lgc-rct 0.1.1

Local Geometric Consistency + Riemannian Centering Transformation for cross-subject EEG transfer learning

LGC-RCT — Local Geometric Consistency + Riemannian Centering Transformation

Calibration-free cross-subject EEG transfer learning for passive Brain-Computer Interfaces.

Licona Muñoz, R. et al. — "Local Geometric Consistency for Cross-Subject EEG Transfer Learning" NAT 2026, Berlin. (citation forthcoming)

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What is LGC-RCT?

LGC-RCT is a calibration-free method for cross-subject EEG transfer learning designed for passive BCI applications. It combines two components:

• RCT (Riemannian Centering Transformation) [1]: a transfer learning method that re-centers covariance representations across domains to the identity matrix. Domains are treated as independent prior to the re-centering and classification phases.

• LGC (Local Geometric Consistency) (proposed): a local-geometry phase applied prior to RCT. For each covariance matrix, a neighborhood including its immediate temporal neighbors on both sides is defined. A local Riemannian mean is computed over this neighborhood, and the matrix is replaced by this local estimate. The parameter K controls the degree of local geometric consistency enforced between neighboring window-level covariance matrices.

In this work, we investigate whether enforcing local geometric consistency among neighboring covariance matrices prior to RCT improves cross-subject workload classification.

Pipeline

 Raw EEG (N channels)
        │
        ▼
 ┌─────────────────────────────────────────────────────────────┐
 │  Band-pass filter + Sliding windows                         │
 │  (dataset-specific band · 4 s windows · 50% overlap)        │
 └─────────────────────────────────────────────────────────────┘
        │  (N_epochs, C, T)
        ▼
 ┌─────────────────────────────────────────────────────────────┐
 │  Covariance matrix estimation                               │
 │  Ledoit-Wolf · pyriemann Covariances('lwf')                 │
 └─────────────────────────────────────────────────────────────┘
        │  (N_epochs, C, C)  SPD matrices
        ▼
 ╔═════════════════════════════════════════════════════════════╗
 ║  LGC — Local Geometric Consistency          [proposed]      ║
 ║  Riemannian moving average over K temporal neighbors        ║
 ║  Block-aware: smoothing does not cross class boundaries     ║
 ╚═════════════════════════════════════════════════════════════╝
        │  (N_epochs, C, C)  smoothed SPD matrices
        ▼
 ┌─────────────────────────────────────────────────────────────┐
 │  RCT — Riemannian Centering Transformation                  │
 │  TLCenter: re-centers each domain to the identity matrix    │
 └─────────────────────────────────────────────────────────────┘
        │
        ▼
 ┌─────────────────────────────────────────────────────────────┐
 │  MDM classifier (with geodesic filtering)                   │
 │  Trained on source domains only · target labels never used  │
 └─────────────────────────────────────────────────────────────┘
        │
        ▼
  Decision rule — Class 0 or Class 1

Key properties

• Calibration-free: no labeled data from the target subject required
• Standard API: follows the (X, y, domains) convention of pyriemann transfer learning
• Paradigm-agnostic: validated on mental workload and affective state classification (preliminary)

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Installation

pip install lgc-rct

For exact reproducibility of published results:

pip install -r requirements-freeze.txt
pip install lgc-rct

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Quick Start

from lgcrct import LGCRCTPipeline, run_loso

# X : np.ndarray, shape (N, C, T) — bandpass-filtered EEG epochs
# y : np.ndarray, shape (N,)      — class labels {0, 1}
# domains : np.ndarray, shape (N,) — subject IDs

# LGC-RCT (proposed method)
pipe = LGCRCTPipeline(half_window=10, cov_estimator="lwf")
pipe.fit(X, y, domains, target_domain="subject_01")
y_pred = pipe.predict(X_test, y_test, domains_test)

# Full LOSO evaluation
results = run_loso(X, y, domains, half_window=10, cov_estimator="lwf")

Input format

X       : np.ndarray (N, C, T)   bandpass-filtered EEG epochs
y       : np.ndarray (N,)        class labels {0, 1}
domains : np.ndarray (N,)        subject IDs (str or int)

Recommendation: apply sliding-window segmentation before calling LGC-RCT. LGC smoothing relies on temporal neighbors within each class segment — more windows per segment means richer temporal structure and stronger smoothing effect. A 4-second window with 50% overlap (2-second step) at 128 Hz is a validated configuration.

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Validated Results

Mental Workload — Team Metrics dataset (private, TNO)

34 pilots, UAV supervision task, binary workload classification, LOSO, 128 Hz.

Method	Alpha ACC	Theta ACC	Calib-free?
MDM	50.56 ± 1.63	51.34 ± 2.15	—
RCT	57.74 ± 2.56	57.94 ± 3.46	YES
LGC-RCT K=1	63.11 ± 4.04	60.61 ± 3.42	YES
LGC-RCT K=10	77.73 ± 6.39	73.91 ± 6.05	YES

As reported in NAT26 Berlin proceedings.

Affective State Classification — DEAP dataset (preliminary)

32 subjects, emotion recognition, binary classification, LOSO, 128 Hz, 15–36 Hz.

Task	Method	ACC	Calib-free?	Status
Valence	LGC-RCT K=10	79.52 ± 5.87	YES	preliminary
Arousal	LGC-RCT K=10	75.51 ± 5.68	YES	preliminary

These results are preliminary and obtained without any dataset-specific tuning. The method configuration (K=10, cov_estimator='lwf' (Ledoit-Wolf covariance estimator)) is identical to the one used for workload; only the frequency band differs (15–36 Hz for DEAP vs. alpha/theta for Team Metrics). They suggest that LGC-RCT generalizes across passive BCI paradigms. Full analysis forthcoming.

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Repository Structure

lgc-rct/
├── lgcrct/                                        # Installable Python package
│   ├── smoothing.py                               # LGC: block-aware Riemannian moving average
│   ├── pipeline.py                                # LGCRCTPipeline: fit / predict / transform
│   └── evaluation.py                              # run_loso: LOSO cross-subject evaluation
├── demos/
│   └── 02_demo_deap.py                            # Demo on DEAP public dataset
├── experiments/                                   # Requires Team Metrics dataset (private, TNO)
│   └── lgc_rct_loso.py                            # Full LOSO experiment script
├── data/
│   └── FORMAT.md                                  # Dataset format and download instructions
└── results/
    ├── loso_34pilots_published.csv                # NAT26 Berlin — K ablation (alpha & theta)
    ├── LGC-RCT_K10_DEAP_VALENCE_15-36Hz_LOSO.csv # DEAP valence (preliminary)
    └── LGC-RCT_K10_DEAP_AROUSAL_15-36Hz_LOSO.csv # DEAP arousal (preliminary)

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Reproducibility

The LGC-RCT pipeline is fully deterministic: Ledoit-Wolf covariance estimation, Riemannian mean computation, and RCT alignment all have unique, closed-form or convergent solutions on the SPD manifold. LOSO partitioning is determined entirely by subject IDs. No random seed is required — results are exactly reproducible given the same data and dependency versions (see requirements-freeze.txt).

This property extends to third-party use: researchers who apply lgc-rct to their own datasets and publish results can guarantee exact replication by any laboratory, without dependence on random initialization, hardware, or number of runs.

Dataset availability

The Team Metrics dataset is proprietary (TNO, Netherlands) and cannot be shared. To access it for replication of the NAT26 Berlin results, contact Anne-Marie Brouwer (TNO).

The DEAP demo (demos/02_demo_deap.py) runs on the publicly available DEAP dataset. See data/FORMAT.md for download instructions.

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Dependencies

Package	Version
Python	≥ 3.9
numpy	≥ 1.26.4
pyriemann	≥ 0.9
scikit-learn	≥ 1.6.1

experiments/lgc_rct_loso.py additionally requires gumpy for EEG bandpass filtering. gumpy is not available on PyPI — install from: https://github.com/gumpy-bci/gumpy Always pass fs=128 explicitly (gumpy defaults to fs=256).

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Citation

If you use this method, please cite the paper:

@inproceedings{licona2026lgcrct,
  title     = {Local Geometric Consistency for Cross-Subject {EEG} Transfer Learning},
  author    = {Licona Mu{\~n}oz, Ricardo and
               Guetschel, Pierre and
               Bruin, Juliette and
               Yilmaz, Efecan and
               Brouwer, Anne-Marie},
  booktitle = {Proceedings of the Fifth Neuroadaptive Technology Conference (NAT'26)},
  year      = {2026},
  note      = {Berlin, Germany}
}

Paper DOI will be added upon proceedings publication.

If you use this software specifically, please also cite the software release:

@software{licona2026lgcrct_software,
  author    = {Licona Mu{\~n}oz, Ricardo and
               Guetschel, Pierre and
               Bruin, Juliette and
               Yilmaz, Efecan and
               Brouwer, Anne-Marie},
  title     = {lgc-rct: Local Geometric Consistency + Riemannian Centering Transformation},
  year      = {2026},
  publisher = {Zenodo},
  version   = {v0.1.1},
  doi       = {10.5281/zenodo.19225509}
}

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References

[1] Zanini, P., Congedo, M., Jutten, C., Said, S., & Berthoumieu, Y. (2018). Transfer Learning: A Riemannian Geometry Framework With Applications to Brain–Computer Interfaces. IEEE Transactions on Biomedical Engineering, 65(5), 1107–1116. https://doi.org/10.1109/TBME.2017.2742541

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License

MIT License — see LICENSE for details.