LMU-EEG: A Legendre Memory Unit Framework for Accurate Seizure Detection from EEG Signals
Bibliographic record
Abstract
This paper presents an energy-efficient seizure detection framework leveraging Legendre Memory Unit (LMU)based models, designed to address the challenges of long-range temporal modeling in EEG time-series data. LMUs offer a hardware-friendly alternative to conventional recurrent architectures by employing a fixed, linear time-invariant (LTI) memory system based on orthogonal Legendre polynomials, enabling precise temporal representation with minimal computational overhead. Our pipeline integrates signal preprocessing, frequency domain feature extraction, Random Forest-based EEG channel selection, class balancing via SMOTE, and LMU-based classification. Evaluated on the CHB-MIT Scalp EEG dataset, the proposed system achieves high sensitivity (average 97.26%) and low false detection rates (average $\mathbf{0. 0 3 2 9}$) across multiple subjects. These results demonstrate that LMUs not only outperform traditional models like LSTMs in detection accuracy and temporal capacity, but also maintain advantages critical for real-time, lowpower neurotechnologies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".