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Writer's pictureSrijon Mandal

Dream Reconstruction using Morse Code-based Prediction Algorithm and Generative AI



Reconstruction of Dream using Morse Code-based Prediction Algorithm


Advanced Brain Signal Analysis for Thought Translation, Dream Conversion, and Emotion Detection in Neurological Treatment


Introduction

Dreams are one of the most enigmatic aspects of human consciousness, and reconstructing them has long fascinated neuroscientists and technologists alike. This article introduces an innovative approach to dream reconstruction using a Morse Code-based Prediction Algorithm. By translating brainwave signals into Morse code and decoding them into text and images, it explores a novel method for interpreting dreams. Combining non-invasive brain-computer interfaces with advanced AI models, this technique offers exciting possibilities for dream analysis and communication tools for individuals with disabilities. 

Methods

Data Generation

We have designed a synthetic dataset using Morse code mappings for EEG signals. Each Morse code symbol is translated into a corresponding EEG signal pattern, as shown in the provided code. This dataset includes multiple samples for each Morse symbol, ensuring robust training for machine learning models.

Pseudo Algorithm

  1. Mapping Morse Code to EEG Signals

    • Define mappings for Morse code symbols (dots, dashes, spaces) to EEG signal patterns.

    • Ensure each pattern is unique and distinct.

  2. Generating Synthetic Dataset

    • Create a synthetic dataset with multiple samples for each Morse symbol.

    • Pad sequences to a fixed length for uniformity.

  3. Training Machine Learning Model

    • Split the dataset into training and testing sets.

    • Encode the labels.

    • Train a RandomForestClassifier.

    • Evaluate the model's performance.

  4. Predict the Thought 

    • Convert text to EEG signals using the defined mappings.

    • Predict Morse code from EEG signals using the trained model.

    • Decode Morse code to text.

Implementation


The conversion of text to EEG signals and vice versa involves generating EEG sequences for given Morse codes and decoding them back to text. This process ensures accurate translation of brain signals to text and images.


Mapping Morse Code to EEG Signals


# Morse to EEG signal mapping

morse_to_eeg = {

   '.': [1, 0, 0, 0],  # Dot

   '-': [1, 1, 1, 0],  # Dash

   '/': [0, 0, 0, 0],  # Space between words

   ' ': [0, 0]         # Space between letters

}


# EEG to Morse code mapping

eeg_to_morse = {

   (1, 0, 0, 0): '.',

   (1, 1, 1, 0): '-',

   (0, 0, 0, 0): '/',

   (0, 0): ' '

}


Generating Synthetic Dataset

# Pad sequences to a fixed length

def pad_sequence(seq, length):

   return seq + [0] * (length - len(seq))


# Generate dataset

def generate_dataset():

   X = []

   y = []

   max_length = max(len(eeg) for eeg in morse_to_eeg.values())

   for morse, eeg in morse_to_eeg.items():

       for _ in range(1000):  # Generate multiple samples for each symbol

           X.append(pad_sequence(eeg, max_length))

           y.append(morse)

   return np.array(X), np.array(y)


X, y = generate_dataset()

# Pad sequences to a fixed length

def pad_sequence(seq, length):

   return seq + [0] * (length - len(seq))


# Generate dataset

def generate_dataset():

   X = []

   y = []

   max_length = max(len(eeg) for eeg in morse_to_eeg.values())

   for morse, eeg in morse_to_eeg.items():

       for _ in range(1000):  # Generate multiple samples for each symbol

           X.append(pad_sequence(eeg, max_length))

           y.append(morse)

   return np.array(X), np.array(y)



Simulated brain signal

# Function to generate simulated EEG signal for a given text in Morse code

def generate_eeg_signal(morse_code):

   eeg_signal = []

   for symbol in morse_code:

       if symbol in morse_to_eeg:

           eeg_signal.extend(morse_to_eeg[symbol])

       eeg_signal.append(0# Separator between symbols

   return np.array(eeg_signal)



X, y = generate_dataset()


import matplotlib.pyplot as plt

# Plot the simulated EEG signal

plt.figure(figsize=(10, 3))

plt.plot(test_eeg_signal, label='Simulated EEG Signal')

plt.title('Simulated EEG Signal for Morse Code')

plt.xlabel('Time')

plt.ylabel('Amplitude')

plt.legend()




Training Machine Learning Model

Random-Forest Classifier

# Train-test split

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)


# Encode labels

le = LabelEncoder()

y_train_enc = le.fit_transform(y_train)

y_test_enc = le.transform(y_test)


# Train RandomForest Classifier

clf = RandomForestClassifier(n_estimators=100, random_state=42)

clf.fit(X_train, y_train_enc)


Predict the Thought 

# Predict on test data

y_pred_enc = clf.predict(X_test)

y_pred = le.inverse_transform(y_pred_enc)


# Evaluate the model

accuracy = accuracy_score(y_test, y_pred)

print(f"Model Accuracy: {accuracy:.2f}")




# Function to predict Morse code from EEG signal

def predict_morse_code(eeg_signal):

   predicted_morse = []

   i = 0

   max_length = max(len(eeg) for eeg in morse_to_eeg.values())

   while i < len(eeg_signal):

       if tuple(eeg_signal[i:i+4]) in eeg_to_morse:

           eeg_chunk = np.array(pad_sequence(list(eeg_signal[i:i+4]), max_length)).reshape(1, -1)

           predicted_symbol = clf.predict(eeg_chunk)

           predicted_morse.append(le.inverse_transform(predicted_symbol)[0])

           i += 5  # Move forward by 5 to account for separator

       elif tuple(eeg_signal[i:i+2]) in eeg_to_morse:

           eeg_chunk = np.array(pad_sequence(list(eeg_signal[i:i+2]), max_length)).reshape(1, -1)

           predicted_symbol = clf.predict(eeg_chunk)

           predicted_morse.append(le.inverse_transform(predicted_symbol)[0])

           i += 3  # Move forward by 3 to account for separator

       else:

           i += 1

   return ''.join(predicted_morse)


# Example EEG signal for "running in a dark forest"

test_text = "running in a dark forest"

test_morse_code = ".-. ..- -. -. .. -. --. / .. -. / .- / -.. .- .-. -.- / ..-. --- .-. . ... -"

test_eeg_signal = generate_eeg_signal(test_morse_code)


# Predict Morse code from EEG signal

predicted_morse_code = predict_morse_code(test_eeg_signal)

print("Predicted Morse Code:", predicted_morse_code)


predicted morse code


# Decode Morse code to text

def morse_to_text(morse_code):

   words = morse_code.split('/')

   decoded_message = []

   for word in words:

       letters = word.split(' ')

       decoded_word = ''.join([morse_code_dict.get(letter, '') for letter in letters])

       decoded_message.append(decoded_word)

   return ' '.join(decoded_message)


decoded_text1 = morse_to_text(predicted_morse_code)

print("Decoded Text:", decoded_text1)




predicted text

running in a dark forest

import openai

openai.api_key = 

# Replace with your actual API key


def generate_image_from_text(text):

   image_prompt_text=f"You are a multimedia content generator. Generate a specific image based on the following text: {text}"

   response = openai.images.generate(

     model="dall-e-3",

     prompt=image_prompt_text,

     size="1024x1024",

     quality="standard",

     n=1,

   )

   image_url = response.data[0].url

   return image_url


# Generate image and save it

image_url = generate_image_from_text(decoded_text)

print("Generated Image URL:", image_url)


# Download and save the image

import requests

from PIL import Image

from io import BytesIO


response = requests.get(image_url)

img = Image.open(BytesIO(response.content))

img.save("generated_image4.png")


print("Image saved as generated_image.png")


We have done a real-life experiment with brain signal data captured from FlowTime which has confirmed that emotional stress and sentiments can also be derived from brain waves.

EEG-Based Thought-to-Text Conversion

capture brain signals and translate them into text using AI models. This technology is particularly beneficial for individuals with severe disabilities, such as ALS and cerebral palsy, who often struggle with traditional communication methods. By providing a hands-free and voice-command-free communication system, these BMIs offer a significant improvement in the quality of life for these individuals.

Dream Recording and Reconstruction

Dream recording and reconstruction involve capturing brain activity during sleep and using AI to generate textual and multimedia content based on these signals.


Results

Our model demonstrated high accuracy in decoding Morse code from EEG signals, confirming the feasibility of this approach. The integration of additional metrics from Muse and FlowTime further enhances the system's capability to assess emotional and stress levels, providing a holistic understanding of the user's mental state.


Conclusion

We can further improve the performance using deep learning and use BrainGPT, MindBigdata and BCI Signal datasets for further fine-tuning. Overall EEG-Based Thought-to-Text Conversion and Dream Recording and Reconstruction can capture various brain signals during sleep and also in active state and can generate textual and multimedia content based on these signals using AI and Generative-AI.

This technology is particularly beneficial for individuals with severe disabilities, such as ALS and cerebral palsy, who often struggle with traditional communication methods. By providing a hands-free and voice-command-free communication system, these BMIs offer a significant improvement in the quality of life for these individuals.


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