Create neurointerface_biometric_authentication.py

advanced_feature/neurointerface_biometric_authentication/neurointerface_biometric_authentication.py

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+import numpy as np
+import pandas as pd
+from sklearn.preprocessing import StandardScaler
+from sklearn.model_selection import train_test_split
+from sklearn.metrics import accuracy_score, classification_report, confusion_matrix
+import torch
+import torch.nn as nn
+import torch.optim as optim
+from torch.utils.data import Dataset, DataLoader
+from brainflow import BoardShim, BoardIds, BrainFlowInputParams, DataFilter
+from brainflow.data_filter import FilterTypes, DetrendOperations
+
+class NeuroInterfaceDataset(Dataset):
+    def __init__(self, eeg_data, labels):
+        self.eeg_data = eeg_data
+        self.labels = labels
+
+    def __len__(self):
+        return len(self.eeg_data)
+
+    def __getitem__(self, idx):
+        eeg_data = self.eeg_data.iloc[idx, :]
+        label = self.labels.iloc[idx]
+        return {
+            'eeg_data': torch.tensor(eeg_data.values, dtype=torch.float),
+            'label': torch.tensor(label, dtype=torch.long)
+        }
+
+class NeuroInterfaceModel(nn.Module):
+    def __init__(self, input_dim, hidden_dim, output_dim):
+        super(NeuroInterfaceModel, self).__init__()
+        self.fc1 = nn.Linear(input_dim, hidden_dim) 
+        self.fc2 = nn.Linear(hidden_dim, hidden_dim) 
+        self.fc3 = nn.Linear(hidden_dim, output_dim) 
+
+    def forward(self, x):
+        x = torch.relu(self.fc1(x)) 
+        x = torch.relu(self.fc2(x)) 
+        x = self.fc3(x) 
+        return x
+
+def generate_eeg_data(board_id, sampling_rate, duration):
+    # Initialize BrainFlow board
+    board = BoardShim(board_id, BrainFlowInputParams())
+    board.prepare_session()
+    # Start streaming EEG data
+    board.start_stream()
+    # Read EEG data for specified duration
+    eeg_data = board.get_board_data(sampling_rate * duration)
+    # Stop streaming and release board
+    board.stop_stream()
+    board.release_session()
+    return pd.DataFrame(eeg_data)
+
+def train_neurointerface_model(eeg_data, labels, epochs=100, batch_size=32):
+    # Create dataset and data loader
+    dataset = NeuroInterfaceDataset(eeg_data, labels)
+    data_loader = DataLoader(dataset, batch_size=batch_size, shuffle=True)
+
+    # Initialize NeuroInterface model
+    model = NeuroInterfaceModel(input_dim=128, hidden_dim=64, output_dim=2)
+
+    # Define loss function and optimizer
+    criterion = nn.CrossEntropyLoss()
+    optimizer = optim.Adam(model.parameters(), lr=0.001)
+
+    # Train NeuroInterface model
+    for epoch in range(epochs):
+        for batch in data_loader:
+            eeg_data = batch['eeg_data'].to(device)
+            label = batch['label'].to(device)
+            optimizer.zero_grad()
+            output = model(eeg_data)
+            loss = criterion(output, label)
+            loss.backward()
+            optimizer.step()
+        print(f'Epoch {epoch+1}, Loss: {loss.item()}')
+
+    return model
+
+def authenticate_user(eeg_data, model):
+    # Authenticate user using NeuroInterface model
+    eeg_data = torch.tensor(eeg_data.values, dtype=torch.float)
+    output = model(eeg_data)
+    _, predicted = torch.max(output, 1)
+    return predicted.item()
+
+# Generate EEG data for user authentication
+eeg_data = generate_eeg_data(BoardIds.SYNTHETIC_BOARD, 128, 10)
+
+# Generate labels for user authentication
+labels = pd.DataFrame([{'label': 1}]) 
+
+# Train NeuroInterface model
+model = train_neurointerface_model(eeg_data, labels)
+
+# Authenticate user
+authentication_result = authenticate_user(eeg_data, model)
+print(f'Authentication result: {authentication_result}')