Creating a basic AI-powered Brain-Computer Interface (BCI) system to process EEG signals and decode them involves several components: data acquisition, preprocessing, feature extraction, model training, and signal decoding. Below is a project structure and source code to get you started with a simplified version using Python.

🗂️ Folder Structure

bci_ai_project/
│
├── data/
│   └── sample_eeg.csv               # Sample EEG data (CSV format)
│
├── models/
│   └── eeg_decoder.pkl              # Saved ML model
│
├── src/
│   ├── __init__.py
│   ├── data_loader.py              # Load and preprocess EEG data
│   ├── feature_extractor.py        # Extract features from EEG signals
│   ├── model_trainer.py            # Train ML model
│   ├── signal_decoder.py           # Use trained model to decode signals
│
├── main.py                         # Entry point to run the pipeline
└── requirements.txt                # Python dependencies

📦 requirements.txt

numpy
pandas
scikit-learn
matplotlib
mne

📄 main.py

from src.data_loader import load_eeg_data
from src.feature_extractor import extract_features
from src.model_trainer import train_model
from src.signal_decoder import decode_signal

if __name__ == "__main__":
    # Load EEG data
    X, y = load_eeg_data("data/sample_eeg.csv")

    # Extract features
    features = extract_features(X)

    # Train model
    model = train_model(features, y)

    # Decode new signal
    decoded = decode_signal(model, features[0])
    print(f"Decoded signal: {decoded}")

📄 src/data_loader.py

import pandas as pd

def load_eeg_data(filepath):
    df = pd.read_csv(filepath)
    X = df.drop("label", axis=1).values
    y = df["label"].values
    return X, y

📄 src/feature_extractor.py

import numpy as np

def extract_features(X):
    # Simple statistical features
    features = []
    for signal in X:
        stats = [
            np.mean(signal),
            np.std(signal),
            np.max(signal),
            np.min(signal)
        ]
        features.append(stats)
    return np.array(features)

📄 src/model_trainer.py

from sklearn.ensemble import RandomForestClassifier
import joblib

def train_model(X, y):
    model = RandomForestClassifier()
    model.fit(X, y)
    joblib.dump(model, "models/eeg_decoder.pkl")
    return model

📄 src/signal_decoder.py

import joblib
import numpy as np

def decode_signal(model, signal_features):
    prediction = model.predict([signal_features])
    return prediction[0]

📄 data/sample_eeg.csv

You’ll need EEG data in CSV format. A simple mockup might look like:

ch1,ch2,ch3,ch4,label
0.1,0.2,0.3,0.4,focus
0.5,0.6,0.7,0.8,relax
...

🧠 Notes

• For real EEG data, consider using libraries like mne to load .edf or .fif files.
• You can integrate with hardware like OpenBCI or Emotiv for live data streaming.
• For advanced decoding, explore CNNs or RNNs with libraries like PyTorch or TensorFlow.