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
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.
Generating Synthetic Dataset
Create a synthetic dataset with multiple samples for each Morse symbol.
Pad sequences to a fixed length for uniformity.
Training Machine Learning Model
Split the dataset into training and testing sets.
Encode the labels.
Train a RandomForestClassifier.
Evaluate the model's performance.
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()
plt.show()
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.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.
コメント