Dynamic Alignment Between Neural Oscillations and Adaptive Artificial Systems

 

Coherencia Predictiva EEG–AGI (CPEA)

Dynamic Alignment Between Neural Oscillations and Adaptive Artificial Systems

 

Conceptual Framing

Traditional Brain–Computer Interfaces treat EEG signals as static feature vectors.
CPEA proposes a different paradigm:

The relevant question is not only what class does this signal represent?
It is does the internal latent state of the model evolve coherently with the neural oscillatory structure?

This Space demonstrates a system where:

• EEG signals are processed spectro-temporally.

• A deep neural architecture generates a dynamic latent representation.

• A coherence metric measures phase alignment between EEG activity and latent dynamics.

• A continual learning module updates parameters online when misalignment is detected.

The system therefore operates as a dynamically coupled structure.

What This Demo Shows

This Space provides an interactive simulation of:

• Real-time (or pseudo-real-time) EEG inference.

• Latent embedding evolution.

• Coherence estimation.

• Adaptive update cycles.

Displayed outputs include:

1. Raw EEG segment (time domain).

2. Time–frequency representation.

3. Latent embedding trajectory.

4. Coherence index $C(t)$.

5. Adaptation trigger events.

The user can introduce:

• Noise perturbation.

• Cross-subject transfer.

• Spectral attenuation.

This allows direct observation of structural resilience.

Architectural Overview

Signal Processing Layer

• Band-pass filtering (1–40 Hz).

• Artifact attenuation (ICA-based preprocessing).

• Continuous wavelet transform.

Deep Representation Layer

• Temporal convolution block.

• Lightweight Transformer encoder.

• Latent embedding space.

Coherence Module

• Hilbert transform-based instantaneous phase extraction.

• Cosine phase similarity metric.

• Continuous coherence scoring.

Continual Learning Core

• Elastic Weight Consolidation.

• Selective replay buffer.

• Low-importance parameter adaptation.

The coherence metric feeds back into optimization.
When coherence drops below threshold, adaptive update activates.

Mathematical Core

Let:

• $X(t)$ be multichannel EEG.

• $Z(t)$ latent embedding.

• $\mathcal{H}$ Hilbert transform.

Coherence:

$$C(t) = \frac{|\langle \mathcal{H}(X(t)), \mathcal{H}(Z(t)) \rangle|}{\|\mathcal{H}(X(t))\|\|\mathcal{H}(Z(t))\|}$$

Training objective:

$$\mathcal{L} = \mathcal{L}_{task} - \lambda C(t) + \gamma \mathcal{L}_{EWC}$$

This demo visualizes how $C(t)$ evolves dynamically.

Why This Matters in BCI

Standard EEG models degrade due to:

• Intersubject variability.

• Fatigue effects.

• Spectral drift.

• Noise contamination.

CPEA introduces an internal structural indicator: coherence.

Rather than detecting only classification error, the system detects dynamic misalignment before performance collapse.

This anticipatory adjustment represents the core innovation.

Experimental Modes Available in the Space

Mode A — Baseline Inference

Observe coherence and accuracy without adaptation.

Mode B — Adaptive Mode

Online updates enabled; coherence-driven adjustment.

Mode C — Noise Injection

Simulated EMG contamination.

Mode D — Cross-Subject Transfer

Pretrained model adapted to unseen subject.

Each mode reports:

• Accuracy.

• Coherence index.

• Adaptation latency.

• Parameter drift magnitude.

Interpretation Guidelines

High coherence with stable accuracy indicates structural alignment.

Decreasing coherence with stable accuracy may indicate impending degradation.

Rapid recovery after perturbation suggests effective continual learning integration.

The demo encourages examination of dynamic stability rather than static metrics alone.

Reproducibility

The Space includes:

• Dataset references (public EEG datasets).

• Model architecture specification.

• Hyperparameters.

• Training protocol.

All experiments are reproducible from the linked repository.

Limitations

• Simulated nea

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  r-real-time; not hardware-embedded BCI.

• Limited electrode montage in demo dataset.

• Simplified artifact handling for computational feasibility.

These constraints are explicit to preserve transparency.

Technical Positioning

CPEA is positioned at the intersection of:

• Neurodynamics.

• Deep representation learning.

• Continual learning.

• Adaptive BCI systems.

It is not merely a classifier enhancement;
it is a structural coupling framework.

Suggested Structure for the Hugging Face Space UI

Tab 1 — Overview
Concept, equations, architecture diagram.

Tab 2 — Live Demo
Interactive inference and coherence display.

Tab 3 — Experiments
Noise, transfer, adaptation tests.

Tab 4 — Technical Details
Loss functions, training pipeline.

Tab 5 — References
Curated scientific sources.