Omni-iEEG: A Large-Scale, Comprehensive iEEG Dataset and Benchmark for Epilepsy Research

Chenda Duan; Yipeng Zhang; Sotaro Kanai; Yuanyi Ding; Atsuro Daida; Pengyue Yu; Tiancheng Zheng; Naoto Kuroda

cs.LGFeb 17, 2026

Omni-iEEG: A Large-Scale, Comprehensive iEEG Dataset and Benchmark for Epilepsy Research

Authors: Chenda Duan, Yipeng Zhang, Sotaro Kanai, Yuanyi Ding, Atsuro Daida, Pengyue Yu, Tiancheng Zheng, Naoto Kuroda,

Organizations: UCLA Samueli School of Engineering · UCLA Mattel Children’s Hospital, David Geffen School of Medicine · Children’s Hospital of Michigan, Wayne State University School of Medicine

Abstract

Epilepsy affects over 50 million people worldwide, and one-third of patients suffer drug-resistant seizures where surgery offers the best chance of seizure freedom. Accurate localization of the epileptogenic zone (EZ) relies on intracranial EEG (iEEG). Clinical workflows, however, remain constrained by labor-intensive manual review. At the same time, existing data-driven approaches are typically developed on single-center datasets that are inconsistent in format and metadata, lack standardized benchmarks, and rarely release pathological event annotations, creating barriers to reproducibility, cross-center validation, and clinical relevance. With extensive efforts to reconcile heterogeneous iEEG formats, metadata, and recordings across publicly available sources, we present $\textbf{Omni-iEEG}$ , a large-scale, pre-surgical iEEG resource comprising $\textbf{302 patients}$ and $\textbf{178 hours}$ of high-resolution recordings. The dataset includes harmonized clinical metadata such as seizure onset zones, resections, and surgical outcomes, all validated by board-certified epileptologists. In addition, Omni-iEEG provides over 36K expert-validated annotations of pathological events, enabling robust biomarker studies. Omni-iEEG serves as a bridge between machine learning and epilepsy research. It defines clinically meaningful tasks with unified evaluation metrics grounded in clinical priors, enabling systematic evaluation of models in clinically relevant settings. Beyond benchmarking, we demonstrate the potential of end-to-end modeling on long iEEG segments and highlight the transferability of representations pretrained on non-neurophysiological domains. Together, these contributions establish Omni-iEEG as a foundation for reproducible, generalizable, and clinically translatable epilepsy research. The project page with dataset and code links is available at omni-ieeg.github.io/omni-ieeg.

Figures & tables

Figure 1: Overview of the Omni-iEEG Dataset and Benchmark.

Split	Dataset	#Subjects	#Selected	#Surgery	#Seizure-Free	Duration (Min)
Training	OpeniEEG	111	111	97	66	5-111
	Zurich	12	12	9	9	1-8
	HUP	42	9	9	6	5
	SourceSink	21	20	12	10	1-16
Testing	OpeniEEG	74	74	67	48	5-120
	Zurich	8	8	6	6	3-12

Table 1: Overview of the Omni-iEEG dataset. “#Subjects” indicates the total number of subjects in the dataset; “#Selected” denotes those included in the current split; “#Surgery” refers to subjects who underwent resective surgery; “#Seizure-Free” indicates subjects achieves post-operative seizue-free; “Duration” specifies the range of recording lengths in minutes.

Split	Dataset	#Subjects	#Total Events	#Artifact	#Real HFO	#spkHFO
Training	OpeniEEG	20	19909	5517	14392	10070
	Zurich	3	829	242	587	449
	HUP	3	178	42	136	120
	SourceSink	3	1120	949	171	121
Testing	OpeniEEG	14	13092	2052	11040	7964
	Zurich	2	443	248	195	102

Table 2: Summary of HFO event annotations. “#Subjects” denotes the number of patients; “#Total Events” refers to all annotated HFO events; “#Artifact” indicates events labeled as artifacts; “#Real HFO” represents real HFO; and “#spkHFO” refers to real HFOs that co-occur with spikes.

Split	Dataset	# Valid Subjects	# Normal Channels	# Pathological Channels	# Seizure Free	# Seizure Non-Free	# Anatomical
Training	OpeniEEG	110	4638	1072	66	32	111
	Zurich	12	5619	—	9	3	—
	HUP	9	1034	106	6	3	—
	SourceSink	19	500	177	10	9	—
Testing	OpeniEEG	74	3533	575	48	19	74
	Zurich	8	2611	—	6	2	—

Table 3: Summary of channel labels and clinical outcomes across dataset splits. “#Subjects” indicates the number of valid subjects; “#Normal/Pathological Channels” represent iEEG channels labeled as normal or pathological; “#Seizure-Free/Non-Free” correspond to subjects with or without post-surgical seizure freedom; “#Anatomical Labeled” lists subjects with anatomical channel annotations.

Model	Precision	Recall	F1	AUC
LSTM+Attention	0.7352	0.7359	0.7338	0.9109
PatchTST Transformer	0.7757	0.7686	0.7726	0.9311
TimesNet	0.7589	0.7726	0.7652	0.9221
PyHFO-Omni	0.8025	0.8110	0.8061	0.9390

Table 4: Benchmark for Pathological Event Classification. Evaluation metrics include macro-averaged precision, recall, F1 and AUC.

	Pathological Channel Classification					Outcome
Model	Precision	Recall	F1	Specificity	AUC	AUC
Event-Based Models
eHFO	0.6053	0.6466	0.6195	0.4101	0.6611	0.4521
PyHFO ${}_{\text{spkHFO}}$	0.6000	0.6431	0.6140	0.4089	0.6557	0.4972
PyHFO-Omni ${}_{\text{spkHFO}}$	0.5799	0.6991	0.5635	0.6951	0.7351	0.7438
Segment-Based Models

Table 5: Benchmark results for identifying pathological brain regions. Evaluation has two dimensions: (1) Pathological Channel Identification , based on clinical priors of pathological(SOZ channels) vs. normal (preserved channels in seizure-free patients); (2) Outcome AUC , which assesses whether higher scores in resected channels correlate with postoperative seizure freedom via the resection ratio.

Appendix figures & tables12 assets

Supplementary material from the paper’s appendix.

Appendix

Parameter	Value
filter_freq	[80, 300]
rms_window	$3\times 10^{-3}$
min_window	$6\times 10^{-3}$
min_gap	$10\times 10^{-3}$
min_osc	6
rms_thres	5

Appendix

Table 6: Detector parameters used in our annotation pipeline. All frequency units are in Hz, time in seconds unless noted.

Annotator(s)	Metric	Value
R1 vs. R2	Cohen’s $\kappa$	0.9427
R3 vs. R1	Cohen’s $\kappa$	0.9243
R3 vs. R2	Cohen’s $\kappa$	0.9091
R1, R2, R3	Fleiss’ $\kappa$	0.9254

Appendix

Table 7: Inter-rater agreement among the three primary annotators. Pairwise agreement is measured using Cohen’s

\kappa

, and overall agreement is measured using Fleiss’

\kappa

Model	Anatomical(5)		Anatomical(12) )		Ictal		Sleep–Awake
	F1	Acc	F1	Acc	F1	Acc	F1	Acc
SEEG-NET Wang et al. (2022)	0.2520	0.2550	0.1081	0.1853	0.7526	0.7624	0.6773	0.6421
CLAP Wu et al. (2023b)	0.4750	0.4894	0.3540	0.3897	0.9245	0.9323	0.7225	0.9331
TimeConv-CNN	0.4788	0.4708	0.3087	0.3215	0.8533	0.8720	0.7118	0.9291

Appendix

Table 8: Macro-averaged F1 and balanced accuracy scores for the Anatomical with 5 Classes and 12 Classes, Ictal/Interictal, and Sleep/Awake classification tasks.

Figure 2: Event-based iEEG input: raw waveform (left) and time-frequency representation (right).

Figure 3: Channel Segment-based iEEG input: raw waveform (left) and time-frequency representation (right).

Held-Out	Precision	Recall	F1
Open-iEEG	0.6955	0.6893	0.6227
Zurich	0.7342	0.7520	0.7416
HUP	0.6967	0.7647	0.7219
SourceSink	0.7110	0.7412	0.7221

Appendix

Table 9: Cross-dataset generalization via leave-one-dataset-out HFO event classification.

Held-Out	Model	Precision	Recall	F1	Specificity	AUC	Outcome
Open-iEEG	TimeConv-CNN	0.5912	0.6393	0.5936	0.7383	0.6983	0.5325
	CLAP	0.5785	0.6207	0.5781	0.7284	0.6560	0.5122
	SEEGNET	0.5746	0.6335	0.5279	0.5608	0.6650	0.4628
Source-Sink	TimeConv-CNN	0.5995	0.5857	0.5902	0.8392	0.6031	0.7740
	CLAP	0.6109	0.6063	0.6083	0.8168	0.6222	0.7592
	SEEG-NET	0.6965	0.5999	0.6100	0.9430	0.6331	0.7740

Appendix

Table 10: Cross-dataset generalization via leave-one-dataset-out pathological brain region identification.

Model	Precision	Recall	F1	Specificity	AUC
TimeConv-CNN (30 s)	0.5768	0.7072	0.5442	0.6592	0.7729
TimeConv-CNN (1 min)	0.6081	0.7607	0.6096	0.7478	0.8229
TimeConv-CNN (2 mins)	0.5919	0.7473	0.5642	0.6684	0.8052

Appendix

Table 11: Segment length ablation study (pathological channel classification).

Model	Precision	Recall	F1	Specificity	AUC	Outcome
TimeConv-CNN	0.5924	0.7192	0.5884	0.7371	0.7735	0.7113
CLAP	0.5997	0.7191	0.6065	0.7740	0.7843	0.7048
SEEGNET	0.5919	0.7339	0.5744	0.6946	0.8076	0.6364

Appendix

Table 12: Ablation on resampling rate for pathological brain region identification (500 Hz).

Figure 4: Model interpretation analysis using SHAP.

Model	Precision	Recall	F1	Specificity	AUC	Outcome
SEEG-NET	0.5790	0.7169	0.5259	0.6049	0.7850	0.5952
CLAP	0.5936	0.6997	0.6009	0.7823	0.7684	0.6770
TimeConv-CNN	0.6259	0.7454	0.6469	0.8230	0.8061	0.7380
EEGNet (adapted)	0.5792	0.6957	0.5640	0.7075	0.7468	0.5942
Linear baseline	0.5323	0.5045	0.4899	0.9880	0.5045	0.4920

Appendix

Table 13: Additional baselines for pathological brain region identification (Task 2).

Patient Name	Dataset	Age	Gender	SO	SOZ	Res	Anat	Ev	Int	Ict	Awk	Split
sub-hupHUP060	hup	42	F	N	Y	Y	N	N	Y	Y	N	train
sub-hupHUP064	hup	21	M	Y	Y	Y	N	N	Y	Y	N	test
sub-hupHUP065	hup	36	M	Y	Y	Y	N	N	Y	Y	N	train
sub-hupHUP070	hup	33	M	Y	Y	Y	N	N	Y	Y	N	train
sub-hupHUP074	hup	25	F	Y	Y	Y	N	N	Y	Y	N	train
sub-hupHUP075	hup	57	F	N	Y	Y	N	N	Y	Y	N	train

Appendix

Table 14: Summary of patient metadata and recording availability. Column abbreviations: SO = Surgical Outcome; SOZ = Seizure Onset Zone Annotation; Res = Resection Annotation; Anat = Anatomical Annotation; Ev = Event Annotation; Int = Interictal Recording; Ict = Ictal Recording; Awk = Awake Recording. Value abbreviations: M = Male; F = Female; Y = Yes (present); N = No (absent); - = Not applicable or not available.

Electroencephalography

Electroencephalography (EEG) studies brain-generated electrical activity to understand cognition and support diagnosis, monitoring, and brain-computer interfaces.

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Accurate identification of the epileptogenic zone (EZ) is essential for seizure freedom after resective surgery in drug-resistant epilepsy, yet seizure freedom rates remain below 50%. We developed EpiiSLM, a dual foundation model system for EZ identification with stereo-electroencephalography (sEEG), by training a signal foundation model on 104,990 minutes of sEEG recordings from the Montreal Neurological Institute & Hospital, while leveraging all recordings regardless of surgical outcome and anchoring EZ biomarker extraction on non-epileptic signals. A language foundation model then integrates sEEG-derived outputs with multimodal clinical information to produce interpretable predictions. Under leave-one-patient-out evaluation, EpiiSLM achieved 0.978 contact-level positive predictive value (PPV), outperforming the seizure onset zone(SOZ)-as-EZ baseline by 15.1% (p < 0.05), and 100% region-level accuracy; on an external dataset, EpiiSLM achieved 0.857 contact-level PPV. EpiiSLM requires only one night of interictal sleep data, suggesting potential to reduce invasive sEEG monitoring duration and improve surgical outcomes.

Abstract

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