eeg-fuseformer-seizure-prediction

name: eeg-fuseformer-seizure-prediction description: Transformer-driven feature fusion framework for EEG-based seizure onset prediction. Combines CNN-LSTM (raw signal) + ResNet-18 (STFT) features via transformer encoder, achieves 98.85% recall on CHB-MIT dataset. version: 1.0 author: arxiv:2606.02166 arxiv_id: 2606.02166 date_created: 2026-06-04 date_updated: 2026-06-04 tags: [EEG, seizure prediction, transformer, feature fusion, CNN-LSTM, ResNet-18, STFT, epilepsy, medical AI] activation_keywords: [EEG seizure prediction, feature fusion, transformer EEG, CNN-LSTM EEG, medical AI, epilepsy prediction, CHB-MIT]

EEG-FuseFormer: Transformer-Driven Feature Fusion for Seizure Prediction

Source: arXiv:2606.02166 (June 2026) Categories: cs.LG (Machine Learning)

Abstract

Transformer-based feature fusion framework for seizure onset prediction that combines intermediate features from CNN-LSTM (raw EEG) and ResNet-18 (STFT EEG). Achieves 98.85% mean recall on CHB-MIT dataset, outperforming state-of-the-art methods with strong cross-patient generalization.

Clinical Problem

Epilepsy Challenge:

• 50+ million people affected globally
• Seizure unpredictability → risk mitigation difficult
• Accurate onset prediction → preventive interventions possible

Clinical Requirements:

1. High recall (catch seizures, avoid false negatives)
2. Cross-patient generalization (new patients without extensive training)
3. Real-time inference (computational efficiency)

Core Architecture

1. Dual Feature Extraction Pipeline

Branch 1: CNN-LSTM (Raw Signal)

Input: Raw EEG signals (multi-channel)
  ↓
CNN Layers: Spatial feature extraction
  - Conv1D kernels → local temporal patterns
  - Multi-scale filters → different frequency bands
  ↓
LSTM Layers: Temporal dynamics
  - Sequence modeling → seizure progression
  - Long-term dependencies → pre-ictal patterns
  ↓
Output: Spatial-temporal features F1

Branch 2: ResNet-18 (STFT Domain)

Input: STFT of EEG signals
  - Short-Time Fourier Transform
  - Time-frequency representation
  ↓
ResNet-18: Deep feature extraction
  - Residual blocks → complex spectral patterns
  - Skip connections → gradient flow
  ↓
Output: Frequency-domain features F2

2. Transformer Fusion Module

Feature Fusion Architecture:

F1 (CNN-LSTM)  F2 (ResNet-18)
    ↓              ↓
  Concatenate → [F1; F2]
    ↓
Transformer Encoder:
  - Self-attention → feature interactions
  - Multi-head → diverse fusion patterns
  - Positional encoding → temporal structure
    ↓
Fused Features: F_fused

Key Innovation:

• Intermediate fusion (not late concatenation)
• Transformer learns optimal feature combinations
• Captures cross-domain dependencies (time ↔ frequency)

3. Prediction Head

Architecture:

F_fused
  ↓
Fully Connected Dense Layers
  - Batch normalization
  - Dropout regularization
  ↓
Output: Seizure probability (binary classification)
  - Seizure vs. Non-seizure window

Implementation Details

Input Processing

EEG Signal Configuration:

• Channels: 23 scalp electrodes (CHB-MIT standard)
• Sampling rate: 256 Hz
• Window size: 5 seconds (1280 samples)
• Preprocessing: Bandpass filter (0.5-40 Hz), notch filter (60 Hz)

STFT Parameters:

• Window length: 256 samples (1 second)
• Hop size: 64 samples (0.25 seconds)
• FFT size: 256 → 129 frequency bins
• Output: Time-frequency spectrogram

Model Hyperparameters

CNN-LSTM Branch:

• CNN: 3 Conv1D layers (filters: 32, 64, 128)
• Kernel sizes: 3, 5, 7 (multi-scale)
• LSTM: 2 layers, hidden size: 128
• Dropout: 0.3

ResNet-18 Branch:

• Standard ResNet-18 architecture
• Input: 2-channel STFT (magnitude, phase)
• Modified first conv: (2, 7, 7) kernel

Transformer Encoder:

• Layers: 4
• Hidden dimension: 256
• Attention heads: 8
• Dropout: 0.2

Prediction Head:

• Dense layers: 128 → 64 → 1
• Activation: ReLU → ReLU → Sigmoid

Training & Validation

Dataset: CHB-MIT Scalp EEG

Statistics:

• Patients: 24 pediatric subjects
• Seizures: ~200 total events
• Recording duration: 1000+ hours
• Annotations: Seizure onset/offset timestamps

Training Protocol

Cross-Validation:

1. Within-Patient: Train/test split per patient
2. Cross-Patient: Train on N patients, test on unseen patient
3. Target Adaptation: Fine-tune pre-trained model on limited target data

Loss Function:

• Binary cross-entropy with class weighting
• Weight seizure samples higher (imbalanced classes)

Optimizer:

• Adam with learning rate 1e-4
• Weight decay 1e-5
• Batch size: 32

Performance Metrics

Results (CHB-MIT):

Metric              Value
Recall              98.85%
Precision           96.2%
F1-Score            97.5%
Specificity          94.1%
AUC-ROC              0.99

Comparison vs. Baselines:

• CNN-only: Recall 92.1%
• LSTM-only: Recall 89.3%
• Late fusion: Recall 95.4%
• EEG-FuseFormer: Recall 98.85% ✓

Cross-Patient Generalization

Target Adaptation Results:

• Fine-tune on 5 seizure windows from new patient
• Recall improves: +3-5% vs. direct cross-patient
• Demonstrates practical clinical deployment path

Key Innovations

1. Intermediate Feature Fusion - Transformer learns cross-domain interactions (time ↔ frequency)
2. Dual Representation - Raw signal + STFT captures complementary patterns
3. High Recall - 98.85% seizure detection (clinical priority)
4. Cross-Patient Adaptation - Practical deployment strategy

Practical Implementation

Deployment Considerations

Real-Time Inference:

• Hardware: CPU (Intel i7), GPU (NVIDIA RTX 3080), Edge (Jetson Nano)
• Latency:
  • CPU: 15ms per window
  • GPU: 3ms per window
  • Edge: 25ms per window
• Memory: ~500MB model weights

Production Pipeline:

Real-time EEG stream
  ↓
Windowing (5-sec buffer)
  ↓
Preprocessing (filtering)
  ↓
STFT computation
  ↓
Parallel inference (CNN-LSTM + ResNet)
  ↓
Transformer fusion
  ↓
Prediction (seizure probability)
  ↓
Alert system if prob > threshold

Code Structure (Conceptual)

class EEGFuseFormer(nn.Module):
    def __init__(self):
        # Branch 1: CNN-LSTM
        self.cnn = nn.Sequential(
            nn.Conv1d(23, 32, kernel_size=3),
            nn.Conv1d(32, 64, kernel_size=5),
            nn.Conv1d(64, 128, kernel_size=7)
        )
        self.lstm = nn.LSTM(128, 128, num_layers=2)
        
        # Branch 2: ResNet-18
        self.resnet = ResNet18(input_channels=2)
        
        # Transformer fusion
        self.transformer = nn.TransformerEncoder(
            d_model=256, nhead=8, num_layers=4
        )
        
        # Prediction head
        self.fc = nn.Sequential(
            nn.Linear(256, 128),
            nn.Linear(128, 64),
            nn.Linear(64, 1)
        )
    
    def forward(self, raw_eeg, stft_eeg):
        # Branch 1
        cnn_feat = self.cnn(raw_eeg)
        lstm_feat, _ = self.lstm(cnn_feat.permute(2, 0, 1))
        
        # Branch 2
        resnet_feat = self.resnet(stft_eeg)
        
        # Fusion
        fused = torch.cat([lstm_feat, resnet_feat], dim=-1)
        fused = self.transformer(fused)
        
        # Prediction
        output = self.fc(fused.mean(dim=1))
        return output

Clinical Applications

Use Cases

1. Hospital Monitoring

   • ICU epilepsy patients
   • Real-time seizure prediction → staff alert

2. Home Monitoring

   • Wearable EEG devices
   • Patient self-alert → preventive medication

3. Seizure Diary Validation

   • Compare predicted vs. patient-reported seizures
   • Detect subclinical events

4. Medication Timing

   • Predict seizure → administer rescue medication
   • Reduce emergency visits

Limitations

1. Dataset Size - CHB-MIT is small (24 patients)
2. Pediatric Focus - Adult EEG may differ
3. Scalp EEG Only - Intracranial EEG not tested
4. Binary Classification - Doesn't predict seizure type

Future Directions

1. Multi-Class Prediction: Seizure types (focal, generalized)
2. Multi-Center Validation: Larger diverse datasets
3. Temporal Prediction: Predict minutes before onset
4. Transformer Variants: Test attention patterns

Related Skills

• [[eeg-foundation-lrp-interpretability]]: EEG interpretation
• [[eeg-foundation-model-adapters]]: EEG domain adaptation
• [[seizure-suppression-hub-stimulation]]: Seizure intervention

References

• arXiv:2606.02166 - Original paper
• CHB-MIT Dataset: physionet.org/content/chbmit/
• Shoeb, A. (2009) - Application of machine learning to epileptic seizure detection

Activation Patterns

Use this skill when:

• Building EEG-based seizure prediction systems
• Designing feature fusion for multi-modal medical data
• Researching transformer applications in neuroscience
• Implementing cross-patient EEG models
• Clinical deployment of seizure prediction

Related arXiv searches:

• ti:EEG AND ti:seizure prediction
• ti:transformer AND ti:feature fusion
• ti:medical AI AND ti:epilepsy