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Documentation Index

Fetch the complete documentation index at: https://docs.nimbusbci.com/llms.txt

Use this file to discover all available pages before exploring further.

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External Preprocessing Integration

Nimbus expects preprocessed features, not raw EEG. This page covers the handoff between external EEG tools and Nimbus SDKs: feature shape, label conventions, export formats, and validation.
For preprocessing theory and recommended feature pipelines, start with Preprocessing Requirements. For Python-native MNE workflows, use Python SDK MNE Integration.

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What This Page Owns

Use this page when you preprocess outside the SDK language that will run inference:
  • MNE-Python features exported for Julia
  • EEGLAB or MATLAB features exported for Python or Julia
  • OpenViBE feature streams saved to CSV or MAT files
  • Cross-tool shape and label validation

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Target Data Shapes

TargetExpected ShapeNotes
Python classifiers(n_trials, n_features)Standard sklearn-style tabular features.
Python BCIData utilities(n_features, n_samples, n_trials)Used by lower-level batch/streaming utilities.
Julia BCIData(n_features, n_samples, n_trials)Labels are usually 1-indexed integers.
For streaming, chunks should be shaped (n_features, chunk_size).

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MNE-Python To Julia

import numpy as np
from scipy.io import savemat

# Example MNE/CSP output: (n_trials, n_components, n_times)
X_csp = csp.fit_transform(epochs.get_data(), labels)

# Julia expects: (n_features, n_samples, n_trials)
features_julia = np.transpose(X_csp, (1, 2, 0))

savemat("features_for_julia.mat", {
    "features": features_julia.astype("float64"),
    "labels": labels.astype("int64"),
    "sampling_rate": float(epochs.info["sfreq"]),
})

using MAT
using NimbusSDK

data = matread("features_for_julia.mat")
features = Float64.(data["features"])
labels = Int.(vec(data["labels"]))

metadata = BCIMetadata(
    sampling_rate = Float64(data["sampling_rate"]),
    paradigm = :motor_imagery,
    feature_type = :csp,
    n_features = size(features, 1),
    n_classes = length(unique(labels)),
    chunk_size = nothing
)

bci_data = BCIData(features, metadata, labels)

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EEGLAB Or MATLAB Export

MATLAB arrays often already use (channels/features, samples, trials), which matches Julia BCIData. 
% features: n_features x n_samples x n_trials
% labels: 1 x n_trials, preferably 1-indexed
save("features_for_nimbus.mat", "features", "labels", "sampling_rate")

using MAT

data = matread("features_for_nimbus.mat")
features = Float64.(data["features"])
labels = Int.(vec(data["labels"]))
If labels come from a zero-indexed pipeline, convert them before training or evaluation: 
if minimum(labels) == 0
    labels .+= 1
end

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OpenViBE CSV Export

OpenViBE often exports time-series rows. Segment the stream into trials before passing data to Nimbus. 
import numpy as np
import pandas as pd
from scipy.io import savemat

df = pd.read_csv("openvibe_features.csv")

# Example columns: timestamp, f1, f2, ..., label, trial_id
feature_cols = [c for c in df.columns if c.startswith("f")]
trial_ids = df["trial_id"].unique()

trials = []
labels = []
for trial_id in trial_ids:
    trial = df[df["trial_id"] == trial_id]
    trials.append(trial[feature_cols].to_numpy().T)
    labels.append(int(trial["label"].iloc[-1]))

features = np.stack(trials, axis=2)  # (n_features, n_samples, n_trials)

savemat("openvibe_for_nimbus.mat", {
    "features": features.astype("float64"),
    "labels": np.asarray(labels, dtype="int64"),
})

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Handoff Validation

Run these checks before loading exported data into an SDK: 
import numpy as np

def validate_export(features, labels):
    assert features.ndim == 3, "Expected (features, samples, trials)"
    assert features.shape[2] == len(labels), "Labels must match trials"
    assert np.isfinite(features).all(), "Features contain NaN or Inf"
    assert len(np.unique(labels)) >= 2, "Need at least two classes"

using NimbusSDK

report = diagnose_preprocessing(bci_data)
if !isempty(report.errors)
    error("Preprocessing export failed validation: $(report.errors)")
end

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Normalization Handoff

Estimate normalization parameters on training data only, then save them with the model or exported feature bundle. 
norm_params = estimate_normalization_params(train_features; method=:zscore)
train_norm = apply_normalization(train_features, norm_params)
test_norm = apply_normalization(test_features, norm_params)
See Feature Normalization for the full workflow.

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Next Read

Preprocessing Requirements

Feature extraction requirements and paradigm guidance.

Python MNE Integration

Native Python SDK workflows with MNE.

Julia SDK Quickstart

Load exported features into Julia workflows.

Feature Normalization

Keep feature scales consistent across sessions.