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<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>MindReader: Decoding Visual Perception</title>
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</head>
<body>
<!-- SLIDE 1: TITLE -->
<div class="slide layout-center">
<h1
style="background: linear-gradient(to right, #38bdf8, #818cf8); -webkit-background-clip: text; -webkit-text-fill-color: transparent;">
Decoding the Mind
</h1>
<p style="max-width: 800px;">A Technical Deep Dive into Brain-Computer Interfaces (BCI) and Generative AI.</p>
<div style="margin-top: 40px; padding: 20px 40px; border: 1px solid #334155; border-radius: 50px;">
<span style="color: var(--accent-primary);">Project MindReader</span>
</div>
</div>
<!-- SLIDE 2: THE PROBLEM (CONCEPT) -->
<div class="slide">
<div class="logo">01. THE PROBLEM</div>
<h2>The "Black Box" of Perception</h2>
<div class="layout-split">
<div>
<p>When you see an object, your brain fires a complex electrical pattern. To a computer, this pattern
looks like <strong>pure noise</strong>.</p>
<div class="concept-card">
<h3><i class="fa-solid fa-bolt"></i> The Challenge</h3>
<p>EEG sensors sit <em>outside</em> the skull. The bone acts as a low-pass filter, blurring the
crisp neuronal spikes into a "hum."</p>
</div>
<div class="concept-card">
<h3><i class="fa-solid fa-magnifying-glass"></i> The Goal</h3>
<p>Can we train an AI to find the "needle in the haystack"—the specific visual signal hidden inside
the noise—and reconstruct the image?</p>
</div>
</div>
<div class="visual-box">
<!-- Placeholder for Diagram: Noisy EEG Signal vs Clean Image -->
<div
style="position: absolute; bottom: 20px; color: white; background: rgba(0,0,0,0.7); padding: 10px;">
Signal vs. Perception</div>
</div>
</div>
</div>
<!-- SLIDE 3: CONCEPT - CONTRASTIVE LEARNING -->
<div class="slide">
<div class="logo">02. AI CONCEPTS</div>
<h2>Concept: Contrastive Learning</h2>
<p>How do we teach a machine to understand relationships without labels? We don't tell it "This is a dog." We
tell it "A matches B."</p>
<div class="layout-split">
<div style="display: flex; flex-direction: column; justify-content: center;">
<ul class="step-list">
<li><i class="fa-solid fa-layer-group"></i> <strong>The Batch:</strong> We take 128 random
Brainwave-Image pairs.</li>
<li><i class="fa-solid fa-check"></i> <strong>Positive Pair:</strong> Brainwave A + Image A (Correct
Match).</li>
<li><i class="fa-solid fa-xmark"></i> <strong>Negative Pair:</strong> Brainwave A + Image B
(Incorrect Match).</li>
<li><i class="fa-solid fa-graduation-cap"></i> <strong>The Task:</strong> The model must pull
Positive Pairs closer together in mathematical space and push Negative Pairs apart.</li>
</ul>
</div>
<div class="visual-box" style="background: #1e293b;">
<!-- Concept Visualization of Vector Space -->
<div style="text-align: center; padding: 20px;">
<div style="font-size: 60px; margin-bottom: 20px;">🧠 <span style="color: #4ade80;">⟷</span> 🖼️
</div>
<p>Aligning Brain Space & Image Space</p>
</div>
</div>
</div>
</div>
<!-- SLIDE 4: CONCEPT - CLIP EMBEDDINGS -->
<div class="slide">
<div class="logo">03. AI CONCEPTS</div>
<h2>The Universal Translator: CLIP</h2>
<p>We cannot translate Brainwaves directly to Pixels. We need a middleman language.</p>
<div
style="display: grid; grid-template-columns: 1fr auto 1fr auto 1fr; gap: 20px; align-items: center; margin-top: 40px;">
<div class="metric-box">
<div class="metric-val" style="font-size: 40px;">Brain</div>
<p>Noisy Voltage</p>
</div>
<div class="pipeline-arrow">➜</div>
<div class="metric-box"
style="border-color: var(--accent-secondary); background: rgba(129, 140, 248, 0.1);">
<div class="metric-val" style="font-size: 40px; color: var(--accent-secondary);">Embedding</div>
<p>Vector [0.1, -0.5, ...]</p>
</div>
<div class="pipeline-arrow">➜</div>
<div class="metric-box">
<div class="metric-val" style="font-size: 40px;">Image</div>
<p>Pixels</p>
</div>
</div>
<div class="concept-card" style="margin-top: 40px;">
<h3>Why Embeddings?</h3>
<p>An <strong>Embedding</strong> is a list of 768 numbers that represents the <em>meaning</em> of an image.
"Dog" and "Wolf" have similar numbers. "Dog" and "Car" have different numbers. We train the brain to
predict these numbers.</p>
</div>
</div>
<!-- SLIDE 5: PROJECT IMPLEMENTATION -->
<div class="slide">
<div class="logo">04. EXECUTION</div>
<h2>The MindReader Pipeline</h2>
<div class="layout-split">
<div>
<h3>Step 1: The "Big Data"</h3>
<p>We processed the <strong>THINGS-EEG</strong> dataset.</p>
<div class="metric-box"
style="text-align: left; display: flex; justify-content: space-between; align-items: center; margin-bottom: 20px;">
<div>
<div class="metric-val">50</div>
<div class="metric-label">Subjects</div>
</div>
<div>
<div class="metric-val">1.1M</div>
<div class="metric-label">Trials</div>
</div>
</div>
<h3>Step 2: The Architecture</h3>
<p>We built a custom <strong>ControlNet Adapter</strong>. This small neural network takes EEG signals
and injects them into a frozen <strong>Stable Diffusion</strong> model, steering the image
generation process.</p>
</div>
<div>
<div class="concept-card">
<h3><i class="fa-solid fa-microchip"></i> Engineering Feat</h3>
<p>Training this required massive compute. We optimized it to run on a single consumer GPU (RTX
2080) using:</p>
<ul>
<li><strong>Gradient Accumulation:</strong> Simulating large batches.</li>
<li><strong>Mixed Precision (FP16):</strong> Halving memory usage.</li>
<li><strong>Feature Caching:</strong> Pre-computing CLIP vectors.</li>
</ul>
</div>
</div>
</div>
</div>
<!-- SLIDE 6: RESULTS -->
<div class="slide">
<div class="logo">05. RESULTS</div>
<h2>From Noise to Signal</h2>
<div class="layout-split">
<div>
<h3 style="color: var(--success);">Retrieval Accuracy</h3>
<p>Can the model pick the correct image out of 128 random options?</p>
<div class="metric-box">
<div class="metric-val">64.2%</div>
<div class="metric-label">Our Model</div>
<p style="font-size: 14px; margin-top: 10px; opacity: 0.7;">vs 0.8% Random Chance</p>
</div>
<p style="margin-top: 20px;">This proves the model isn't guessing. It has learned a robust mapping
between brain activity and visual concepts.</p>
</div>
<div class="visual-box"
style="background: white; flex-direction: column; justify-content: flex-start; padding: 20px;">
<h3 style="color: #002b5c; margin-bottom: 20px;">Generative Reconstruction</h3>
<div style="display: grid; grid-template-columns: 1fr 1fr; gap: 10px; width: 100%;">
<!-- Placeholders for user result images -->
<div
style="background: #eee; height: 150px; border-radius: 8px; display: grid; place-items: center; color: #333;">
Input: "Bun"<br>(User Saw)</div>
<div
style="background: #eee; height: 150px; border-radius: 8px; display: grid; place-items: center; color: #333;">
Output: Round/Golden<br>(AI Dreamt)</div>
<div
style="background: #eee; height: 150px; border-radius: 8px; display: grid; place-items: center; color: #333;">
Input: "Washboard"<br>(User Saw)</div>
<div
style="background: #eee; height: 150px; border-radius: 8px; display: grid; place-items: center; color: #333;">
Output: Linear Texture<br>(AI Dreamt)</div>
</div>
</div>
</div>
</div>
<!-- SLIDE 7: CONCLUSION -->
<div class="slide layout-center">
<div class="logo">06. FUTURE</div>
<h2>The Next Frontier: Steering</h2>
<p style="max-width: 900px; text-align: center;">We have proven we can <strong>READ</strong> the signal. The
next step is to <strong>CONTROL</strong> it.</p>
<div class="tile-grid"
style="display: grid; grid-template-columns: 1fr 1fr; gap: 40px; margin-top: 40px; width: 100%;">
<div class="concept-card">
<h3>Current State</h3>
<p>Passive Decoding. The user looks at an image, and we try to guess what it is.</p>
</div>
<div class="concept-card" style="border-left-color: var(--success);">
<h3>Future Project</h3>
<p><strong>Steering Vectors.</strong> Can we mathematically identify the "Truth" vector in an LLM? Can
we perform "neurosurgery" on a model to stop hallucinations?</p>
</div>
</div>
</div>
</body>
</html>