Advanced Modeling Techniques

Note: This page describes future directions and conceptual approaches for advanced BCI modeling. Most techniques described here are not yet implemented in NimbusSDK.Current SDK: Provides RxLDA and RxGMM models for classification tasks.Future Releases: May include advanced techniques based on user demand and research validation.

Current Capabilities vs. Future Work

✅ Currently Available

RxLDA: Linear discriminant analysis
RxGMM: Gaussian mixture models
Model Training: Supervised learning
Model Calibration: Subject adaptation
Uncertainty Quantification: Confidence scores
Streaming Inference: Real-time processing

⏳ Future Directions

Multi-modal fusion (EEG + EMG)
Temporal models (HMM, Kalman)
Hierarchical models
Transfer learning
Custom factor graphs
Online adaptation

Concept: EEG + EMG Fusion

Combining cortical activity (EEG) with muscle activity (EMG) could provide: Potential Benefits:

Robustness: Redundant information sources
Complementary timing: EEG precedes EMG by ~50-100ms
Improved accuracy: Leverage both cortical intent and peripheral execution

Conceptual Model:

p(\text{intent} | \text{EEG}, \text{EMG}) \propto p(\text{EEG}|\text{intent}) \cdot p(\text{EMG}|\text{intent}) \cdot p(\text{intent})

Status: Not implemented - requires multi-modal data collection and validation

Concept: EEG + fNIRS Integration

Combining fast electrical (EEG) with slow hemodynamic (fNIRS) signals: Potential Benefits:

Temporal resolution: EEG provides millisecond precision
Spatial resolution: fNIRS provides better localization
Artifact rejection: Cross-validate signals

Status: Research direction - not planned for near-term release

Temporal Dynamics (Future)

Hidden Markov Models (HMM)

Concept: Model sequences of brain states over time

p(s_1, \ldots, s_T, x_1, \ldots, x_T) = p(s_1) \prod_{t=2}^{T} p(s_t|s_{t-1}) \prod_{t=1}^{T} p(x_t|s_t)

Potential Applications:

Continuous brain state tracking
Sequence classification (multi-step gestures)
State-dependent adaptive BCI

Why Not Implemented Yet:

Requires temporal training data
More complex calibration
Higher computational cost
Needs validation for real-time BCI

Workaround: Use RxLDA/RxGMM on temporally aggregated features (already common practice)

Kalman Filtering

Concept: Continuous state estimation with linear dynamics

s_t = A s_{t-1} + w_t \quad \text{(State evolution)}

x_t = H s_t + v_t \quad \text{(Observations)}

Potential Applications:

Smooth cursor control
Continuous movement decoding
Predictive control

Why Not Implemented Yet:

BCI data often discrete (trials) rather than continuous
Linear assumptions may not hold for neural data
Requires careful system identification

Current Approach: Streaming inference with chunk-based updates provides similar real-time capabilities

Hierarchical Models (Conceptual)

Multi-Level Modeling

Concept: Model population, subject, and session levels

\theta_{\text{population}} \rightarrow \theta_{\text{subject}} \rightarrow \theta_{\text{session}}

Potential Benefits:

Transfer learning across subjects
Reduced calibration data
Population-level insights

Status: Basic calibration (subject adaptation) available; full hierarchy not implemented

Example: Population Prior + Subject Adaptation

# Current SDK approach (simplified)
using NimbusSDK

# 1. Load population-level baseline model
baseline_model = load_model(RxLDAModel, "motor_imagery_baseline_v1")

# 2. Adapt to individual subject with small dataset
subject_calib_data = collect_calibration(num_trials=20)
personalized_model = calibrate_model(baseline_model, subject_calib_data)

# 3. Use personalized model
results = predict_batch(personalized_model, test_data)

This provides a simplified form of hierarchical modeling through transfer learning.

Advanced Uncertainty Quantification (Partial)

Current Capabilities

NimbusSDK already provides:

results = predict_batch(model, data)

# Full posterior distribution
posterior = results.posteriors  # [n_classes × n_trials]

# Confidence (max posterior probability)
confidence = results.confidences  # [n_trials]

# Entropy as uncertainty measure
entropy = -sum(posterior .* log.(posterior .+ 1e-10), dims=1)

Future Directions

Advanced Uncertainty (Future)

Potential additions:

Epistemic vs. aleatoric: Separate model vs. data uncertainty
Predictive distributions: Forecast future trials
Confidence calibration: Ensure confidence scores are well-calibrated
Out-of-distribution detection: Flag unusual inputs

Status: Basic uncertainty available; advanced methods under research

Transfer Learning (Partial)

Current: Model Calibration

NimbusSDK supports transfer learning via calibration:

# Pre-trained model trained on many subjects
baseline = load_model(RxLDAModel, "motor_imagery_baseline_v1")

# Quick adaptation to new subject
personalized = calibrate_model(baseline, subject_data; iterations=20)

This is a form of transfer learning: Prior knowledge (baseline model) + subject-specific adaptation.

Future: Advanced Transfer

Advanced Transfer Learning (Future)

Potential enhancements:

Domain adaptation: Transfer across paradigms
Few-shot learning: Learn from very few examples
Meta-learning: Learn to adapt quickly
Cross-session transfer: Maintain performance over days/weeks

Status: Basic transfer via calibration available; advanced methods planned

Custom Factor Graphs (Future)

Concept

Allow users to define custom probabilistic models using RxInfer.jl:

# Hypothetical future API
using RxInfer

@model function custom_bci(y, x)
    x ~ NormalMeanVariance(0.0, 1.0)
    y ~ NormalMeanVariance(x, 0.1)
end

# Integrate with NimbusSDK (hypothetical)
custom_model = NimbusModel(custom_bci)
train_model(custom_model, training_data)

Why Not Available Yet:

Requires unified API between RxInfer.jl and NimbusSDK
Training/inference abstractions need generalization
Needs extensive testing and validation

Workaround: Advanced users can use RxInfer.jl directly, but without NimbusSDK’s convenience functions

Online Adaptation (Partial)

Current: Offline Calibration

# Collect all calibration data first
calib_data = collect_all_trials()

# Then calibrate
personalized = calibrate_model(baseline, calib_data)

Future: Online Learning

Online Adaptation (Future)

Concept: Continuously update model during use

# Hypothetical future API
adaptive_session = start_adaptive_session(model, metadata)

for chunk in eeg_stream
    result = process_chunk_adaptive(adaptive_session, chunk)
    
    # Model automatically updates based on feedback
    provide_feedback(adaptive_session, result, ground_truth)
end

Challenges:

Risk of catastrophic forgetting
Requires reliable feedback signal
Stability vs. adaptivity tradeoff

Status: Research prototype; not production-ready

Non-Gaussian Models (Future)

Current: Gaussian Assumptions

RxLDA and RxGMM assume Gaussian feature distributions:

p(x|c) = \mathcal{N}(x | \mu_c, \Sigma_c)

This works well for many BCI features (CSP, bandpower, ERP amplitudes).

Future: Flexible Distributions

Non-Gaussian Models (Future)

Potential additions:

Student-t distributions: Robust to outliers
Mixture models: Flexible multi-modal distributions
Non-parametric methods: No distributional assumptions

Status: RxGMM provides some flexibility; more options under consideration

Sparse Models (Conceptual)

Concept: Automatic Feature Selection

Models that automatically select relevant features:

p(x|\text{class}, \text{active features})

Potential Benefits:

Reduced overfitting
Interpretability (which features matter?)
Reduced computation

Status: Preprocessing feature selection recommended; sparse models not implemented

Practical Recommendations

For Current SDK Users

Best Practices with RxLDA/RxGMM:

Good feature extraction is more important than complex models
- Use CSP for motor imagery
- Extract ERP amplitudes for P300
- Compute bandpower for frequency-based paradigms
Start simple: RxLDA often sufficient
- Try RxLDA first
- Use RxGMM if classes overlap significantly
Calibrate: Subject-specific adaptation improves accuracy
- Collect 20-50 calibration trials
- Use calibrate_model() for personalization
Monitor uncertainty: Use confidence scores
- Reject low-confidence predictions
- Adapt thresholds based on application

For Advanced Users

If you need features not in NimbusSDK:

Use RxInfer.jl directly for custom factor graphs
Preprocess thoughtfully to create informative features
Consider hybrid approaches (e.g., RxLDA for classification + post-hoc smoothing)
Contribute: Open-source contributions welcome!

Research Collaborations

Partner With Us

Interested in advanced BCI models?We’re actively researching:

Multi-modal fusion
Online adaptation
Transfer learning
Novel paradigms

Contact: research@nimbusbci.com
GitHub: github.com/nimbusbci

Implementation Timeline

Feature	Status	Estimated Timeline
RxLDA	✅ Available	Shipped
RxGMM	✅ Available	Shipped
Model Calibration	✅ Available	Shipped
Streaming Inference	✅ Available	Shipped
HMM Models	⏳ Planned	2025 Q4
Kalman Filters	⏳ Planned	2026 Q1
Online Adaptation	🔬 Research	TBD
Multi-Modal Fusion	🔬 Research	TBD
Custom Factor Graphs	💡 Proposed	TBD

Next Steps

Current Models

Learn about RxLDA and RxGMM

Working Examples

See current SDK in action

Training Guide

Train models on your data

API Reference

Complete SDK documentation

Philosophy: NimbusSDK prioritizes proven, production-ready techniques (RxLDA, RxGMM) over experimental methods. Advanced features will be added incrementally as they mature and demonstrate clear benefits for real-world BCI applications.

Getting started

API documentation

Core concepts

Model specification

Inference configuration

BCI Models

Examples & tutorials

Development

​Advanced Modeling Techniques

​Current Capabilities vs. Future Work

✅ Currently Available

⏳ Future Directions

​Multi-Modal Sensor Fusion (Future)

​Concept: EEG + EMG Fusion

​Concept: EEG + fNIRS Integration

​Temporal Dynamics (Future)

​Hidden Markov Models (HMM)

​Kalman Filtering

​Hierarchical Models (Conceptual)

​Multi-Level Modeling

​Example: Population Prior + Subject Adaptation

​Advanced Uncertainty Quantification (Partial)

​Current Capabilities

​Future Directions

Advanced Uncertainty (Future)

​Transfer Learning (Partial)

​Current: Model Calibration

​Future: Advanced Transfer

Advanced Transfer Learning (Future)

​Custom Factor Graphs (Future)

​Concept

​Online Adaptation (Partial)

​Current: Offline Calibration

​Future: Online Learning

Online Adaptation (Future)

​Non-Gaussian Models (Future)

​Current: Gaussian Assumptions

​Future: Flexible Distributions

Non-Gaussian Models (Future)

​Sparse Models (Conceptual)

​Concept: Automatic Feature Selection

​Practical Recommendations

​For Current SDK Users

​For Advanced Users

​Research Collaborations

Partner With Us

​Implementation Timeline

​Next Steps

Current Models

Working Examples

Training Guide

API Reference

Advanced Modeling Techniques

Current Capabilities vs. Future Work

Multi-Modal Sensor Fusion (Future)

Concept: EEG + EMG Fusion

Concept: EEG + fNIRS Integration

Temporal Dynamics (Future)

Hidden Markov Models (HMM)

Kalman Filtering

Hierarchical Models (Conceptual)

Multi-Level Modeling

Example: Population Prior + Subject Adaptation

Advanced Uncertainty Quantification (Partial)

Current Capabilities

Future Directions

Transfer Learning (Partial)

Current: Model Calibration

Future: Advanced Transfer

Custom Factor Graphs (Future)

Concept

Online Adaptation (Partial)

Current: Offline Calibration

Future: Online Learning

Non-Gaussian Models (Future)

Current: Gaussian Assumptions

Future: Flexible Distributions

Sparse Models (Conceptual)

Concept: Automatic Feature Selection

Practical Recommendations

For Current SDK Users

For Advanced Users

Research Collaborations

Implementation Timeline

Next Steps