Same pipelines, different answers: how evaluation design shapes EEG seizure prediction
Authors: z. song, M. Pedersen, D. Henare
Abstract
Seizure-prediction studies on scalp electroencephalography (EEG) often report accuracies that would be clinically useful, yet the score a model receives also depends on how its test data were separated from its training data. We kept five published pipelines fixed, from a spectral baseline (power spectral density with linear discriminant analysis, PSD+LDA) to a transformer, and changed only how they were evaluated on the CHB-MIT database. When whole subjects were held out in 20 repeated partitions, every pipeline scored close to the 0.5 reference (mean area under the ROC curve, AUC, 0.527-0.553), and the five pipelines differed less from one another than each varied between partitions. Within patients, PSD+LDA reached an AUC of 0.815 when windows were allocated at random but 0.572 when each preictal block was kept intact and later seizures were held out for testing. Tracing every window back to its recording showed that an index-based within-patient split had let raw EEG samples cross the train-test boundary in all ten eligible cases. Thresholds tuned to allow 1.67% false positives on validation data did not hold that rate on new subjects, and at those thresholds only 2-6% of preictal windows were detected. A zero-shot test of the baseline on the Siena database gave an AUC of 0.480. In this benchmark, the choice of evaluation changed the answer far more than the choice of architecture. We release the window manifests, partitions and predictions so that new models can be tested on the same terms. All results concern retrospective window classification, not continuous alarms.
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Window leakage in clinical EEG machine learning: matched subject-independent evaluation across ADHD and Parkinson cohortsBurak Aggul · 2026 · Frontiers in Neuroinformatics · Not summarized yet
Window leakage in clinical EEG machine learning measures cross-participant contamination in ADHD and Parkinson classification, rather than seizure prediction.
Abstract
Windowed electroencephalography (EEG) pipelines can produce misleadingly high performance when segments from the same participant occur in both training and testing. We define this contamination as window leakage and test it in two clinically and technically distinct public cohorts: 121 children (61 attention-deficit/hyperactivity disorder [ADHD], 60 controls) and 149 adults (100 Parkinson's disease, 49 controls). Signals were harmonized to 18 common channels and 128 Hz. All primary models used stratified 8-fold participant-disjoint evaluation, 50%-overlapping windows, participant-level probability aggregation, and validation participants drawn exclusively from each outer-training fold. A de…
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Leakage-Controlled Recording-Level Evaluation of Machine Learning, Deep Learning, and Convolutional-Kernel Models for EEG-Based Epileptic Seizure DetectionS. Sunitha, Umadevi Ramamoorthy · 2026 · International Journal of Engineering and Manufacturing · Not summarized yet
Leakage-Controlled Recording-Level Evaluation compares seizure-detection models with recording-grouped validation, not seizure-prediction pipelines.
Abstract
Electroencephalogram (EEG)-based seizure detection is a challenging time-series classification problem because EEG signals are nonlinear, non-stationary, and temporally heterogeneous. This study presents a unified comparison of classical machine-learning, neural and deep-learning, convolutional-kernel time-series, and probability-level ensemble models using the Epileptic Seizure Recognition benchmark dataset. The 11,500 EEG segments were reconstructed into 500 recording groups, and recording-grouped nested cross-validation was applied to prevent segments from the same recording from crossing training and test partitions. Depending on the modelling paradigm, segments were represented either b…
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Seeing Seizures: A Large-Scale Empirical Study of Video and EEG Deep Learning for Automated Rodent Seizure Detection and Severity GradingAlloy Das, Shreyan Ganguly, N. Massey et al. · 2026 · Not summarized yet
Seeing Seizures evaluates video and EEG models for rodent seizure detection and severity grading, rather than forecasting seizures in people.
Abstract
Manual annotation of synchronized video and EEG recordings is a bottleneck in preclinical seizure research. We present an empirical study of seizure detection and behavioral severity grading using 24,131 paired clips from 601 sessions across 20 rats in a soman-induced epilepsy model. Ten video architectures and seven single-channel EEG models are evaluated independently on binary detection and three- and five-class severity tasks. In the session-exclusive reference comparison, R(2+1)D-18 and GRU achieve detection macro-F1 scores of 0.968 and 0.974, respectively; for five-class grading, the corresponding scores are 0.657 and 0.560. Subject-exclusive evaluation reduces five-class macro-F1 to 0…
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Selection of Low‐Density EEG Montages for Optimal Seizure Detection PerformanceS. Abdullateef, Iris L. Soare-Nguyen, B. Jordan et al. · 2026 · Healthcare technology letters · Not summarized yet
Selection of Low-Density EEG Montages optimizes channel configurations for seizure detection, not evaluation design for prediction.
Abstract
ABSTRACT Multi‐channel electroencephalography (EEG) is widely recognised for its ability to non‐invasively capture dynamic brain activity with high temporal resolution. However, conventional EEG systems require meticulous setup, expert interpretation and specialised infrastructure, which limits their applicability in real‐time, resource‐constrained environments such as critical care and emergency settings. As wearable and point‐of‐care EEG technologies gain traction, there is increasing demand for low‐density, automated seizure detection methods that are clinically reliable and scalable. This study presents a data‐driven pipeline for an EEG‐based task, seizure detection, that identifies comp…
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Predicting ICU Seizures Using 1 hour of cEEG.S. Phadikar, Santiago Philibert-Rosas, Steven E. Haworth et al. · 2026 · Journal of clinical neurophysiology · Not summarized yet
Predicting ICU Seizures estimates near-term seizure risk from one hour of adult ICU EEG, a different prediction setting.
Abstract
PURPOSE Continuous electroencephalography (cEEG) is the gold standard for detecting electrographic seizures in the intensive care unit (ICU), but limited resources limit prolonged monitoring for all patients. We developed an automated, interpretable pipeline that uses a single hour of cEEG to estimate near-term seizure risk across multiple prediction windows. METHODS We retrospectively studied 50 adults who underwent ICU cEEG monitoring at a single tertiary center. From a 1-hour segment, we computed automated interictal-ictal continuum (IIC) burden probabilities for lateralized periodic discharges (LPDs), generalized periodic discharges (GPDs), and lateralized rhythmic delta activity (LRDA),…
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Measuring Evaluation Leakage in EEG Motor Imagery Decoding, with an Application to Stroke PatientsAria Chadha · 2026 · Proceedings of the OJSS Virtual Research Conference 2026 · Not summarized yet
Measuring Evaluation Leakage studies how CSP leakage affects EEG motor-imagery decoding, not seizure prediction.
Abstract
Brain-computer interfaces (BCIs) that decode imagined movement from EEG are often evaluated using Common Spatial Patterns (CSP), a supervised filtering method. If CSP is fitted to all data before splitting into training and test sets, information from the test labels leaks into the model and inflates reported accuracy. This study measures the size of that effect. In a null experiment where true accuracy should be at chance (50%), fitting CSP before the split produced 91% within-subject accuracy, a 41-percentage-point inflation, compared with about 5 points in cross-subject evaluation. On real data from 87 healthy participants and 46 post-stroke patients, leakage added 14.3 and 10.6 points re…
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How much does the reduced electroencephalographic montage matter for seizure detection? A large-cohort simulation study.J. Kojima, Hao-Er Shi, Svanik Jaikumar et al. · 2026 · Epilepsia · Not summarized yet
How much does the reduced electroencephalographic montage matter compares full and reduced montages for automated seizure detection.
Abstract
OBJECTIVE Subscalp electroencephalographic (EEG) systems with few channels have emerged as promising solutions for ultra-long-term seizure monitoring, but the impact of montage configuration on automated seizure detection is unclear. We compared automated detection performance between full-scalp and simulated reduced montages approximating published subscalp devices, and assessed variation by epilepsy type, lateralization, and localization. METHODS We conducted a retrospective cross-sectional study of consecutive epilepsy monitoring unit (EMU) admissions from January 2017 to December 2024 at the Hospital of the University of Pennsylvania. Admissions with at least one clinician-annotated seiz…
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Patient-independent EEG seizure detection using channel attention and dual-branch time–frequency featuresHrishikesh Tiwary, Arnav V. Bhavsar, M. H · 2026 · Neural computing & applications (Print) · Not summarized yet
Patient-independent EEG seizure detection develops a channel-attention model under subject-disjoint protocols, addressing detection rather than prediction.
Abstract
We developed a patient-independent EEG-based seizure detection model that generalizes across subjects despite inter-subject variability in spatial seizure expression, electrode characteristics, and artifacts. Scalp EEG recordings from the CHB–MIT (18 channels) and the TUSZ v2.0.3 (20 channels) datasets were evaluated under strict subject-disjoint protocols, employing leave-one-subject-out (LOSO) cross-validation for CHB–MIT and the official subject-disjoint train/dev/eval split for TUSZ. A channel-wise attention front end adaptively reweights EEG channels prior to feature extraction. The reweighted signals are processed through dual-branch time–frequency representations based on empirical mo…
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Updated on 2 November 2026.