/Cognitive-States-Classification

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Cognitive State Analysis with EEGNet and ATCNet 🧠🔍

Overview

Welcome to the Cognitive State Analysis repository! Here, we explore the fascinating world of cognitive states using cutting-edge deep learning models: EEGNet and ATCNet.

1. Cognitive States 🌟

Understanding cognitive states is like deciphering the brain's secret code. Here are the key states we'll explore:

  • Attention 👀: Imagine your brain as a spotlight. When you're focused on a task or a captivating movie, your attention state is in full swing. EEG helps us peek into this spotlight.

  • Relaxation 😌: Ah, the blissful moments when stress melts away. Relaxation is essential for mental well-being. EEG captures those serene brain waves during meditation or a lazy Sunday afternoon.

  • Alertness 🚨: Ever had that "I need coffee" feeling? Alertness keeps us awake, vigilant, and ready to tackle challenges. EEG reveals the brain's wake-up call.

  • Meditation 🧘‍♂️: Picture a serene mountaintop. Meditation takes us there. EEG shows us the brain's zen mode.

2. EEG (Electroencephalogram) and EEG Data 📊

  • EEG: Electroencephalogram records brain activity using electrodes placed on your scalp. It's like eavesdropping on neurons having a chat. 🧠💬

  • EEG Data: These brainwave recordings come in time-series form. Think of them as brain symphonies—each electrode playing its unique melody. 🎵📈

3. EEGNet and ATCNet Models 🤖

EEGNet 🌐

  • Description: EEGNet is our brain-savvy neural network.
  • Features:
    • 🌀 Depthwise separable convolutions: Efficient feature extraction.
    • ⏱️ Temporal convolutional layers: Capturing brain rhythms over time.
    • 🏋️ Lightweight design: Perfect for real-time applications.

ATCNet (Attention-based Temporal Convolutional Network) 🎯

  • Description: ATCNet combines attention and convolutions for brainwave magic.
  • Features:
    • 👀 Multi-head self-attention: Like brain detectives focusing on crucial moments.
    • 🌊 Temporal convolutions: Surfing the brainwaves for patterns.
    • 🏆 State-of-the-art performance: ATCNet knows its cognitive ABCs.

References 📚

  1. Complex networks and deep learning for EEG signal analysis: https://link.springer.com/article/10.1007/s11571-020-09626-1
  2. Research Paper: https://www.mdpi.com/2306-5729/4/1/14
  3. MSHANet: a multi-scale residual network with hybrid attention: https://link.springer.com/article/10.1007/s11571-024-10127-8
  4. EEG Motor Imagery Deep Learning Repository:https://github.com/edw4rdyao/eeg_mi_dl
  5. EEG classification using deep 1D convolutional neural network: https://youtu.be/qLYVnB4pWI8?si=99y4Q3aScFfRJ41U

Feel free to fork this repository. If you need more brainpower , just ask! 🧠🤖🌟