESOURCE;

999 a| CLICK ON WEB ADDRESSw| ASISc| 1i| 6795469-2001l| HSLELECm| HSLr| Ys| Yt| HEETDu| 7/29/2026x| ETDz| HERESOURCE;

TRANSFORMER MODELS FOR ROBUST EEG SOURCE LOCALIZATION

Author/creator	Polishchuk, Simon author
Other author	Hart, Dr. degree supervisor.
Other author	East Carolina University
Format	Theses and dissertations
Publication	[Greenville, N.C.] : [East Carolina University 2026.]
Description	44 pages
Supplemental Content	Access via ScholarShip

Summary	Electroencephalography (EEG) measures brain activity using electrodes placed on the scalp. Estimating the underlying source locations from these measurements is an ill-posed inverse problem, and traditional methods often struggle in the presence of noise and complex source configurations. Recent approaches have applied neural networks to this task, but many rely on assumptions that limit their ability to generalize across different data settings. This work presents a transformer-based model for EEG source localization that supports both single-source and multi-source prediction. The model is designed to handle challenges commonly found in EEG data, including measurement noise, varying source configurations, and missing electrodes. Training is performed on large-scale simulated EEG data generated from known source locations.A structured evaluation framework is introduced to assess performance under different conditions, including varying noise levels, source region densities, and electrode dropout sce- narios. Across these settings, the transformer model consistently achieves lower localization error compared to both classical methods and existing neural network approaches. In addition, a set of ablation studies is conducted to analyze the impact of architectural and training design choices. The code and data used in this work will be made publicly available.
Dissertation note	East Carolina University 2026.
Bibliography note	Includes bibliographical references.
Technical details	System requirements: Adobe Reader.
Technical details	Mode of access: World Wide Web.

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TRANSFORMER MODELS FOR ROBUST EEG SOURCE LOCALIZATION

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