quantum-eeg-foundation

name

quantum-eeg-foundation

description

Quantum-enhanced EEG signal analysis and neural network foundation model skill. Implements quantum-classical hybrid architectures for brain signal processing, combining quantum encoding layers with classical EEGNet for improved feature extraction from high-dimensional EEG data. Use when developing BCI systems, EEG analysis pipelines, quantum-neuroscience applications, or quantum machine learning for brain signal processing.

Quantum EEG Foundation

Overview

Enables quantum-enhanced EEG signal analysis through hybrid quantum-classical neural networks. Combines quantum computing advantages with established EEGNet architectures for improved encoding of complex, high-dimensional brain signals. Based on QEEGNet (arXiv:2407.19214) research pattern.

Workflow Decision Tree

EEG Analysis Request → Identify Task Type
├── BCI System Development → QEEGNet Hybrid Architecture
├── Signal Classification → Quantum Feature Extraction Workflow
├── Real-time Processing → Optimized Quantum Encoding
└── Research/Exploration → Full QEEGNet Implementation

1. QEEGNet Hybrid Architecture

Core Pattern: Integrate quantum encoding layer with classical EEGNet

Architecture Overview

Raw EEG Signal → Temporal Filter → Spatial Filter → Quantum Encoding Layer → Classification
                      ↓                ↓                    ↓                    ↓
                 EEGNet Core    EEGNet Core        Variational Circuit    Output Layer

Step 1: Prepare EEGNet Backbone

import torch
import pennylane as qml

class EEGNetBackbone(nn.Module):
    """Classical EEGNet architecture for EEG processing"""
    def ():
        ().__init__()
        
        .temporal_conv = nn.Conv2d(, , (, ), padding=(, ))
        
        .spatial_conv = nn.Conv2d(, , (n_channels, ))
        
        .separable_conv = nn.Conv2d(, , (, ), padding=(, ))
        
     ():
        x = .temporal_conv(x)
        x = .spatial_conv(x)
        x = .separable_conv(x)
         x.view(x.size(), -)