Here’s a conceptual Python project that simulates a Satellite Wireless Brain-Computer Interface (BCI) system acting as a Cognitive Service Provider. This system:

• Connects users via satellite-like wireless simulation
• Decodes EEG signals using AI
• Offers cognitive services (e.g., focus enhancement, emotion detection)
• Tracks usage and charges customers accordingly

🗂️ Folder Structure

satellite_bci_service/
│
├── data/
│   └── eeg_streams/                 # Simulated EEG data per user
│
├── models/
│   └── cognitive_model.pkl         # Trained ML model
│
├── src/
│   ├── __init__.py
│   ├── satellite_link.py           # Simulate satellite connection
│   ├── eeg_processor.py            # Decode EEG signals
│   ├── cognitive_services.py       # Provide cognitive services
│   ├── billing.py                  # Track usage and charge customers
│   ├── user_manager.py             # Manage user profiles
│
├── main.py                         # Entry point
└── requirements.txt                # Dependencies

📦 requirements.txt

numpy
pandas
scikit-learn
joblib

📄 main.py

from src.user_manager import load_users
from src.satellite_link import connect_user
from src.eeg_processor import process_eeg
from src.cognitive_services import provide_service
from src.billing import charge_user

if __name__ == "__main__":
    users = load_users()

    for user in users:
        print(f"\nConnecting {user['name']}...")
        eeg_data = connect_user(user["id"])
        decoded = process_eeg(eeg_data)
        service = provide_service(decoded)
        charge = charge_user(user["id"], service)
        print(f"Service: {service} | Charge: ${charge:.2f}")

📄 src/user_manager.py

def load_users():
    return [
        {"id": "user001", "name": "Alice"},
        {"id": "user002", "name": "Bob"},
    ]

📄 src/satellite_link.py

import numpy as np

def connect_user(user_id):
    # Simulate EEG signal stream
    np.random.seed(hash(user_id) % 1000)
    return np.random.rand(4, 100)  # 4 channels, 100 samples

📄 src/eeg_processor.py

import numpy as np
import joblib

def extract_features(eeg_data):
    return np.mean(eeg_data, axis=1)

def process_eeg(eeg_data):
    features = extract_features(eeg_data)
    model = joblib.load("models/cognitive_model.pkl")
    prediction = model.predict([features])
    return prediction[0]

📄 src/cognitive_services.py

def provide_service(decoded_signal):
    services = {
        "focus": "NeuroFocus Boost",
        "relax": "CalmSync Therapy",
        "stress": "Cortisol Regulator"
    }
    return services.get(decoded_signal, "Generic Cognitive Support")

📄 src/billing.py

def charge_user(user_id, service_name):

    pricing = {
        "NeuroFocus Boost": 19.99,
        "CalmSync Therapy": 14.99,
        "Cortisol Regulator": 24.99,
        "Generic Cognitive Support": 9.99
    }
    return pricing.get(service_name, 0.0)

📄 models/cognitive_model.pkl

You’ll need to train and save a model using EEG features. Here’s a quick script to generate it:

# train_model.py
from sklearn.ensemble import RandomForestClassifier
import numpy as np
import joblib

X = np.random.rand(100, 4)
y = np.random.choice(["focus", "relax", "stress"], 100)

model = RandomForestClassifier()
model.fit(X, y)
joblib.dump(model, "models/cognitive_model.pkl")

🧠 Expansion Ideas

• Add real-time EEG streaming via Bluetooth or TCP
• Integrate satellite APIs (e.g., Starlink or simulated latency)
• Build a web dashboard for users and billing
• Use OAuth for secure user authentication