eeg-hopfield-emotion-energy

name	eeg-hopfield-emotion-energy
description	Energy landscapes for quantifying brain network stability during emotional processing. Uses Hopfield network energy framework to analyze EEG dynamics, mapping emotional states to attractor basins in brain network energy landscapes. Provides a physics-based framework for understanding emotional stability and transitions. Activation: energy landscape, Hopfield network emotion, brain network stability, emotional processing EEG, attractor dynamics, affective neuroscience, 能量景观, 情绪脑网络, 吸引子动力学
version	1.0.0
metadata	{"hermes":{"source_paper":"Energy Landscapes of Emotion: Quantifying Brain Network Stability during Emotional Processing","arxiv_id":"2603.27644","tags":["eeg","emotion","energy-landscape","hopfield","attractor","stability"]}}

Energy Landscapes of Emotion

Overview

Maps emotional states to attractor basins in brain network energy landscapes using Hopfield network framework. Provides quantitative measures of emotional stability, transition barriers, and metastability from EEG data.

Core Framework

Energy Function

E(x) = -½ Σᵢⱼ Wᵢⱼ xᵢ xⱼ + Σᵢ θᵢ xᵢ

Where:

• Wᵢⱼ = functional connectivity between brain regions
• xᵢ = activity of region i
• θᵢ = bias terms (regional excitability)

Emotional States as Attractors

• Each emotional state corresponds to a local energy minimum
• Depth of basin → emotional stability
• Barrier height → difficulty of emotional transition
• Basin width → emotional flexibility

Analysis Pipeline

class EmotionEnergyLandscape:
    def __init__(self, connectivity_matrix):
        self.W = connectivity_matrix  # from EEG functional connectivity
        self.energy_history = []
    
    def compute_energy(self, brain_state):
        """Compute Hopfield energy for a brain state."""
        return -0.5 * brain_state @ self.W @ brain_state.T
    
    def find_attractors(self, states):
        """Identify emotional attractor basins."""
        energies = [self.compute_energy(s) for s in states]
        # Cluster states by energy level
        attractors = cluster_by_energy(energies, states)
        return attractors
    
    def transition_barriers(self, attractor_a, attractor_b):
        """Compute energy barrier between emotional states."""
        path = minimum_energy_path(attractor_a, attractor_b)
        return max(path.energies) - attractor_a.energy

Key Metrics

1. Landscape Depth: Overall energy range → emotional range capacity
2. Basin Stability: Depth of each attractor → emotional persistence
3. Transition Rates: Barrier heights → emotional switching frequency
4. Metastability: Number and arrangement of basins → emotional complexity

Applications

• Depression: abnormally deep negative attractors
• Anxiety: shallow basins with low barriers
• Bipolar: multiple competing deep basins
• Emotional regulation therapy planning

Related Skills

• eeg-hopfield-emotion-energy, neural-dynamics-decision-making, kuramoto-brain-network