EEG-Signals

🧠 NeuroScan — Epileptic Seizure Detection System

A real-time, AI-powered EEG analysis platform for epileptic seizure detection using a hybrid CNN-Transformer architecture with Continuous Wavelet Transform (CWT) feature engineering.

Overview • Architecture • Pipeline • Model • Quick Start • API • Dataset

📌 Overview

NeuroScan is an end-to-end clinical-grade EEG analysis system that classifies brain signals into four neurological states in real time:

State	Description	Clinical Significance
🟢 Normal	Healthy background cortical activity	No intervention required
🟡 Preictal	Pre-seizure warning phase	Alert clinician, prepare rescue medication
🔴 Seizure (Ictal)	Active epileptic seizure event	Immediate clinical response required
🟠 Postictal	Post-seizure recovery state	Monitor for secondary events

Key Features:

🔬 Real CNN-Transformer hybrid model trained on CHB-MIT Scalp EEG Database
🌊 Continuous Wavelet Transform (CWT) scalogram visualization
📋 Auto-generated clinical reports with confidence scoring
🔐 Session-based clinician authentication (8-hour tokens)
📁 Supports .edf (medical), .csv, and .txt EEG file formats
🎮 Built-in demo mode with synthetic signals for all 4 seizure states

🏗 High-Level Architecture

flowchart TD
    subgraph INPUT["📥  INPUT SOURCES"]
        A1["🗂️ EDF File\n(.edf medical format)"]
        A2["📄 CSV / TXT File"]
        A3["🔴 Live Demo Signal\n(Normal / Preictal / Seizure / Postictal)"]
        A4["📡 JSON API Call"]
    end

    subgraph S1["⚙️  STAGE 1 — Signal Preprocessing"]
        B1["Notch Filter\n60 Hz removal"]
        B2["Bandpass Filter\n0.5 – 50 Hz"]
        B3["Epoch Extraction\n2s windows @ 256 Hz"]
        B4["Artifact Rejection\n> 300 µV discarded"]
        B5["Z-Score Normalization\nper channel"]
    end

    subgraph S2["🌊  STAGE 2 — CWT Feature Engineering"]
        C1["Complex Morlet Wavelet\ncmor1.5-1.0"]
        C2["Scalogram Generation\n22 ch × 50 freq × 512 time"]
        C3["PNG Heatmaps\n(base64, for dashboard)"]
    end

    subgraph S3["🤖  STAGE 3 — CNN-Transformer Model"]
        D1["2D CNN Blocks\nSpatial Feature Extraction"]
        D2["Reshape Bridge\n→ Sequence of 128 time steps"]
        D3["Transformer Encoder\n2 layers · 8 heads · d=768"]
        D4["Classifier Head\nSoftmax → 4 classes"]
    end

    subgraph S4["📋  STAGE 4 — Clinical Output"]
        E1["Prediction Label\n+ Confidence Scores"]
        E2["Medical Report\nAuto-generated notes"]
        E3["Clinical Dashboard\nLive waveform · Scalograms"]
    end

    A1 & A2 & A3 & A4 --> B1
    B1 --> B2 --> B3 --> B4 --> B5
    B5 --> C1 --> C2 --> C3
    C2 --> D1 --> D2 --> D3 --> D4
    D4 --> E1
    C3 --> E3
    E1 --> E2 --> E3

🔄 Data Flow

sequenceDiagram
    actor C as 👨‍⚕️ Clinician
    participant D as 🖥️ Dashboard
    participant A as 🐍 Flask API
    participant M as 🤖 CNN-Transformer

    C->>D: Login (Practice ID + Key)
    D->>A: POST /api/auth
    A-->>D: Session Token (8 hr)

    C->>D: Click "Seizure" demo button
    D->>A: GET /api/demo-signal/seizure
    A-->>D: Synthetic 22ch × 512 EEG signal

    D->>A: POST /api/predict
    Note over A: Stage 1 — Notch · Bandpass · Epoch · Z-Score
    Note over A: Stage 2 — CWT → (22, 50, 512) scalogram
    A->>M: Forward pass
    Note over M: CNN → Transformer → Softmax
    M-->>A: [0.01, 0.01, 0.97, 0.01]
    Note over A: Stage 4 — Generate clinical report

    A-->>D: prediction + confidences + scalograms + notes
    D-->>C: Dashboard updates in real time

🔬 Pipeline Stages

Stage 1 — Data Ingestion & Signal Preprocessing

flowchart LR
    RAW["Raw EEG\n22ch × N samples\n(µV)"]
    N["Notch Filter\n60 Hz IIR\nRemoves power-line noise"]
    BP["Bandpass Filter\n0.5–50 Hz Butterworth 4th order\nRetains brain frequencies"]
    EP["Epoch Extraction\n2s sliding window · 512 samples\n50% overlap"]
    AR["Artifact Rejection\nRejects epochs > 300 µV\n(muscle/electrode pop)"]
    ZS["Z-Score Normalization\nMean=0 · Std=1\nper channel"]
    OUT["Clean Epoch\n22 × 512"]

    RAW --> N --> BP --> EP --> AR --> ZS --> OUT

Supported Input Formats:

Format	Parser	Use Case
`.edf`	MNE-Python	Medical EEG files (European Data Format)
`.csv` / `.txt`	Built-in CSV reader	Exported recordings, research data
JSON body	Direct API input	Real-time streaming, integrations
Demo button	Synthetic generator	Testing & demonstration

Stage 2 — CWT Scalogram Generation

flowchart LR
    SIG["1D Signal\nper channel\n512 samples"]
    CWT["Complex Morlet CWT\ncmor1.5-1.0\n1–50 Hz · 50 bins"]
    SCALO["2D Scalogram\n50 freq × 512 time\nper channel"]
    TENSOR["Final Tensor\n22 × 50 × 512\nall channels"]
    PNG["PNG Heatmaps\nbase64 encoded\nfor dashboard"]

    SIG --> CWT --> SCALO
    SCALO -->|"×22 channels"| TENSOR
    TENSOR --> PNG

Why CWT? Raw EEG is a noisy 1D voltage trace. The CWT decomposes it into a 2D frequency-time heatmap where seizure-specific patterns — rhythmic 3–8 Hz bursts and spike-wave complexes — become visually distinct and far easier for the CNN to detect.

Stage 3 — CNN-Transformer Hybrid Model

flowchart TD
    IN["Input Tensor\nBatch × 22 × 50 × 512\n22ch · 50 freq bins · 512 time"]

    subgraph CNN["🖼️  2D CNN — Spatial Feature Extraction"]
        C1["Conv2D 22→32 · BatchNorm · ReLU\nMaxPool 2×2\n→ Batch × 32 × 25 × 256"]
        C2["Conv2D 32→64 · BatchNorm · ReLU\nMaxPool 2×2\n→ Batch × 64 × 12 × 128"]
    end

    BRIDGE["🔀 Reshape Bridge\nPermute + Flatten\n→ Batch × 128 timesteps × 768 features"]

    subgraph TF["⚡  Transformer Encoder — Temporal Attention"]
        T1["Self-Attention Layer 1\n8 heads · d_model=768"]
        T2["Self-Attention Layer 2\n8 heads · d_ff=2048"]
    end

    POOL["📉 Global Average Pooling\nMean over 128 timesteps\n→ Batch × 768"]

    subgraph CLS["🎯  Classifier Head"]
        L1["Linear 768→256 · ReLU · Dropout 50%"]
        L2["Linear 256→4 · Softmax"]
    end

    OUT["Output\n4 Class Probabilities\nNormal · Preictal · Seizure · Postictal"]

    IN --> C1 --> C2 --> BRIDGE --> T1 --> T2 --> POOL --> L1 --> L2 --> OUT

Training Details:

Parameter	Value
Dataset	CHB-MIT Scalp EEG Database (24 pediatric patients)
Optimizer	AdamW · lr=1e-4 · weight_decay=1e-2
Scheduler	ReduceLROnPlateau · factor=0.5 · patience=3
Loss	Cross-Entropy with class weights `[0.1, 0.4, 0.9, 0.4]`
Regularization	Dropout 50% · BatchNorm · weight decay
Precision	Mixed FP16 (AMP on GPU)

Why class weighting? In real EEG data, seizures are <1% of recording time. Without weighting the model just predicts “Normal” always and still gets 99% accuracy — being clinically useless. Heavier weight on seizure class forces the model to focus on rare events.

Stage 4 — Clinical Dashboard & Output

Output	Description
Classification	Top predicted class with confidence %
Confidence Scores	Full probability distribution across all 4 classes
CWT Scalograms	Heatmap images from channels FP1-F7 and C3-P3
Signal Preview	512-point raw waveform for visual inspection
Medical Report	Auto-generated clinical notes with recommended actions
Inference Time	End-to-end pipeline latency in milliseconds

🚀 Quick Start

Prerequisites

Python 3.10+
pip

1. Clone the Repository

git clone https://github.com/ScriptOrbit-132/EEG-Signals.git
cd EEG-Signals

2. Install Dependencies

pip install -r requirements.txt

3. Run the Server

python code/app.py

You should see:

========================================================
  NeuroScan EEG Analysis API - v1.0
  Model mode : real
  Classes    : ['Normal', 'Preictal', 'Seizure (Ictal)', 'Postictal']
  -------------------------------------------------
  Demo credentials:
    DEMO_CLINIC          -> key: NS2026
    chb_research         -> key: CHB_MIT_001
    neuroscan_dev        -> key: DEV_9999
  -------------------------------------------------
  Open: http://localhost:5000
========================================================

4. Open the Dashboard

Navigate to http://localhost:5000 and login:

Practice ID: DEMO_CLINIC
Access Key: NS2026

🎮 Demo

Button	Simulates	Expected Confidence
🟢 Normal	Healthy resting-state EEG	~97% Normal
🟡 Preictal	Pre-seizure warning signals	~89% Preictal
🔴 Seizure	Active ictal event with spike-wave bursts	~97% Seizure
🟠 Postictal	Post-seizure delta slowing	~93% Postictal

File Upload: Drag-and-drop a .edf, .csv, or .txt file for real inference against the trained model.

📁 Project Structure

EEG-Signals/
│
├── 📄 neuroscan_dashboard.html       # Frontend — clinical dashboard UI
├── 🧠 backend_model_completed.pt     # Pre-trained model weights (~45 MB)
├── 📊 processed_metadata.csv         # Training data index mapping
├── 📦 requirements.txt               # Python dependencies
├── 📖 README.md                      # This file
├── 📋 SETUP.md                       # Quick setup guide
│
└── code/
    ├── 🐍 app.py                     # Flask API — all 4 pipeline stages
    ├── 🤖 model.py                   # EEG_2D_Hybrid_Model definition
    ├── 🌊 preprocess_features.py     # CWT feature extraction (train + inference)
    ├── 🔧 preprocess_seizure_only.py # Targeted seizure data preprocessor
    ├── 🔍 find_hardest_seizure.py    # Edge case seizure locator
    ├── 🧪 test_manual_seizure.py     # Model accuracy verification
    ├── 📈 train_full.py              # Full training pipeline
    ├── ⚡ train_optimized.py          # Memory-optimized training
    ├── 🔄 run_pipeline.py            # End-to-end pipeline runner
    ├── 📊 resource_monitor.py        # Hardware safety guard
    ├── ⚙️ training_config.yaml       # Resource threshold config
    └── 🌐 render.yaml                # Render.com deployment config

🔌 API Reference

Authentication

POST /api/auth
Content-Type: application/json

{ "practice_id": "DEMO_CLINIC", "key": "NS2026" }

Predict — JSON

POST /api/predict
Content-Type: application/json
X-Auth-Token: <token>

{ "signal_data": [[...22 channels...]], "patient_id": "patient_001" }

Predict — File Upload

POST /api/predict
Content-Type: multipart/form-data
X-Auth-Token: <token>

file: eeg_recording.edf

Demo Signal

GET /api/demo-signal/{normal|preictal|seizure|postictal}

Health Check

GET /api/health

🧬 Dataset

CHB-MIT Scalp EEG Database (PhysioNet):

24 pediatric patients with intractable epilepsy
22-channel EEG (international 10-20 system)
256 Hz sampling rate · ~983 hours · 198 annotated seizures

FP1-F7  F7-T7  T7-P7  P7-O1  FP1-F3  F3-C3  C3-P3  P3-O1
FP2-F4  F4-C4  C4-P4  P4-O2  FP2-F8  F8-T8  T8-P8  P8-O2
FZ-CZ   CZ-PZ  P7-T7  T7-FT9 FT9-FT10 FT10-T8

Shoeb, A. H. (2009). Application of Machine Learning to Epileptic Seizure Onset Detection and Treatment. PhD Thesis, MIT.

⚙️ Tech Stack

Layer	Technology	Role
Backend	Flask 3.0+	REST API server
Frontend	HTML / CSS / JS	Clinical dashboard UI
Deep Learning	PyTorch 2.2+	CNN-Transformer model
Signal Processing	SciPy	IIR / Butterworth filters
Wavelet Transform	PyWavelets	CWT scalogram generation
EEG Parsing	MNE-Python	Medical EDF file reader
Visualization	Matplotlib	Scalogram PNG rendering
Deployment	Gunicorn + Render	Production WSGI server

📚 References

Abiyev, R. et al. (2020). Identification of Epileptic EEG Signals Using CNN. Applied Sciences, 10(12), 4089.
Shoeb, A. H. (2009). Application of Machine Learning to Epileptic Seizure Onset Detection and Treatment. PhD Thesis, MIT.
Goldberger, A. et al. (2000). PhysioBank, PhysioToolkit, and PhysioNet. Circulation, 101(23), e215–e220.

📜 License

MIT License — see LICENSE for details.

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