Description

We currently calculate several features from signal data on a nightly basis since these calculations are computationally expensive. If we can speed these calculations up, we can utilize these features within the live BCI system. This body of work aims to use Convolutional Neural Networks (CNNs) to learn to predict nightly feature values from signal data in order to quickly calculate these features.

We should start by predicting the live system features to understand how well CNNs are at this task. Once we've done that, we can move onto predicting the nightly features.

Signal Dataset

Since we currently don't have live rat ECoG signals to use, we can learn how effective CNNs are at predicting nightly features based on human ECoG data. This isn't exactly analogous, but it should give us an indication of how effective this ML system would be. We have ECoG data from humans playing a game:

Dataset link - https://openneuro.org/datasets/ds004770/versions/1.0.0
Dataset Google Drive Link: https://drive.google.com/file/d/1Wh8SJ1qZ3_mBZdX_Hukz04uYbYQvfXSQ/view?usp=sharing
Dataset Paper: https://assets.researchsquare.com/files/rs-3581007/v1/c27bf88d-3f89-4b8f-bf79-0fd848624f38.pdf?c=1702544255
Dataset Name: sub-01_ses-task_task-game_run-01_ieeg.edf

Nightly Features

In this section, we will list out the nightly features we want to be able to predict quickly from ECoG data. These features can be found here:

https://github.com/Metaverse-Crowdsource/EEG-Chaos-Kuramoto-Neural-Net/blob/main/Systems_and_states/Experiments.ipynb

• 2D and 3D phase space reconstructions using delay embedding
• Mutual information for determining optimal delay
• False nearest neighbors for determining optimal embedding dimension
• Cao's method for determining optimal embedding parameters
• Katz Fractal Dimension
• Multiscale Entropy
• Wavelet-based Fractal Analysis and Hurst exponent estimation
• Lyapunov Exponents
• UMAP (Uniform Manifold Approximation and Projection) for dimensionality reduction
• t-SNE (t-Distributed Stochastic Neighbor Embedding) for dimensionality reduction
• Hamiltonian Matrix Construction using energy features, temporal symmetry, and channel correlations
• Quantum-inspired metrics:
• Spectral gap
• Localization length
• Purity
• von Neumann entropy
• Linear entropy
• Participation ratio
• Fidelity
• Concurrence
• Power Spectral Density (PSD) using Welch's method and FFT
• Harmonics Detection in PSD
• Harmonics Detection using Lyapunov Exponents
• Amari Neural Field Equation analysis:
• Spectrum analysis
• Response surface visualization
• Weighted Undirected Network measures:
• Clustering coefficient
• Modularity
• Small-worldness (sigma and omega)
• Global efficiency
• Assortativity
• Riemannian geometry analysis:
• Covariance matrix computation
• Riemannian mean of covariance matrices

https://github.com/Metaverse-Crowdsource/EEG-Chaos-Kuramoto-Neural-Net/blob/main/Spectral%20Analysis/Spectral%20Analysis.ipynb

• Welch's Power Spectral Density (PSD)
• Fast Fourier Transform (FFT) Power Spectral Density (PSD)
• Lomb-Scargle Periodogram
• Wavelet Transform Power Spectral Density (PSD)
• Autocorrelation Function (ACF)
• Partial Autocorrelation Function (PACF)
• Akaike Information Criterion (AIC) from AutoRegressive (AR) models
• Bayesian Information Criterion (BIC) from AutoRegressive (AR) models
• AutoRegressive (AR) model predicted values
• Band Powers (Delta, Theta, Alpha, Beta, Gamma)
• Short-Time Fourier Transform (STFT)
• Spectral Entropy
• Coherence using Continuous Wavelet Transform (CWT)
• Spectral Centroids
• Frequency of Maximum Power
• Spectral Edge Density
• Continuous Wavelet Transform (CWT) Coefficients

https://github.com/Metaverse-Crowdsource/EEG-Chaos-Kuramoto-Neural-Net/blob/main/Transfer%20Entropy/Transfer%20Entropy.ipynb

• Transfer Entropy between hemispheres (left and right)
• Transfer Entropy sequence for sliding windows between hemispheres
• CNN input features from Transfer Entropy sequence between hemispheres
• RNN input features from Transfer Entropy sequence between hemispheres
• Transfer Entropy between hemispherical channel pairs
• Transfer Entropy between brain regions (frontal, temporal, parietal, occipital)
• Full granularity Transfer Entropy between all channel pairs

Live System Features

These features are fast to calculate and are thus included in the live BCI system. Try to predict these first using CNNs to understand how effective CNNs are. You can find the features listed here:

Peaks:

• peak_height
• peak_counts
• average peak heights
• average distances
• average prominences
• variance
• std_dev
• RMS
• frequencies
• PSDs
• delta_band_power
• theta_band_power
• alpha_band_power
• beta_band_power
• spectral entropy
• fft_results
• magnitudes
• centroids
• spectral_edge_densities
• positive frequencies
• positive fft results
• cumulative sums
• total powers
• thresholds
• phases for each signals
• pairwise phase locking values
• higuchi_fractal_dimension
• zero_crossing_rate
• IMFS
• signal shapes
• average signal shapes
• warping factors
• evolution_rate
• analytic signals
• envelops
• derivatives

Task List

• Train CNN on computationally cheap live system features
• Train CNN on computationally expensive nightly features

We need to take a look at the relevant features to extract, and experiment with different combinations of these features.
For our first experiment setup, we will use a 1 dimensional DOOM aim+shoot game.

old game.py code section for DOOM :

# scenarios: https://vizdoom.farama.org/environments/default/
# custom scenario: https://vizdoom.farama.org/environments/creatingCustom/
# Initialize the game environment using the given configuration and scenario paths
def initialize_vizdoom(config_path, scenario_path):
    # Create a new DoomGame instance
    game = vzd.DoomGame()
    # Overwrite the default config path with a specific path
    config_path = "AAA_projects/UnlimitedResearchCooperative/Synthetic_Intelligence_Labs/ViZDoom/scenarios/my_way_home.cfg"
    # Overwrite the default scenario path with a specific path
    scenario_path = "AAA_projects/UnlimitedResearchCooperative/Synthetic_Intelligence_Labs/ViZDoom/scenarios/my_way_home.wad"
    # Make the game window visible
    game.set_window_visible(True)
    # Set the game mode to PLAYER (as opposed to SPECTATOR)
    game.set_mode(vzd.Mode.PLAYER)
    # Enable detailed objects information
    game.set_objects_info_enabled(True)

    game.set_sectors_info_enabled(True)

    # Set the screen resolution
    game.set_screen_resolution(vzd.ScreenResolution.RES_640X480)
    # Enable rendering of the HUD
    game.set_render_hud(True)

    game.set_automap_render_textures(True)

    game.set_render_weapon(True)

    game.set_render_decals(True)

    game.set_render_particles(True)

    game.set_render_effects_sprites(True)

    game.set_render_messages(True)

    game.set_render_corpses(True)

    game.set_render_all_frames(True)

    game.set_sound_enabled(True)
    
    # Clear any previously available buttons and specify new ones for this game instance
    game.clear_available_buttons()
    game.add_available_button(vzd.Button.ATTACK)
    game.add_available_button(vzd.Button.USE)
    game.add_available_button(vzd.Button.MOVE_BACKWARD)
    game.add_available_button(vzd.Button.MOVE_FORWARD)
    game.add_available_button(vzd.Button.TURN_RIGHT)
    game.add_available_button(vzd.Button.TURN_LEFT)
    # 
    game.set_button_max_value(self: vizdoom.DoomGame, button: vizdoom.Button, max_value: float)
    # Sets the maximum allowed absolute value for the specified Button. Setting the maximum value to 0 results in no constraint at all (infinity). This method makes sense only for delta buttons. The constraints limit applies in all Modes.
    # Has no effect when the game is running.

    # Initialize the game with the specified settings
    game.init()
    return game
    # The following lines are unreachable due to the preceding return statement
    # and should be removed or corrected for proper execution.


# buttons for actions: 
#    https://vizdoom.farama.org/api/python/doomGame/#vizdoom.DoomGame.set_available_buttons
#    https://github.com/Farama-Foundation/ViZDoom/blob/master/examples/python/delta_buttons.py

# Decode action strings into boolean arrays indicating which actions are active
def decode_actions(action_str):
    # Convert the comma-separated string into a list of integers
    action_codes = [int(code) for code in action_str.split(',') if code.isdigit()]
    # Initialize a boolean list to represent the activation state of each action
    action = [False] * len(vzd.Button)
    # Set the corresponding action to True based on the action codes
    for code in action_codes:
        if code < len(action):
            action[code] = True
    return action

# variables for game state: 
#    https://vizdoom.farama.org/main/api/python/gameState/#
#    https://vizdoom.farama.org/api/python/enums/
#    https://github.com/Farama-Foundation/ViZDoom/issues/361
#    https://github.com/Farama-Foundation/ViZDoom
#    https://github.com/Farama-Foundation/ViZDoom/blob/master/examples/python/buffers.py
#    https://github.com/Farama-Foundation/ViZDoom/blob/master/examples/python/labels_buffer.py
#    https://github.com/Farama-Foundation/ViZDoom/blob/master/examples/python/objects_and_sectors.py
#    https://vizdoom.farama.org/main/api/python/doomGame/#vizdoom.DoomGame.set_sectors_info_enabled
#    https://vizdoom.farama.org/main/api/python/gameState/#vizdoom.GameState.objects

# Extract and return game state information as a dictionary
def extract_game_state(game):
    # Retrieve various game variables
    hitcount = game.get_game_variable(vzd.GameVariable.HITCOUNT)
    hits_taken = game.get_game_variable(vzd.GameVariable.HITS_TAKEN)
    dead = game.get_game_variable(vzd.GameVariable.DEAD) > 0
    health = game.get_game_variable(vzd.GameVariable.HEALTH)
    attack_ready = game.get_game_variable(vzd.GameVariable.ATTACK_READY) > 0
    
    # Player position for distance calculation
    player_x = game.get_game_variable(vzd.GameVariable.POSITION_X)
    player_y = game.get_game_variable(vzd.GameVariable.POSITION_Y)
    player_z = game.get_game_variable(vzd.GameVariable.POSITION_Z)

    def detect_doors(labels):
    # Example logic; you'll need to adjust based on how doors are labeled in your scenario
    doors_detected = any(label.object_name.lower().contains("door") for label in labels)
    return doors_detected

    def categorize_enemy_type(labels):
    enemy_types_detected = {"weak": 0, "strong": 0, "boss": 0}
    for label in labels:
        if "Imp" in label.object_name:  # Example: assuming 'Imp' as a weak enemy
            enemy_types_detected["weak"] += 1
        elif "Demon" in label.object_name:  # Example: a stronger enemy
            enemy_types_detected["strong"] += 1
        # Add more conditions based on known enemy types in ViZDoom
    return enemy_types_detected

    # This requires keeping track of past actions and outcomes
    action_states = {"moving": False, "shooting": False, "escaping_enemy": False}

    def determine_exploring_state(depth_buffer):
    # Example heuristic: a narrower field in the depth buffer might indicate a corridor
    # This will require custom logic based on your game's design and scenarios
    return "corridor" if is_corridor(depth_buffer) else "open room"

    # Example logic for tracking if a key has been picked up
    level_states = {"looking_for_door_key": True, "have_door_key": False}

    def detect_wall_states(depth_buffer):
    # Example logic to process the depth buffer and determine wall proximity and orientation
    wall_states_detected = {"wall_to_the_left": False, "wall_to_the_right": False, "wall_in_front": False}
    # Fill in the logic based on depth buffer analysis
    return wall_states_detected

    # Initialize variables for enemy information
    enemy_in_view = 0.0
    enemy_position_x = 0.0
    enemy_position_y = 0.0
    enemy_position_z = 0.0
    enemy_angle = 0.0
    enemy_pitch = 0.0
    enemy_roll = 0.0
    enemy_velocity_x = 0.0
    enemy_velocity_y = 0.0
    enemy_velocity_z = 0.0

    visible_objects = []
    if state and state.labels:
        for obj in state.labels:  # Using labels for visible objects
            obj_distance = np.sqrt((obj.object_position_x - player_x) ** 2 + (obj.object_position_y - player_y) ** 2 + (obj.object_position_z - player_z) ** 2)
            visible_objects.append({
                "label": obj.value,
                "name": obj.object_name,
                "distance": obj_distance,
                "position": {
                    "x": obj.object_position_x,
                    "y": obj.object_position_y,
                    "z": obj.object_position_z,
                }
            })
     
    # Check if the game state has labels for identifying objects
    state = game.get_state()
    if state and state.labels:
        for label in state.labels:
            if label.object_name == "DoomPlayer" and label.object_id != 0:
                # Update enemy information based on the first encountered enemy
                enemy_in_view = 1.0
                enemy_position_x = label.object_position_x
                enemy_position_y = label.object_position_y
                enemy_position_z = label.object_position_z
                enemy_angle = label.object_angle
                enemy_pitch = label.object_pitch
                enemy_roll = label.object_roll
                enemy_velocity_x = label.object_velocity_x
                enemy_velocity_y = label.object_velocity_y
                enemy_velocity_z = label.object_velocity_z
                break  # Exit loop after finding the first enemy
    # https://vizdoom.farama.org/main/api/python/gameState/#data-types-used-in-gamestate
    
    # add_available_game_variable(self: vizdoom.DoomGame, variable: vizdoom.GameVariable) → None
    # Adds the specified GameVariable to the list of available game variables (e.g. HEALTH, AMMO1, ATTACK_READY) in the GameState returned by get_state() method.
    # Has no effect when the game is running.
    # Config key: availableGameVariables/available_game_variables (list of values)
    # Attempt to extract the screen buffer, if available
    
    screen_buffer = None
    if game.get_screen_format() != vzd.ScreenFormat.CRCGCB:
        screen_buffer = state.screen_buffer

    # Compile extracted information into a dictionary and return it
    game_state_info = {
        "hitcount": hitcount,
        "hits_taken": hits_taken,
        "dead": dead,
        "health": health,
        "attack_ready": attack_ready,
        "enemy_in_view": enemy_in_view,
        "enemy_position": {
            "x": enemy_position_x,
            "y": enemy_position_y,
            "z": enemy_position_z
        },
        "enemy_angle": enemy_angle,
        "enemy_pitch": enemy_pitch,
        "enemy_roll": enemy_roll,
        "enemy_velocity": {
            "x": enemy_velocity_x,
            "y": enemy_velocity_y,
            "z": enemy_velocity_z
        },
        "screen_buffer": screen_buffer
    }
    return game_state_info

In your D_game.py you are using these config files
config_path = "AAA_projects/UnlimitedResearchCooperative/Synthetic_Intelligence_Labs/ViZDoom/scenarios/my_way_home.cfg" scenario_path = "AAA_projects/UnlimitedResearchCooperative/Synthetic_Intelligence_Labs/ViZDoom/scenarios/my_way_home.wad"

But they are not included in this repository. Running the main function in the file will produce errors. Please include them in the repository.

create a 1d_enemy_shooter_DOOM script, game+player metadata extraction (1D position of enemy, 1D position of player aim), reward system specific to game (move reward / punish trigger to game script)

File used in the above screenshot - CMSIS-DAP-V1-F103.hex

Here are some methods of fusion and synthesis we can use for our features:

1.Deep Feature Synthesis (DFS)
2.Feature Embedding
3.Residual Connections
4.Attention Mechanisms
5.Bottleneck Layers
6.Skip Connections and Feature Concatenation
7.Regularization Techniques
8.Auxiliary Outputs
9.Custom Loss Functions and Multi-task Learning
10.Model Interpretability and Feature Importance Analysis
11.Feature Aggregation:
- Summation and Averaging
- Weighted Sums
- Higher Order Combinations
- Polynomials and Cross-terms
12.Dimensionality Reduction Techniques:
- Principal Component Analysis (PCA)
- Autoencoders
13.Non-linear Combinations:
- Functions of Features
- Bucketing/Binning
14.Concatenation for Embedding Layers
15.Conditional Features
16.Clustering-Based Features
17.Temporal or Sequential Combinations
18.Feature Transformation with Domain Knowledge
19.Feature Normalization
20.Manifold Learning
21.Kernel Methods
22.Spectral Methods
23.Advanced Probabilistic Models
24.Tensor Decomposition
25.Complex Networks and Graph Analysis
26.Wavelet Transforms
27.Differential Equations and Dynamical Systems
28.Information Theory
29.Lie Groups and Differential Geometry
30.Topological Data Analysis (TDA)

Description

Feature extraction using CNN quantization involves using a convolutional neural network (CNN) to extract meaningful features from neural data and then quantizing these features to reduce memory and computational requirements. This process allows for efficient representation of the data while preserving important information for downstream tasks such as classification or prediction.

Steps

1. Acquire continuous stream of neural data:
   This involves obtaining a continuous stream of data from neural sources, such as EEG signals.

2. Preprocess data:

• Segmentation: Divide the continuous data into smaller segments, often of fixed duration, such as 0.25-second intervals.
• Normalization: Adjust the amplitude of each segment to a standard scale to remove biases and enable better comparison
• Filtering: Apply filters to remove noise and artifacts from the data, enhancing the signal quality.
• Extract features and compare before/after normalization: Identify relevant features for further analysis and assess the impact of normalization on these features.

3. Extract features:

• Computational Features : Compute statistical features such as peak heights, variance, standard deviation, and root mean square (RMS).
• Spectral Analysis : Analyze frequency domain characteristics, such as power in different frequency bands (delta, theta, alpha, beta).
• Additional Features : Calculate other features like centroids, spectral edge densities, Higuchi fractal dimension, zero-crossing rate, and evolution rate.
• Prepare for CNN Input : Organize the extracted features into a suitable format for input to a CNN.

4. Design CNN architecture:

• Layer Definition : Define the layers of the CNN, including convolutional layers, pooling layers, and fully connected layers.
• Architecture Configuration : Specify the overall architecture, including the input and output layers, and the arrangement of the intermediate layers.

5. Train CNN on extracted features:

• Training Process : Use the extracted features as input to the CNN and train the network using a suitable optimization algorithm, such as gradient descent.

6. Quantize CNN weights and activations:

• Weight Quantization : Convert the floating-point weights of the CNN to fixed-point or integer representations to reduce memory and computational requirements.
• Activation Quantization : Apply quantization techniques to the activations during inference to reduce the computational complexity of the network.

7. Apply quantized CNN for feature quantization:

• Feature Quantization : Utilize the quantized CNN to process new data and extract quantized features, which can be used for further analysis or classification tasks.

8. Utilize quantized features for downstream tasks:

• Classification: Classify neural activity into different categories or states.
• Prediction: Predict future behavior or states based on the quantized features.
• Anomaly detection: Identify abnormal patterns or events in the neural data.
• Control: Drive actions or responses based on the analyzed neural activity.

9. Evaluate the performance of the system
   Measure metrics such as accuracy, precision, recall, F1-score, or any domain-specific performance indicators.

10. Iterate and refine:
    This might involve adjusting hyperparameters, refining the preprocessing steps, modifying the network architecture, or exploring alternative algorithms.

11. Deployment and integration:
    Integrate the system into existing infrastructure or applications, ensuring compatibility and scalability.

12. Continuous monitoring and maintenance:
    Monitor the performance of the deployed system over time.
    Perform maintenance tasks such as updating models with new data, retraining periodically, or addressing any issues that arise during operation.

By following these steps, we can develop a robust system for analyzing neural data using a quantized CNN and leverage the extracted features for various applications.

Pseudocode

Implementing the entire process of feature extraction and CNN-based quantization in a single code snippet would be quite complex. However, I can provide you with a simplified Python code example that demonstrates the basic steps of feature extraction and quantization using a hypothetical dataset and a simple CNN architecture.

Here's a basic outline of the code:

1. Assume we have extracted features from neural data.
2. We design a CNN architecture by defining layers such as convolutional and pooling layers and configuring the network structure, including input and output layers.
3. We train the CNN model on the extracted features by splitting the data into training and testing sets, preprocessing features, defining and compiling the model, and training it on the training data.
4. We quantize the CNN weights and activations to reduce memory usage and improve efficiency, converting floating-point weights to fixed-point or integer representations and applying quantization to activations during inference.
5. We apply the quantized CNN to process new data and extract quantized features for downstream tasks such as classification or prediction.
6. We utilize the quantized features for various applications, including classification or prediction tasks.
7. We evaluate the performance of the system using metrics such as accuracy to assess its effectiveness.
8. We iterate and refine the model architecture, hyperparameters, or preprocessing steps based on performance evaluation to improve its performance.
9. We deploy the trained model for real-world use, integrating it into existing infrastructure or applications for practical applications.
10. We continuously monitor and maintain the deployed system's performance, updating models with new data and addressing any issues that arise during operation.

import numpy as np
import tensorflow as tf
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler

# Step 1: Acquire continuous stream of neural data (assumed to be already available)
# Load your data here...

# Step 2: Preprocess data
# Assuming data is stored in X_data and labels in y_data
# You may need to define functions for filtering and feature extraction
def preprocess_data(X_data):
    # Segment data into 0.25-second intervals
    segments = segment_data(X_data, segment_length=0.25)
    
    # Normalize each segment
    scaler = StandardScaler()
    normalized_segments = [scaler.fit_transform(seg) for seg in segments]
    
    # Apply filtering to remove noise and artifacts
    filtered_segments = [apply_filtering(seg) for seg in normalized_segments]
    
    # Extract features
    features = [extract_features(seg) for seg in filtered_segments]
    
    return features

# Define helper functions for segmentation, filtering, and feature extraction...

# Step 3: Extract features (assumed to be implemented)
ef extract_features(segment):
    # Compute various features such as peak heights, variance, etc.
    # Perform spectral analysis
    # Compute other features like centroids, zero-crossing rate, etc.
    return computed_features

# Step 4: Design CNN architecture
def create_model(input_shape, num_classes):
    model = tf.keras.Sequential([
        tf.keras.layers.Conv1D(32, 3, activation='relu', input_shape=input_shape),
        tf.keras.layers.MaxPooling1D(2),
        tf.keras.layers.Conv1D(64, 3, activation='relu'),
        tf.keras.layers.MaxPooling1D(2),
        tf.keras.layers.Flatten(),
        tf.keras.layers.Dense(128, activation='relu'),
        tf.keras.layers.Dense(num_classes, activation='softmax')
    ])
    return model

# Step 5: Train CNN on extracted features
X_train, X_test, y_train, y_test = train_test_split(X_data, y_data, test_size=0.2, random_state=42)

X_train_features = preprocess_data(X_train)
X_test_features = preprocess_data(X_test)

model = create_model(input_shape=X_train_features[0].shape, num_classes=num_classes)
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
model.fit(np.array(X_train_features), np.array(y_train), epochs=10, batch_size=32, validation_split=0.1)

# Step 6: Quantize CNN weights and activations (optional)
converter = tf.lite.TFLiteConverter.from_keras_model(model)
converter.optimizations = [tf.lite.Optimize.DEFAULT]
quantized_tflite_model = converter.convert()

# Step 7: Apply quantized CNN for feature quantization
interpreter = tf.lite.Interpreter(model_content=quantized_tflite_model)
interpreter.allocate_tensors()

input_index = interpreter.get_input_details()[0]['index']
output_index = interpreter.get_output_details()[0]['index']

quantized_features = []

for segment in X_new_data:
    # Assuming segment is preprocessed
    input_data = np.expand_dims(segment, axis=0).astype(np.float32)
    interpreter.set_tensor(input_index, input_data)
    interpreter.invoke()
    output_data = interpreter.get_tensor(output_index)
    quantized_features.append(output_data)

quantized_features = np.array(quantized_features)

# Step 8: Utilize quantized features for downstream tasks
# For example, if you want to make predictions using the quantized features:
predictions = model.predict(quantized_features)

# Step 9: Evaluate the performance of the system
test_loss, test_accuracy = model.evaluate(np.array(X_test_features), np.array(y_test))
print("Test Accuracy:", test_accuracy)

# Step 10: Iterate and refine (if necessary)
# You can iterate on the model architecture, hyperparameters, or preprocessing steps based on performance evaluation.
# For example, you might adjust the learning rate, add regularization, or experiment with different network architectures.

# Example:
# model = create_model(input_shape=X_train_features[0].shape, num_classes=num_classes)
# model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=0.001), loss='sparse_categorical_crossentropy', metrics=['accuracy'])
# model.fit(np.array(X_train_features), np.array(y_train), epochs=20, batch_size=32, validation_split=0.1)

# Step 11: Deployment and integration
# Deploy the model for real-world use, integrating it into your application or infrastructure.
# Depending on your deployment environment, you might deploy as a web service, mobile app, or embedded system.

# Example:
# Save the quantized model for deployment
# with open("quantized_model.tflite", "wb") as f:
#     f.write(quantized_tflite_model)

# Step 12: Continuous monitoring and maintenance
# Monitor the deployed system's performance and perform maintenance tasks as needed.
# This might involve retraining the model with new data, updating the model architecture, or addressing any issues that arise in production.

# Example:
# Load the deployed model
# interpreter = tf.lite.Interpreter(model_path="quantized_model.tflite")
# interpreter.allocate_tensors()

# Perform inference on new data
# input_index = interpreter.get_input_details()[0]['index']
# output_index = interpreter.get_output_details()[0]['index']

# input_data = np.expand_dims(new_data, axis=0).astype(np.float32)
# interpreter.set_tensor(input_index, input_data)
# interpreter.invoke()
# output_data = interpreter.get_tensor(output_index)
# Perform further processing or actions based on the output_data

We need to experiment with various combinations of features for intuitive data visualisations, balancing the time it takes to plot, the computational expense

https://github.com/Unlimited-Research-Cooperative/Bio-Silicon-Synergetic-Intelligence-System/blob/main/Software/PC/Backend/desktop_browser_app/signals_monitor/Visualizations_for_GUI/Visualizations.ipynb