wifi-densepose/v2/crates/ruv-neural/ruv-neural-decoder

ruv-neural-decoder

Cognitive state classification and BCI decoding from neural topology embeddings.

Overview

ruv-neural-decoder classifies cognitive states from brain graph embeddings and topology metrics. It provides multiple decoding strategies -- KNN classification from labeled exemplars, threshold-based rule systems, temporal transition detection, and clinical biomarker scoring -- plus an ensemble pipeline that combines all strategies for robust real-time brain-computer interface (BCI) output.

Features

• KNN decoder (knn_decoder): K-nearest neighbor classification using stored labeled embeddings from ruv-neural-memory; supports configurable k and distance metrics
• Threshold decoder (threshold_decoder): Rule-based classification from topology metric ranges (mincut value, modularity, efficiency, Fiedler value) with configurable TopologyThreshold bounds per cognitive state
• Transition decoder (transition_decoder): Detects cognitive state transitions from temporal topology dynamics; outputs StateTransition events matching known TransitionPattern templates
• Clinical scorer (clinical): ClinicalScorer for biomarker detection via deviation from healthy baseline distributions; flags abnormal topology patterns
• Ensemble pipeline (pipeline): DecoderPipeline combining all decoder strategies with confidence-weighted voting; produces DecoderOutput with classified state, confidence score, and contributing decoder votes

Usage

use ruv_neural_decoder::{
    KnnDecoder, ThresholdDecoder, TopologyThreshold,
    TransitionDecoder, ClinicalScorer, DecoderPipeline, DecoderOutput,
};
use ruv_neural_core::topology::{CognitiveState, TopologyMetrics};

// Threshold-based decoding from topology metrics
let mut decoder = ThresholdDecoder::new();
decoder.add_threshold(TopologyThreshold {
    state: CognitiveState::Focused,
    min_modularity: 0.3,
    max_modularity: 0.5,
    min_efficiency: 0.6,
    ..Default::default()
});
let state = decoder.decode(&metrics);

// KNN-based decoding from embeddings
let mut knn = KnnDecoder::new(5); // k=5
knn.add_exemplar(embedding, CognitiveState::Rest);
let predicted = knn.classify(&query_embedding);

// Transition detection from temporal sequences
let mut transition_decoder = TransitionDecoder::new();
if let Some(transition) = transition_decoder.check(&current_metrics) {
    println!("Transition: {:?} -> {:?}", transition.from, transition.to);
}

// Full ensemble pipeline
let mut pipeline = DecoderPipeline::new();
let output: DecoderOutput = pipeline.decode(&metrics, &embedding);
println!("State: {:?}, confidence: {:.2}", output.state, output.confidence);

API Reference

Module	Key Types
knn_decoder	KnnDecoder
threshold_decoder	ThresholdDecoder, TopologyThreshold
transition_decoder	TransitionDecoder, StateTransition, TransitionPattern
clinical	ClinicalScorer
pipeline	DecoderPipeline, DecoderOutput

Feature Flags

Feature	Default	Description
std	Yes	Standard library support
wasm	No	WASM-compatible decoding

Integration

Depends on ruv-neural-core for CognitiveState, TopologyMetrics, and NeuralEmbedding types. Consumes embeddings from ruv-neural-embed and topology results from ruv-neural-mincut. The KNN decoder can query stored exemplars from ruv-neural-memory.

License

MIT OR Apache-2.0