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Probabilistic AI for Brain-Computer Interfaces

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The Uncertainty Challenge in BCI

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Neural Signal Variability

• Signal-to-noise ratio: Neural signals often have poor SNR, especially in non-invasive recordings
• Temporal dynamics: Brain states change continuously, affecting signal patterns
• Individual differences: Each person’s brain produces unique signal characteristics
• Environmental factors: Movement, attention, fatigue all affect neural recordings

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Why Deterministic Approaches Fail

• Extract features from neural signals
• Apply a classifier (SVM, LDA, etc.)
• Return a single class prediction
• No confidence measure or alternatives provided

• Overconfident predictions: Always returns an answer, even with poor signal quality
• No adaptability: Cannot adjust when brain states change
• Binary decisions: Cannot express uncertainty or multiple possibilities
• Poor generalization: Fails when conditions differ from training data

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Probabilistic AI Solution

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Bayesian Inference for BCI

using NimbusSDK

# Authenticate and load model
NimbusSDK.install_core("your-api-key")
model = load_model(RxLDAModel, "motor_imagery_4class_v1")

# Run inference - returns full posterior distribution
results = predict_batch(model, bci_data)

# Access predictions with confidence
for (i, pred) in enumerate(results.predictions)
    confidence = results.confidences[i]
    posterior = results.posteriors[:, i]  # Full distribution over classes
    
    println("Prediction: $pred")
    println("Confidence: $(round(confidence, digits=3))")
    println("Posterior distribution: $(round.(posterior, digits=3))")
    
    # Make confidence-based decisions
    if confidence < 0.7
        println("⚠️  Low confidence - consider rejecting this trial")
    end
end

• Returns full posterior probability distribution, not just a single prediction
• Confidence scores for each prediction
• Can identify uncertain trials and request clarification
• Gracefully handles poor signal quality

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Key Advantages

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Probabilistic Models for Neural Signals

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State-Space Models

Hidden Brain State → Observable Neural Signal
     ↓                        ↓
  Intent/Command         EEG/EMG/etc.

• Infer hidden states from noisy observations
• Track state changes over time
• Predict future states for proactive responses
• Handle missing data gracefully

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Hierarchical Models

• Local neural populations: Individual electrode signals
• Regional networks: Coordinated activity across brain regions
• Global brain states: Overall attention, arousal, cognitive load
• Behavioral intentions: High-level goals and commands

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Real-World Benefits

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Medical Applications

using NimbusSDK

# Assess signal quality for each trial
quality = assess_trial_quality(results)

# Adaptive responses based on confidence
if quality.confidence_acceptable
    # High confidence - execute command immediately
    execute_command(results.predictions[end])
elseif results.confidences[end] > 0.5
    # Medium confidence - show top 2 alternatives
    show_alternatives(results.predictions[end], results.posteriors[:, end])
else
    # Low confidence - request clearer signal
    display_message("Please focus and try again")
end

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Consumer Applications

using NimbusSDK

# Initialize streaming for real-time gaming
session = init_streaming(model, metadata)

# Process continuous stream with quality monitoring
for chunk in game_stream
    chunk_result = process_chunk(session, chunk)
    
    # Adaptive difficulty based on performance
    if chunk_result.confidence > 0.85
        # User performing well - increase challenge
        increase_game_difficulty()
    elseif chunk_result.confidence < 0.55
        # User struggling - provide hints or reduce challenge
        adjust_game_assistance()
    end
end

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Research Applications

using NimbusSDK

# Track performance metrics in real-time
tracker = OnlinePerformanceTracker(window_size=50)

for (prediction, true_label, confidence) in zip(results.predictions, labels, results.confidences)
    metrics = update_and_report!(tracker, prediction, true_label, confidence)
    
    println("Running accuracy: $(round(metrics.accuracy * 100, digits=1))%")
    println("Mean confidence: $(round(metrics.mean_confidence, digits=3))")
    
    # Provide adaptive feedback
    if metrics.accuracy < 0.6
        println("⚠️  Consider recalibration")
    end
end

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Technical Implementation

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RxInfer.jl Message Passing

1. Factor graphs represent probabilistic relationships between neural features and brain states
2. Variational message passing propagates information efficiently through the graph
3. Reactive updates handle streaming data in real-time with minimal latency
4. Automatic inference - RxInfer generates efficient inference algorithms automatically

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Performance Characteristics

• Real-time processing: 10-25ms inference latency per trial
• Batch efficiency: Process hundreds of trials in seconds
• Memory efficiency: Fixed memory usage for streaming
• Scalability: Handle 100+ features without performance degradation

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Supported Models

• API Name: RxLDAModel
• Shared covariance across classes
• Fast inference and training
• Best for well-separated classes
• Typical accuracy: 70-90% (motor imagery)

• API Name: RxGMMModel
• Class-specific covariances
• More flexible for overlapping distributions
• Slightly slower than Bayesian LDA
• Better for complex distributions
• Handles complex class structures
• Slightly slower but more robust

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Getting Started