Bayesian LDA - Bayesian Linear Discriminant Analysis

Python: NimbusLDA | Julia: NimbusLDA
Mathematical Model: Pooled Gaussian Classifier (PGC) Bayesian LDA is a Bayesian classification model with uncertainty quantification. It uses a shared precision matrix across all classes, making it fast and efficient for BCI classification.

Python Personalizer head: use as Personalizer(head="lda") (default), or standalone as NimbusLDA. See Personalizer & Middleware.

Available in Both SDKs:

Python SDK: NimbusLDA class (sklearn-compatible)
Julia SDK: NimbusLDA (RxInfer.jl-based)

Both implementations provide the same Bayesian inference with uncertainty quantification.

Start Here

Quickstart

Start with SDK setup and first inference workflow.

Model Selection

Compare Nimbus models by data characteristics and use case.

Examples

See practical BCI examples for training and inference.

Overview

Bayesian LDA extends traditional Linear Discriminant Analysis with full Bayesian inference, providing: ✅ Posterior probability distributions (not just point estimates)
✅ Uncertainty quantification for each prediction
✅ Probabilistic confidence scores
✅ Fast inference (<20ms per trial)
✅ Training and calibration support
✅ Batch and streaming inference modes

Quick Start

Python
Julia

from nimbus_bci import NimbusLDA
import numpy as np

# Create and fit classifier
clf = NimbusLDA(mu_scale=3.0)
clf.fit(X_train, y_train)

# Predict with uncertainty
predictions = clf.predict(X_test)
probabilities = clf.predict_proba(X_test)

# Online learning
clf.partial_fit(X_new, y_new)

using NimbusSDK

# Train model
model = train_model(
    NimbusLDA,
    train_data;
    iterations=50
)

# Predict
results = predict_batch(model, test_data)

When to Use Bayesian LDA

Bayesian LDA is ideal for:

Motor Imagery classification (2-4 classes)
Well-separated class distributions
Fast inference requirements (<20ms)
Interpretable results for medical applications
When classes have similar covariance structures

Consider Bayesian QDA instead if:

Classes have significantly different covariance structures
Complex, overlapping distributions
Need more flexibility in modeling per-class variances

Model Architecture

Mathematical Foundation (Pooled Gaussian Classifier)

Bayesian LDA implements a Pooled Gaussian Classifier (PGC), which models class-conditional distributions with a shared precision matrix:

p(x | y=k) = N(μ_k, W^-1)

Where:

μ_k = mean vector for class k (learned from data)
W = shared precision matrix (same for all classes)
Assumes classes have similar covariance structure

Key Assumption: All classes share the same covariance structure, which makes training and inference faster.

Hyperparameters

Bayesian LDA supports configurable hyperparameters for optimal performance tuning: Available Hyperparameters (training):

Parameter	Type	Default	Range	Description
`dof_offset`	Int	2	[1, 5]	Degrees of freedom offset for Wishart prior
`mean_prior_precision`	Float64	0.01	[0.001, 0.1]	Prior precision for class means

Parameter Effects:

dof_offset: Controls regularization strength
- Lower values (1) → More data-driven, less regularization
- Higher values (3-5) → More regularization, more conservative
mean_prior_precision: Controls prior strength on class means
- Lower values (0.001) → Weaker prior, trusts data more
- Higher values (0.05-0.1) → Stronger prior, more regularization

Model Structure

struct NimbusLDA <: BCIModel
    mean_posteriors::Vector          # MvNormal posteriors for class means
    precision_posterior::Any         # Wishart posterior for shared precision
    priors::Vector{Float64}          # Empirical class priors
    metadata::ModelMetadata          # Model info
    dof_offset::Int                  # Degrees of freedom offset (training)
    mean_prior_precision::Float64    # Mean prior precision (training)
end

RxInfer Implementation

The Bayesian LDA model uses RxInfer.jl for variational Bayesian inference: Learning Phase:

@model function NimbusLDA_learning_model(y, labels, n_features, n_classes, dof_offset, mean_prior_precision)
    # Prior on shared precision
    dof = n_features + dof_offset
    W ~ Wishart(dof, I)
    
    # Priors on class means
    for k in 1:n_classes
        m[k] ~ MvNormal(mean=zeros(n_features), precision=mean_prior_precision * I)
    end
    
    # Likelihood with known labels
    for i in eachindex(y)
        k = labels[i]
        y[i] ~ MvNormal(mean=m[k], precision=W)
    end
end

Prediction Phase (Mixture Likelihood): Inference uses the learned posterior distributions as priors in a mixture model:

@model function NimbusLDA_predictive(y, empirical_priors, n_features)
    n_classes = length(empirical_priors)
    
    # Class means and shared precision are initialized from trained posteriors.
    local m
    for k in 1:n_classes
        m[k] ~ MvNormal(mean=zeros(n_features), precision=I)
    end

    local p
    for k in 1:n_classes
        p[k] ~ Wishart(n_features + 2, I)
    end
    
    z ~ Categorical(empirical_priors)
    for i in eachindex(y)
        y[i] ~ NormalMixture(switch = z, m = m, p = p)
    end
end

The trained mean and precision posteriors are supplied through RxInfer initialization so uncertainty is carried into prediction.

Usage

1. Load Pre-trained Model

Python SDK: The Python SDK (nimbus-bci) trains models locally and doesn’t require pre-trained model loading. See Python SDK Quickstart for training examples.

Python
Julia

from nimbus_bci import NimbusLDA
import pickle

# Python SDK: Train your own model locally
# No authentication or model zoo needed

# Quick training example
clf = NimbusLDA(mu_scale=3.0)
clf.fit(X_train, y_train)

# Save for later use
with open("my_motor_imagery.pkl", "wb") as f:
    pickle.dump(clf, f)

# Load saved model
with open("my_motor_imagery.pkl", "rb") as f:
    clf = pickle.load(f)

print("Model info:")
print(f"  Classes: {clf.classes_}")
print(f"  Features: {clf.n_features_in_}")

using NimbusSDK

# Authenticate
NimbusSDK.install_core("nbci_live_your_key")

# Load from Nimbus model zoo
model = load_model(NimbusLDA, "motor_imagery_4class_v1")

println("Model loaded:")
println("  Features: $(get_n_features(model))")
println("  Classes: $(get_n_classes(model))")
println("  Paradigm: $(get_paradigm(model))")

2. Train Custom Model

Python
Julia

from nimbus_bci import NimbusLDA
from nimbus_bci.compat import extract_csp_features
import mne
import pickle

# Load and preprocess EEG data
raw = mne.io.read_raw_gdf("motor_imagery.gdf", preload=True)
raw.filter(8, 30)

events = mne.find_events(raw)
event_id = {'left': 1, 'right': 2, 'feet': 3, 'tongue': 4}
epochs = mne.Epochs(raw, events, event_id, tmin=0, tmax=4, preload=True)

# Extract CSP features
csp_features, csp = extract_csp_features(epochs, n_components=8)
labels = epochs.events[:, 2]

# Train NimbusLDA model with default hyperparameters
clf = NimbusLDA()
clf.fit(csp_features, labels)

# Or train with custom hyperparameters
clf_tuned = NimbusLDA(
    mu_scale=3.0,        # Prior strength for means (default: 1.0)
    class_prior_alpha=1.0
)
clf_tuned.fit(csp_features, labels)

# Save for later use
with open("my_motor_imagery.pkl", "wb") as f:
    pickle.dump(clf_tuned, f)

print("Model trained successfully!")
print(f"Training accuracy: {clf_tuned.score(csp_features, labels):.1%}")

using NimbusSDK

# Prepare training data with labels
train_features = csp_features  # (16 × 250 × 100)
train_labels = [1, 2, 3, 4, 1, 2, ...]  # 100 labels

train_data = BCIData(
    train_features,
    BCIMetadata(
        sampling_rate = 250.0,
        paradigm = :motor_imagery,
        feature_type = :csp,
        n_features = 16,
        n_classes = 4,
        chunk_size = nothing
    ),
    train_labels  # Required for training!
)

# Train NimbusLDA model with default hyperparameters
model = train_model(
    NimbusLDA,
    train_data;
    iterations = 50,        # Inference iterations
    showprogress = true,    # Show progress bar
    name = "my_motor_imagery",
    description = "4-class MI classifier with CSP"
)

# Or train with custom hyperparameters (v0.2.0+)
model = train_model(
    NimbusLDA,
    train_data;
    iterations = 50,
    showprogress = true,
    name = "my_motor_imagery_tuned",
    description = "4-class MI with tuned hyperparameters",
    dof_offset = 2,                    # DOF offset (default: 2)
    mean_prior_precision = 0.01        # Prior precision (default: 0.01)
)

# Save for later use
save_model(model, "my_model.jld2")

Training Parameters:

iterations: Number of variational inference iterations (default: 50)
- More iterations = better convergence but slower training
- 50-100 is typically sufficient
showprogress: Display progress bar during training
name: Model identifier
description: Model description for documentation
dof_offset: Degrees of freedom offset (default: 2, range: [1, 5])
mean_prior_precision: Prior precision for means (default: 0.01, range: [0.001, 0.1])

3. Subject-Specific Calibration

Fine-tune a pre-trained model with subject-specific data (much faster than training from scratch):

Python
Julia

from nimbus_bci import NimbusLDA
import numpy as np
import pickle

# Load baseline model (trained on multiple subjects)
with open("motor_imagery_baseline.pkl", "rb") as f:
    base_clf = pickle.load(f)

# Collect 10-20 calibration trials from new subject
X_calib, y_calib = collect_calibration_trials()  # Your function

# Personalize using online learning (partial_fit)
personalized_clf = NimbusLDA()
personalized_clf.fit(X_baseline, y_baseline)  # Start with baseline

# Fine-tune on calibration data
for _ in range(10):  # Multiple passes for adaptation
    personalized_clf.partial_fit(X_calib, y_calib)

# Save personalized model
with open("subject_001_calibrated.pkl", "wb") as f:
    pickle.dump(personalized_clf, f)

print("Model personalized successfully!")

# Load base model
base_model = load_model(NimbusLDA, "motor_imagery_baseline_v1")

# Collect 10-20 calibration trials from new subject
calib_features = collect_calibration_trials()  # Your function
calib_labels = [1, 2, 3, 4, 1, 2, ...]

calib_data = BCIData(calib_features, metadata, calib_labels)

# Calibrate (personalize) the model
personalized_model = calibrate_model(
    base_model,
    calib_data;
    iterations = 20  # Fewer iterations needed
)

save_model(personalized_model, "subject_001_calibrated.jld2")

Calibration Benefits:

Requires only 10-20 trials per class (vs 50-100 for training from scratch)
Faster: 20 iterations vs 50-100
Better generalization: Uses pre-trained model as prior
Typical accuracy improvement: 5-15% over generic model
Hyperparameters automatically preserved: calibrate_model() inherits training hyperparameters from the base model (dof_offset, mean_prior_precision) ensuring consistency

Important: You don’t need to specify hyperparameters when calibrating - they are automatically inherited from the base model. This ensures the calibrated model uses the same regularization strategy that worked well during initial training.

4. Batch Inference

Process multiple trials efficiently:

Python
Julia

from nimbus_bci import NimbusLDA
import numpy as np

# Run batch inference
predictions = clf.predict(X_test)
probabilities = clf.predict_proba(X_test)
confidences = np.max(probabilities, axis=1)

# Analyze results
print(f"Predictions: {predictions}")
print(f"Mean confidence: {np.mean(confidences):.3f}")

# Calculate accuracy
accuracy = np.mean(predictions == y_test)
print(f"Accuracy: {accuracy * 100:.1f}%")

# Prepare test data
test_data = BCIData(test_features, metadata, test_labels)

# Run batch inference
results = predict_batch(model, test_data; iterations=10)

# Analyze results
println("Predictions: ", results.predictions)
println("Mean confidence: ", mean(results.confidences))

# Calculate accuracy
accuracy = sum(results.predictions .== test_labels) / length(test_labels)
println("Accuracy: $(round(accuracy * 100, digits=1))%")

# Calculate ITR
itr = calculate_ITR(accuracy, 4, 4.0)  # 4 classes, 4-second trials
println("ITR: $(round(itr, digits=1)) bits/minute")

5. Streaming Inference

Real-time chunk-by-chunk processing:

For detailed Python streaming examples, see Python SDK Streaming Inference.

Python
Julia

from nimbus_bci import StreamingSession
from nimbus_bci.data import BCIMetadata

# Configure metadata with chunk size
metadata = BCIMetadata(
    sampling_rate=250.0,
    paradigm="motor_imagery",
    feature_type="csp",
    n_features=16,
    n_classes=4,
    chunk_size=250  # 1-second chunks
)

# Initialize streaming session
session = StreamingSession(clf.model_, metadata)

# Process chunks as they arrive
for chunk in eeg_feature_stream:
    result = session.process_chunk(chunk)
    print(f"Chunk: pred={result.prediction}, conf={result.confidence:.3f}")

# Finalize trial with aggregation
final_result = session.finalize_trial()
print(f"Final: pred={final_result.prediction}, conf={final_result.confidence:.3f}")

# Reset for next trial
session.reset()

# Initialize streaming session
session = init_streaming(model, metadata_with_chunk_size)

# Process chunks as they arrive
for chunk in eeg_feature_stream
    result = process_chunk(session, chunk; iterations=10)
    println("Chunk: pred=$(result.prediction), conf=$(round(result.confidence, digits=3))")
end

# Finalize trial with aggregation
final_result = finalize_trial(session; method=:weighted_vote)
println("Final: pred=$(final_result.prediction), conf=$(round(final_result.confidence, digits=3))")

Hyperparameter Tuning (v0.2.0+)

Fine-tune Bayesian LDA for optimal performance on your specific dataset.

When to Tune Hyperparameters

Consider tuning when:

Default performance is unsatisfactory
You have specific data characteristics (very noisy or very clean)
You have limited or extensive training data
You want to optimize for your specific paradigm

Tuning Strategies

For High SNR / Clean Data / Many Trials

Use lower regularization to let the data drive the model:

Python
Julia

from nimbus_bci import NimbusLDA

# Lower regularization for clean data
clf = NimbusLDA(
    mu_scale=1.0,      # Weaker prior, trust data more
    class_prior_alpha=0.5
)
clf.fit(X_train, y_train)

model = train_model(
    NimbusLDA,
    train_data;
    iterations = 50,
    dof_offset = 1,               # Less regularization
    mean_prior_precision = 0.001  # Weaker prior, trust data more
)

Use when:

SNR > 5 dB
100+ trials per class
Clean, artifact-free data
Well-controlled experimental conditions

For Low SNR / Noisy Data / Few Trials

Use higher regularization for stability:

Python
Julia

from nimbus_bci import NimbusLDA

# Higher regularization for noisy data
clf = NimbusLDA(
    mu_scale=5.0,      # Stronger prior
    class_prior_alpha=2.0
)
clf.fit(X_train, y_train)

model = train_model(
    NimbusLDA,
    train_data;
    iterations = 50,
    dof_offset = 3,               # More regularization
    mean_prior_precision = 0.05   # Stronger prior
)

Use when:

SNR < 2 dB
40-80 trials per class
Noisy data or limited artifact removal
Challenging recording conditions

Balanced / Default Settings

The defaults work well for most scenarios:

model = train_model(
    NimbusLDA,
    train_data;
    iterations = 50,
    dof_offset = 2,               # Balanced (default)
    mean_prior_precision = 0.01   # Balanced (default)
)

Use when:

Moderate SNR (2-5 dB)
80-150 trials per class
Standard BCI recording conditions
Starting point for experimentation

Hyperparameter Search Example

Systematically search for optimal hyperparameters:

Python
Julia

from nimbus_bci import NimbusLDA
from sklearn.model_selection import GridSearchCV, train_test_split

# Define parameter grid
param_grid = {
    'mu_scale': [1.0, 3.0, 5.0, 10.0],
    'class_prior_alpha': [0.5, 1.0, 2.0]
}

# Split data
X_train_sub, X_val, y_train_sub, y_val = train_test_split(
    X_train, y_train, test_size=0.2, stratify=y_train, random_state=42
)

print("Searching hyperparameters...")
clf = NimbusLDA()
grid_search = GridSearchCV(
    clf,
    param_grid,
    cv=5,
    scoring='accuracy',
    verbose=1
)

grid_search.fit(X_train_sub, y_train_sub)

print("\nBest hyperparameters:")
print(f"  mu_scale: {grid_search.best_params_['mu_scale']}")
print(f"  class_prior_alpha: {grid_search.best_params_['class_prior_alpha']}")
print(f"  CV accuracy: {grid_search.best_score_*100:.1f}%")

# Get best model
best_clf = grid_search.best_estimator_

# Retrain on all training data
best_clf.fit(X_train, y_train)

using NimbusSDK

# Define search grid
dof_values = [1, 2, 3, 4]
prior_values = [0.001, 0.01, 0.05, 0.1]

# Split data into train/validation
train_data, val_data = split_data(all_data, ratio=0.8)

best_accuracy = 0.0
best_params = nothing

println("Searching hyperparameters...")
for dof in dof_values
    for prior in prior_values
        # Train model with these hyperparameters
        model = train_model(
            NimbusLDA,
            train_data;
            iterations = 50,
            dof_offset = dof,
            mean_prior_precision = prior,
            showprogress = false
        )
        
        # Validate
        results = predict_batch(model, val_data)
        accuracy = sum(results.predictions .== val_data.labels) / length(val_data.labels)
        
        println("  dof=$dof, prior=$prior: $(round(accuracy*100, digits=1))%")
        
        # Track best
        if accuracy > best_accuracy
            best_accuracy = accuracy
            best_params = (dof=dof, prior=prior)
        end
    end
end

println("\nBest hyperparameters:")
println("  dof_offset: $(best_params.dof)")
println("  mean_prior_precision: $(best_params.prior)")
println("  Validation accuracy: $(round(best_accuracy*100, digits=1))%")

# Retrain on all data with best hyperparameters
final_model = train_model(
    NimbusLDA,
    all_data;
    iterations = 50,
    dof_offset = best_params.dof,
    mean_prior_precision = best_params.prior
)

Quick Tuning Guidelines

Scenario	`dof_offset`	`mean_prior_precision`	Notes
Excellent data quality	1	0.001	Minimal regularization
Good data quality	2 (default)	0.01 (default)	Balanced approach
Moderate data quality	2-3	0.01-0.03	Slight regularization
Poor data quality	3-4	0.05-0.1	Strong regularization
Very limited trials	4	0.1	Maximum regularization

Pro Tip: Start with defaults (dof_offset=2, mean_prior_precision=0.01) and only tune if performance is unsatisfactory. The defaults are optimized for typical BCI scenarios.

Training Requirements

Data Requirements

Minimum: 40 trials per class (160 total for 4-class)
Recommended: 80+ trials per class (320+ total for 4-class)
For calibration: 10-20 trials per class sufficient

Bayesian LDA requires at least 2 trials to estimate class statistics and shared precision matrix. Single-trial training is not statistically valid for LDA and will raise an ArgumentError.Your training data must have shape (n_features, n_samples, n_trials) where n_trials >= 2.

Feature Normalization

Critical for cross-session BCI performance!Normalize your features before training for 15-30% accuracy improvement across sessions.

Python
Julia

from sklearn.preprocessing import StandardScaler
import pickle

# Estimate normalization from training data
scaler = StandardScaler()
X_train_norm = scaler.fit_transform(X_train)

# Train with normalized features
clf = NimbusLDA()
clf.fit(X_train_norm, y_train)

# Save model and scaler together
with open("model_with_scaler.pkl", "wb") as f:
    pickle.dump({'model': clf, 'scaler': scaler}, f)

# Later: Apply same normalization to test data
X_test_norm = scaler.transform(X_test)
predictions = clf.predict(X_test_norm)

# Estimate normalization from training data
norm_params = estimate_normalization_params(train_features; method=:zscore)
train_norm = apply_normalization(train_features, norm_params)

# Train with normalized features
train_data = BCIData(train_norm, metadata, labels)
model = train_model(NimbusLDA, train_data)

# Save params with model
@save "model.jld2" model norm_params

# Later: Apply same params to test data
test_norm = apply_normalization(test_features, norm_params)

See Feature Normalization for the recommended train/test scaling workflow.

Feature Requirements

Bayesian LDA expects preprocessed features, not raw EEG: ✅ Required preprocessing:

Bandpass filtering (8-30 Hz for motor imagery)
Artifact removal (ICA recommended)
Spatial filtering (CSP for motor imagery)
Feature extraction (log-variance for CSP features)
Temporal aggregation (handled automatically during training/inference)

❌ NOT accepted:

Raw EEG channels
Unfiltered data
Non-extracted features

See Preprocessing Requirements for details.

Temporal Aggregation

Critical Preprocessing Step: Before training, the SDK automatically aggregates the temporal dimension of each trial into a single feature vector. This prevents treating temporally correlated samples as independent observations, which would violate the i.i.d. assumption of the model.

The aggregation method depends on your feature type and paradigm:

CSP features: Log-variance aggregation (default for motor imagery)
Power spectral features: Mean or median aggregation
Other features: Configurable via BCIMetadata.temporal_aggregation

This aggregation happens automatically during train_model() and predict_batch() calls.

Performance Characteristics

Computational Performance

Operation	Latency	Notes
Training	10-30 seconds	50 iterations, 100 trials per class
Calibration	5-15 seconds	20 iterations, 20 trials per class
Batch Inference	10-20ms per trial	10 iterations
Streaming Chunk	10-20ms	10 iterations per chunk

All measurements on standard CPU (no GPU required).

Classification Accuracy

Paradigm	Classes	Typical Accuracy	ITR
Motor Imagery	2 (L/R hand)	75-90%	15-25 bits/min
Motor Imagery	4 (L/R/Feet/Tongue)	70-85%	20-35 bits/min
P300	2 (Target/Non-target)	80-95%	25-40 bits/min

Accuracy is highly subject-dependent. Subject-specific calibration typically improves accuracy by 5-15%.

Model Inspection

View Model Parameters

Python
Julia

import numpy as np

params = clf.model_.params

# Posterior class means
print("Posterior class means:")
for k, class_label in enumerate(clf.classes_):
    print(f"  Class {class_label}: {params['mu'][k]}")

# Shared Normal-Wishart posterior parameters
print("\nShared precision posterior:")
print(f"  nu: {params['nu']}")
print(f"  psi shape: {params['psi'].shape}")

# Model info
print("\nModel info:")
print(f"  Features: {clf.n_features_in_}")
print(f"  Classes: {clf.classes_}")

# Class priors (learned from data)
print("\nClass priors:")
priors = np.exp(params["log_priors"])
for k, class_label in enumerate(clf.classes_):
    print(f"  Class {class_label}: {priors[k]:.3f}")

# Extract point estimates from posterior distributions
using Distributions

# Class means (extract from posterior distributions)
println("Class means:")
for (k, mean_posterior) in enumerate(model.mean_posteriors)
    mean_point = mean(mean_posterior)  # Extract point estimate
    println("  Class $k: ", mean_point)
end

# Shared precision matrix (extract from posterior distribution)
println("\nShared precision matrix (first 3x3):")
precision_point = mean(model.precision_posterior)  # Extract point estimate
println(precision_point[1:3, 1:3])

# Model metadata
println("\nMetadata:")
println("  Name: ", model.metadata.name)
println("  Paradigm: ", model.metadata.paradigm)
println("  Features: ", model.metadata.n_features)
println("  Classes: ", model.metadata.n_classes)

# Class priors
println("\nClass priors:")
for (k, prior) in enumerate(model.priors)
    println("  Class $k: ", prior)
end

Accessing model parameters: The SDK stores full posterior distributions (not just point estimates) for proper Bayesian inference. To get point estimates, use mean(posterior) to extract the mean of the posterior distribution. For precision matrices, use mean(precision_posterior) to get the expected precision matrix.

Compare Models

Python
Julia

from nimbus_bci import NimbusLDA
import numpy as np

# Train multiple models with different hyperparameters and compare
models = []
for mu_scale in [1.0, 3.0, 5.0]:
    clf = NimbusLDA(mu_scale=mu_scale)
    clf.fit(X_train, y_train)
    accuracy = clf.score(X_test, y_test)
    
    print(f"mu_scale: {mu_scale}, Accuracy: {accuracy*100:.1f}%")
    models.append((mu_scale, clf, accuracy))

# Select best model
best_config, best_clf, best_acc = max(models, key=lambda x: x[2])
print(f"\nBest configuration: mu_scale={best_config} ({best_acc*100:.1f}%)")

# Train multiple models and compare
models = []
for n_iter in [20, 50, 100]
    model = train_model(NimbusLDA, train_data; iterations=n_iter)
    results = predict_batch(model, test_data)
    accuracy = sum(results.predictions .== test_labels) / length(test_labels)
    
    println("Iterations: $n_iter, Accuracy: $(round(accuracy*100, digits=1))%")
    push!(models, (n_iter, model, accuracy))
end

Advantages & Limitations

Advantages

✅ Fast Training: Shared covariance estimation is efficient
✅ Fast Inference: Analytical posterior computation (<20ms)
✅ Interpretable: Clear probabilistic formulation
✅ Memory Efficient: Single shared precision matrix
✅ Robust: Handles uncertainty naturally via Bayesian inference
✅ Production-Ready: Battle-tested in real BCI applications

Limitations

❌ Shared Covariance Assumption: May not fit well if classes have very different spreads
❌ Linear Decision Boundary: Cannot capture non-linear class boundaries
❌ Gaussian Assumption: Assumes normal class distributions
❌ Not Ideal for Overlapping Classes: Use NimbusQDA for complex distributions

Model Selection Context

Use NimbusLDA when classes are mostly stationary and well separated, and speed or interpretability matter. If classes overlap with different covariance structure, consider NimbusQDA. If the decision boundary is non-Gaussian, consider NimbusSoftmax in Python or NimbusProbit in Julia. If distributions drift over time, consider NimbusSTS in Python. For the canonical side-by-side comparison, see Model Specification.

Next Read

Bayesian QDA (NimbusQDA)

More flexible classifier with class-specific covariances

NimbusSoftmax (Python)

Python non-Gaussian static classifier for complex decision boundaries

NimbusProbit (Julia)

Julia non-Gaussian static classifier for complex decision boundaries

Bayesian STS (NimbusSTS)

Adaptive model for non-stationary data and long sessions

Training Tutorial

Complete training walkthrough

Code Examples

Working examples

References

Implementation:

RxInfer.jl: https://rxinfer.com/
Source code: src/models/nimbus_lda/ in NimbusSDKCore

Theory:

Fisher, R. A. (1936). “The use of multiple measurements in taxonomic problems”
Bishop, C. M. (2006). “Pattern Recognition and Machine Learning” (Chapter 4)
Pooled Gaussian Classifier (PGC) with shared covariance structure

BCI Applications:

Blankertz et al. (2008). “Optimizing spatial filters for robust EEG single-trial analysis”
Lotte et al. (2018). “A review of classification algorithms for EEG-based BCI”

​Bayesian LDA - Bayesian Linear Discriminant Analysis

​Start Here

Quickstart

Model Selection

Examples

​Overview

​Quick Start

​When to Use Bayesian LDA

​Model Architecture

​Mathematical Foundation (Pooled Gaussian Classifier)

​Hyperparameters

​Model Structure

​RxInfer Implementation

​Usage

​1. Load Pre-trained Model

​2. Train Custom Model

​3. Subject-Specific Calibration

​4. Batch Inference

​5. Streaming Inference

​Hyperparameter Tuning (v0.2.0+)

​When to Tune Hyperparameters

​Tuning Strategies

​For High SNR / Clean Data / Many Trials

​For Low SNR / Noisy Data / Few Trials

​Balanced / Default Settings

​Hyperparameter Search Example

​Quick Tuning Guidelines

​Training Requirements

​Data Requirements

​Feature Normalization

​Feature Requirements

​Temporal Aggregation

​Performance Characteristics

​Computational Performance

​Classification Accuracy

​Model Inspection

​View Model Parameters

​Compare Models

​Advantages & Limitations

​Advantages

​Limitations

​Model Selection Context

​Next Read

Bayesian QDA (NimbusQDA)

NimbusSoftmax (Python)

NimbusProbit (Julia)

Bayesian STS (NimbusSTS)

Training Tutorial

Code Examples

​References

Bayesian LDA - Bayesian Linear Discriminant Analysis

Start Here

Overview

Quick Start

When to Use Bayesian LDA

Model Architecture

Mathematical Foundation (Pooled Gaussian Classifier)

Hyperparameters

Model Structure

RxInfer Implementation

Usage

1. Load Pre-trained Model

2. Train Custom Model

3. Subject-Specific Calibration

4. Batch Inference

5. Streaming Inference

Hyperparameter Tuning (v0.2.0+)

When to Tune Hyperparameters

Tuning Strategies

For High SNR / Clean Data / Many Trials

For Low SNR / Noisy Data / Few Trials

Balanced / Default Settings

Hyperparameter Search Example

Quick Tuning Guidelines

Training Requirements

Data Requirements

Feature Normalization

Feature Requirements

Temporal Aggregation

Performance Characteristics

Computational Performance

Classification Accuracy

Model Inspection

View Model Parameters

Compare Models

Advantages & Limitations

Advantages

Limitations

Model Selection Context

Next Read

References