| 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__(self, n_channels, n_samples, n_classes):
super().__init__()
self.temporal_conv = nn.Conv2d(1, 8, (1, 64), padding=(0, 32))
self.spatial_conv = nn.Conv2d(8, 16, (n_channels, 1))
self.separable_conv = nn.Conv2d(16, 16, (1, 16), padding=(0, 8))
def forward(self, x):
x = self.temporal_conv(x)
x = self.spatial_conv(x)
x = self.separable_conv(x)
return x.view(x.size(0), -1)
Step 2: Design Quantum Encoding Layer
n_qubits = 4
dev = qml.device("default.qubit", wires=n_qubits)
@qml.qnode(dev)
def quantum_eeg_layer(features, weights):
"""Quantum encoding for EEG features"""
for i in range(n_qubits):
qml.RY(features[i] * np.pi, wires=i)
for layer in range(n_layers):
for i in range(n_qubits):
qml.Rot(weights[layer, i, 0],
weights[layer, i, 1],
weights[layer, i, 2], wires=i)
for i in range(n_qubits - 1):
qml.CNOT(wires=[i, i+1])
qml.CNOT(wires=[n_qubits-1, 0])
return [qml.expval(qml.PauliZ(i)) for i in range(n_qubits)]
Step 3: Create QEEGNet Hybrid Model
class QEEGNet(nn.Module):
"""Quantum-enhanced EEGNet"""
def __init__(self, backbone, quantum_layer, n_classes):
super().__init__()
self.backbone = backbone
self.quantum = quantum_layer
self.q_weights = nn.Parameter(torch.randn(n_layers, n_qubits, 3) * 0.1)
self.classifier = nn.Linear(n_qubits, n_classes)
def forward(self, x):
features = self.backbone(x)
features = features[:, :n_qubits]
quantum_features = self.quantum(features, self.q_weights)
return self.classifier(torch.stack(quantum_features).T)
Step 4: Train QEEGNet
def train_qeegnet(model, train_data, epochs=100):
optimizer = torch.optim.Adam(model.parameters(), lr=0.001)
criterion = nn.CrossEntropyLoss()
for epoch in range(epochs):
for batch_x, batch_y in train_data:
optimizer.zero_grad()
outputs = model(batch_x)
loss = criterion(outputs, batch_y)
loss.backward()
optimizer.step()
return model
2. Quantum Feature Extraction
Key Advantage: Quantum circuits can capture complex correlations in EEG signals
Quantum Encoding Strategies
- Angle Encoding: Direct feature → rotation angle mapping
- Amplitude Encoding: Normalize features as quantum state amplitudes
- Basis Encoding: Binary encoding for discrete features
Feature Dimension Handling
def dimension_adapter(features, target_dim=n_qubits):
"""Adapt high-dimensional EEG features to quantum layer"""
pca = PCA(n_components=target_dim)
return pca.fit_transform(features)
return features[:, :target_dim]
projection = nn.Linear(features.shape[1], target_dim)
return projection(features)
3. BCI Applications
Motor Imagery Classification
model = QEEGNet(
backbone=EEGNetBackbone(n_channels=64, n_samples=512),
quantum_layer=quantum_eeg_layer,
n_classes=4
)
Emotion Recognition
model = QEEGNet(
backbone=EEGNetBackbone(n_channels=32, n_samples=256),
quantum_layer=quantum_eeg_layer,
n_classes=2
)
4. Optimization Strategies
Noise Handling
dev = qml.device("default.qubit", wires=n_qubits, shots=1000)
@qml.qnode(dev)
def quantum_eeg_layer_robust(features, weights):
return [qml.expval(qml.PauliZ(i)) for i in range(n_qubits)]
Batch Processing
def batch_quantum_processing(model, eeg_batch):
"""Process multiple EEG samples efficiently"""
results = []
for sample in eeg_batch:
quantum_out = model.quantum(sample, model.q_weights)
results.append(quantum_out)
return torch.stack(results)
Key Research Findings
From QEEGNet papers:
- arXiv:2503.00080: QEEGNet extended to investigate generalization across multiple EEG datasets (cognitive and motor tasks). Hybrid quantum-classical architectures require further optimization to fully leverage quantum advantages in EEG processing. Cross-task and cross-dataset generalization remains a challenge.
- arXiv:2407.19214: Quantum encoding improves EEG feature extraction efficiency. Hybrid architecture outperforms pure classical on complex EEG tasks.
- Quantum layer reduces computational overhead for high-dimensional data
- Suitable for BCI systems requiring real-time processing
- Pitfall: Quantum advantage must be proven against classical baselines, not shown in isolation
Framework Compatibility
PennyLane (Recommended)
import pennylane as qml
Qiskit Machine Learning
from qiskit_machine_learning import QNN
TensorFlow Quantum
import tensorflow_quantum as tfq
Resources
references/
qeegnet_paper.md: QEEGNet paper summary (arXiv:2407.19214)
eeg_encoding.md: Quantum encoding strategies for EEG
bci_applications.md: BCI use cases and implementations
assets/
qeegnet_template.py: QEEGNet boilerplate code
Related Skills
quantum-neural-hybrid: General quantum-classical hybrid architectures
quantum-neuroscience-analysis: Quantum neuroscience research patterns
spikingjelly-framework: Alternative neuromorphic approach (spiking neural networks)
References
- QEEGNet: Quantum Machine Learning for Enhanced Electroencephalography Encoding (arXiv:2407.19214)
- EEGNet: A Compact Convolutional Neural Network for EEG-based BCIs
- Transfer learning in hybrid classical-quantum neural networks (arXiv:1912.08278)
This skill enables quantum-enhanced EEG analysis for neuroscience and BCI applications.