bai-Mind-64

bai-64 Mind | EEG-to-Text Model [BETA]🧠✍️

Classify imagined speech commands from EEG brain signals using deep learning.

Overview

This project enables Brain-Computer Interface (BCI) applications by decoding imagined directional commands ("Up", "Down", "Left", "Right") from EEG brain signals. Users think about a direction without speaking, and the system predicts their intended command.

Quick Start

Installation

pip install -r requirements.txt

Basic Usage

import numpy as np
from tensorflow import keras

# Load pre-trained model
model = keras.models.load_model('path/to/your/model.h5')

# Your EEG data (1 second, 64 channels, 250 Hz sampling)
eeg_data = np.random.randn(250, 64)  # Replace with real EEG

# Make prediction
prediction = model.predict(eeg_data.reshape(1, 250, 64))
classes = ['Up', 'Down', 'Left', 'Right']
predicted_command = classes[np.argmax(prediction)]

print(f"Predicted command: {predicted_command}")
print(f"Confidence: {np.max(prediction):.3f}")

Real-Time BCI Application

from analysis import InnerSpeechAnalyzer

# Initialize predictor
analyzer = InnerSpeechAnalyzer('path/to/your/model.h5')
predictor = analyzer.create_real_time_predictor()

# Real-time loop
while True:
    eeg_data = capture_eeg_signal()  # Your EEG acquisition function
    command, confidence = predictor.predict_thought(eeg_data)
    
    if confidence > 0.8:
        execute_command(command)  # Your command execution
        print(f"Executing: {command}")

Hardware Requirements

EEG Device

• Channels: 64 Channels (10-20 system)
• Sampling Rate: 250+ Hz
• Impedance: <5kΩ
• Bandwidth: 0.5-100 Hz

Recommended Devices

• OpenBCI Cyton + Daisy (16+ channels) (64 channels recommended)
• Emotiv EPOC X (14 channels) (64 channels recommended)
• g.tec g.USBamp (Professional) (64 channels recommended)

Applications

• 🦽 Assistive Technology: Control for paralyzed patients
• 🎮 Gaming: Mind-controlled games and VR
• 🤖 Robotics: Brain-controlled robot navigation
• 💻 Silent Computing: Hands-free computer control
• 🧪 Research: Neuroscience and BCI studies

Data Format

Your EEG data should be:

• Shape: (250, 64) per trial
• Duration: 1 second recording
• Channels: 64 EEG electrodes
• Sampling: 250 Hz
• Classes: ["Up", "Down", "Left", "Right"]

Features

✅ Ready-to-use pre-trained model
✅ Real-time prediction for BCI applications
✅ Custom training with your own EEG data
✅ Multiple architectures (CNN-LSTM, Transformer)
✅ EEG preprocessing pipeline included
✅ Cross-platform support (Windows, macOS, Linux)

Dependencies

tensorflow>=2.8.0,<3.0.0
scikit-learn>=1.0.0
numpy>=1.21.0
scipy>=1.7.0
pandas>=1.3.0
mne>=1.0.0
matplotlib>=3.5.0
seaborn>=0.11.0

Example Use Cases

Wheelchair Control

# User thinks "forward" → wheelchair moves forward
# User thinks "left" → wheelchair turns left

Smart Home

# User thinks "up" → lights turn on
# User thinks "down" → lights turn off

Gaming

# User thinks "right" → character moves right
# Mental commands for game control

Support

• Web Site: Neurazum
• Email: [email protected]

Note

This project is in the BETA phase. Use at your own risk. Due to the process, low accuracy rates may be observed. In addition, since the data belongs to <span style="color: #ff8d26; "><b>Neurazum</b></span>, the function structure may change in future models.

License

CC-BY-NC-SA 4.0 - see LICENSE file for details.

Acknowledgments

1. Neurazum's own data set was used. This data set is closed source.
2. Nieto, N., Peterson, V., Rufiner, H. L., Kamienkowski, J. E., & Spies, R. (2021). "Thinking out loud, an open access EEG-based BCI dataset for inner speech recognition." bioRxiv. https://doi.org/10.1101/2021.04.19.440473

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Enable mind-controlled technology with EEG! 🚀

<span style="color: #ff8d26; "><b>Neurazum</b> AI Department</span>