Dr. Carlos RuizViquez
What if your dreams could write themselves into stories? I built an ML system that does exactly that.
The Midnight Epiphany 💭
It was 3 AM, and I couldn’t sleep. As a PhD and expert in AI/ML, my brain often races with algorithmic possibilities at the worst times. But this particular insomnia session sparked something extraordinary: What if we could capture the narrative essence of dreams using EEG data and transform them into coherent stories?
Not science fiction. Not anymore.
The Wild Idea That Actually Worked 🚀
I present to you DreamScribe: a neural architecture that:
Reads EEG patterns during REM sleep
Maps brain waves to narrative elements
Generates cohesive stories that reflect the dreamer’s subconscious journey
class DreamToStory(nn.Module):
def init(self):
super().init()
self.eeg_encoder = WaveletTransformer(
channels=64,
temporal_resolution=256
)
self.narrative_decoder = GPT2Model.from_pretrained(
'gpt2-medium',
add_cross_attention=True
)
self.emotion_classifier = EmotionalArcDetector()
The Architecture: Where Neuroscience Meets NLP 🧬
Phase 1: Dream Signal Processing
Using a custom Temporal Convolutional Network with attention mechanisms, I process raw EEG data:
def process_dream_signals(self, eeg_data):
# Extract theta and gamma waves (associated with REM)
dream_features = self.wavelet_decompose(eeg_data,
bands=['theta', 'gamma'])
# Apply attention to identify "narrative peaks"
narrative_moments = self.attention_layer(dream_features)
return self.temporal_conv(narrative_moments)
Phase 2: The Dream2Vec Embedding Space
Here’s where it gets spicy. I created a novel embedding space that maps brain activity patterns to narrative elements:
Spike patterns → Character introductions
Theta wave bursts → Scene transitions
Cross-frequency coupling → Plot twists
Alpha suppression → Emotional climaxes
Phase 3: Story Generation with Coherence Constraints
def generate_story(self, dream_embeddings):
story_tokens = []
narrative_state = self.initialize_story_state()
for dream_segment in dream_embeddings:
# Maintain narrative coherence
context = self.build_context_window(story_tokens,
dream_segment)
# Generate with dream-guided attention
next_passage = self.narrative_decoder(
context,
dream_attention_mask=dream_segment.unsqueeze(0)
)
story_tokens.extend(next_passage)
narrative_state = self.update_story_arc(narrative_state,
next_passage)
return self.tokens_to_prose(story_tokens)
The Results Are Haunting 👻
From one subject’s 90-minute REM cycle, DreamScribe generated:
“The glass elevator descended through layers of forgotten birthdays. Each floor revealed another version of myself, younger, older, sometimes wearing my father’s face. At the bottom, a door opened to a garden where digital flowers bloomed in impossible colors, their petals made of whispered passwords I’d long forgotten…”
The subject confirmed this matched their dream about confronting past identities and lost memories. Goosebumps.
Technical Deep Dive: The Secret Sauce 🔬
1. Multi-Modal Attention Fusion
I developed a novel attention mechanism that learns cross-modal relationships:
class DreamNarrativeAttention(nn.Module):
def forward(self, eeg_features, language_features):
# Cross-attention between brain signals and language
dream_context = self.cross_attention(
query=language_features,
key=eeg_features,
value=eeg_features
)
# Temporal consistency enforcement
coherent_narrative = self.temporal_smoother(dream_context)
return self.narrative_projection(coherent_narrative)
2. The Emotional Arc Predictor
Dreams aren’t random — they follow emotional patterns. My system tracks these arcs:
emotional_trajectory = self.emotion_classifier(eeg_sequence)
story_sentiment = self.enforce_emotional_consistency(
generated_text,
emotional_trajectory
)
3. Preventing Nightmare Mode
A crucial safety feature: detecting distressing patterns and softening the narrative:
if self.distress_detector(eeg_pattern) > threshold:
story_modifier = self.therapeutic_reframing_module
story = story_modifier(story, target_valence='neutral_positive')
Real-World Applications That Blew My Mind 🌟
Therapeutic Storytelling: Helping PTSD patients reframe traumatic dreams
Creative Assistance: Writers using their dreams as story seeds
Dream Journals 2.0: Automatic, accurate dream documentation
Neurological Research: Understanding narrative construction in the brain
The Dataset Challenge & Solution 💪
Creating a dataset was… interesting. I partnered with a sleep lab and collected:
10,000 hours of EEG data during REM sleep
Morning dream recall transcripts
Professional writer annotations for narrative structure
The preprocessing pipeline alone deserves its own paper:
class DreamDataPipeline:
def preprocess(self, raw_eeg, dream_transcript):
# Artifact removal
clean_eeg = self.remove_artifacts(raw_eeg)
# Synchronize EEG segments with recalled events
aligned_data = self.temporal_alignment(
clean_eeg,
self.extract_events(dream_transcript)
)
# Create supervised training pairs
return self.create_narrative_pairs(aligned_data)
The Plot Twist: Reverse Engineering Stories 🔄
Here’s where it gets META. I reversed the process:
Feed a story into the system
Generate the “dream EEG pattern” that would create it
Compare with actual dream recordings
This opened doors to understanding how our brains construct narratives during sleep!
Challenges & Ethical Considerations 🤔
Privacy: Dreams are intimate. All data is encrypted and anonymized.
Accuracy: Not every dream translates perfectly (yet)
Consent: Participants can delete their dream-stories anytime
Interpretation: The system generates a story, not the story
What’s Next? The Dream Continues 🌙
I’m currently working on:
- DreamShare: A platform where people can safely share their generated dream stories
- Lucid Learning: Training the model to recognize lucid dreaming patterns
- Cross-Cultural Dreams: How narrative generation differs across cultures
- Dream2Movie: Generating visual scenes from dream stories
*Try It Yourself! *🛠️
I’ve open-sourced a simplified version:
git clone https://github.com/cruizviquez/dreamscribe-lite
cd dreamscribe-lite
pip install -r requirements.txt
python generate_from_eeg.py --input your_sleep_data.edf
*The Philosophical Question *🤯
If an AI can transform our dreams into stories, what does that say about the nature of consciousness, creativity, and the narrative self? Are we just biological story-generators, creating meaning from neural noise?
📋This project showcases:
- Novel Architecture Design: Creating new neural architectures for unexplored problems
- Interdisciplinary Thinking: Combining neuroscience, NLP, and signal processing
- Full-Stack ML: From data collection to deployment
- Ethical AI Development: Building with privacy and consent in mind Research → Product: Transforming academic research into practical applications
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