moecog 0.1.0

Mother of All ECoG Benchmarks — MOABB-style benchmarking for ECoG motor decoding

MOECoG — Mother of All ECoG Benchmarks

A MOABB-style benchmarking framework for electrocorticographic (ECoG) motor decoding.

"ECoG isn't just the future — it's the testable present."

Vision

MOABB transformed EEG-BCI research by making algorithm comparison reproducible and fair. MOECoG does the same for ECoG motor decoding — the signal modality at the critical intersection of clinical viability (long-term stability, lower surgical risk) and high-performance neural control (high-gamma access, mm-scale spatial resolution).

This project will be successful when we read in an abstract:

"...the proposed method obtained a correlation of 0.82 on MOECoG, outperforming the state of the art by 12%..."

Why ECoG Needs Its Own Benchmark

Property	EEG (MOABB)	ECoG (MOECoG)
Signal type	Scalp potentials	Cortical surface potentials
Key features	mu/beta ERD/ERS	High-gamma broadband (>70 Hz) + beta suppression
Spatial resolution	~cm	~mm
Electrode geometry	Standard montages (10-20)	Patient-specific grids/strips
Primary tasks	Classification (L/R imagery)	Both classification AND continuous regression
Cross-subject	Standard channel alignment	Requires anatomical registration
Noise profile	EMG, EOG artifacts	Epileptiform activity, referencing

MOABB's paradigm/dataset/evaluation/pipeline abstraction is brilliant — but its assumptions (fixed channel montages, epoched classification, standard frequency bands) break down for ECoG.

Architecture

MOECoG follows MOABB's 4-concept design, adapted for ECoG:

+--------------+    +---------------+    +---------------+    +---------------+
|   Dataset    |--->|   Paradigm    |--->|  Evaluation   |--->|   Pipeline    |
|              |    |               |    |               |    |               |
| Raw ECoG +   |    | Motor Imagery |    | WithinSubject |    | Feature ext.  |
| electrode    |    | Finger Flex   |    | CrossSession  |    | + Classifier  |
| positions +  |    | Arm Reach     |    | Transfer      |    | or Regressor  |
| anatomy      |    | Grasp Type    |    |               |    |               |
+--------------+    +---------------+    +---------------+    +---------------+

Key Differences from MOABB

• Dual-task paradigms: Classification (which finger?) AND regression (finger trajectory)
• Anatomical electrode registration: Patient-specific grids mapped to MNI/FreeSurfer atlas
• Broadband feature extraction: High-gamma (70-150 Hz), beta (13-30 Hz), phase-amplitude coupling
• Continuous decoding metrics: Correlation coefficient (r), R-squared, normalized MSE — not just accuracy
• Naturalistic movement support: Not just cued trials but free/spontaneous movements (AJILE12)

Included Datasets

Tier 1: Core Benchmark

Motor-specific, public, well-documented.

ID	Dataset	Source	Subjects	Task	Channels	Modality
MillerFingerFlex	BCI Competition IV Dataset 4	Miller & Schalk	3	Individual finger flexion (5-class regression)	48-64	ECoG grid
MillerLibrary	Stanford/Mayo ECoG Library	Miller 2019, Nature Human Behaviour	34	16 experiments (motor, sensory, language, visual)	Varies	ECoG grid
AJILE12	Annotated Joints in Long-term ECoG	Peterson et al. 2022, Scientific Data	12	Naturalistic wrist movements	>=64	ECoG grid

Tier 2: Extended Benchmark

ID	Dataset	Source	Subjects	Task
BCITetraplegia	BCI and Tetraplegia (WIMAGINE)	Benabid/Costecalde et al.	1 (chronic)	4-class motor imagery, 2D cursor
GraspECoG	Natural grasp types	Various (Pistohl, Bleichner)	Varies	Grasp classification
MoveAgain	Blackrock/BrainGate ECoG subsets	If released publicly	Varies	Arm/hand movement

Tier 3: Cross-Modality Comparison

ID	Dataset	Why included
MOABB_MI	MOABB motor imagery EEG datasets	Direct EEG vs ECoG comparison on matched paradigms

Paradigms

FingerFlexionRegression

• Task: Predict continuous finger flexion trajectories from ECoG
• Metrics: Pearson r, R-squared, NRMSE per finger
• Datasets: MillerFingerFlex, MillerLibrary (motor subset)
• Baseline: BCI Competition IV Dataset 4 leaderboard

MotorImageryClassification

• Task: Classify imagined/attempted movements (L/R hand, feet, tongue, etc.)
• Metrics: Accuracy, ROC-AUC, Cohen's kappa
• Datasets: MillerLibrary (motor imagery subset), BCITetraplegia

NaturalisticReachDecoding

• Task: Decode wrist movement onset and trajectory from unconstrained behavior
• Metrics: Event detection F1, trajectory r, latency
• Datasets: AJILE12

GraspClassification

• Task: Classify grasp types or hand gestures from sensorimotor ECoG
• Metrics: Accuracy, confusion matrix analysis
• Datasets: GraspECoG, MillerLibrary (gesture subset)

Evaluation Strategies

Strategy	Description	Use Case
WithinSubjectCV	K-fold within single subject	Standard single-patient decoding
CrossSessionEval	Train on session A, test on session B	Stability / recalibration assessment
CrossSubjectTransfer	Leave-one-subject-out (with atlas projection)	Generalization / zero-shot transfer
TemporalStabilityEval	Chronological split (early to late)	Long-term signal stability

Baseline Pipelines

Feature Extraction

• LogBandPower — Log power in canonical bands (mu, beta, low-gamma, high-gamma)
• BroadbandChange — Miller's broadband spectral change method
• PAC — Phase-amplitude coupling (theta/gamma, beta/high-gamma)
• CSP_ECoG — Common Spatial Patterns adapted for patient-specific grids
• TimeFrequency — Continuous wavelet / multitaper spectrograms

Decoders

• LDA / SVM / LogisticRegression — Classical classifiers
• Ridge / Kalman — Linear regressors for trajectory decoding
• ECoGNet — Lightweight CNN for ECoG (braindecode-compatible)
• FingerFlex — Convolutional encoder-decoder (Lomtev et al.)
• HTNet — Transfer learning across subjects via Hilbert transform

Quick Start

import moecog
from moecog.datasets import MillerFingerFlex
from moecog.paradigms import FingerFlexionRegression
from moecog.evaluations import WithinSubjectCV
from moecog.pipelines.features import LogBandPower
from sklearn.linear_model import Ridge
from sklearn.pipeline import make_pipeline

# Define pipeline
pipelines = {
    "LogBandPower+Ridge": make_pipeline(LogBandPower(), Ridge(alpha=1.0))
}

# Load data
dataset = MillerFingerFlex()
paradigm = FingerFlexionRegression(fmin=1, fmax=150)
evaluation = WithinSubjectCV(paradigm=paradigm, datasets=[dataset], n_splits=5)

# Run benchmark
results = evaluation.process(pipelines)
print(results.groupby("pipeline")["score"].mean())

Installation

pip install moecog

Or for development:

git clone https://github.com/epyifany/MOECoG.git
cd MOECoG
pip install -e ".[dev]"

Dependencies

Core:

• mne >= 1.5 — ECoG signal handling, coordinate transforms
• numpy, scipy, scikit-learn — Core ML
• pandas — Results management
• h5py — Persistent results storage
• pooch — Robust data downloading

Optional:

• torch, braindecode — Deep learning baselines (pip install moecog[deep])
• pynwb, dandi — NWB data access for AJILE12 (pip install moecog[nwb])
• nilearn, nibabel — Anatomical registration (pip install moecog[anatomy])
• matplotlib, seaborn — Visualization (pip install moecog[viz])

Citation

If you use MOECoG in your research, please cite:

@software{moecog2025,
  title = {MOECoG: Mother of All ECoG Benchmarks},
  author = {Yu, Yifan},
  year = {2025},
  url = {https://github.com/epyifany/MOECoG}
}

And the foundational datasets:

• Miller, K.J. "A library of human electrocorticographic data and analyses." Nature Human Behaviour 3(11), 1225-1235 (2019).
• Peterson, S.M. et al. "AJILE12: Long-term naturalistic human intracranial neural recordings and pose." Scientific Data 9, 184 (2022).
• Schalk, G. et al. BCI Competition IV Dataset 4.

License

BSD-3-Clause (matching MOABB)