Enterprise AI Research Analysis
Aligning Brain Signals with Multimodal Speech and Vision Embeddings
Authored by Kateryna Shapovalenko and Quentin Auster
Executive Impact Summary
This research explores how pre-trained AI models can help decode the complex, layered processing of language in the human brain, offering insights for advanced human-computer interfaces and robust AI systems.
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Avg. Training Correlation Achieved
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Participants in EEG Study
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Distinct Embeddings per Stimulus
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Dimensionality Reduction (PCA)
Deep Analysis & Enterprise Applications
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Our Layered Alignment Approach
This research builds on prior work aligning EEG with averaged speech embeddings by investigating which layers of pre-trained models best reflect the brain's layered processing during speech perception. We systematically compare individual layers, progressive concatenation, and progressive summation strategies.
Enterprise Process Flow
Find Best Stimulus Embedding
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Train EEG Encoder
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Finetune for Word Prediction
Robust EEG Data Preparation
We utilized a 62-channel EEG dataset of 33 participants listening to Chapter One from Alice in Wonderland, totaling approximately 6.7 hours of audio. The data underwent extensive preprocessing to ensure quality and relevance for analysis.
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Total Listening Data (33 Participants)
This extensive dataset was segmented, filtered (60Hz notch, 2Hz high-pass), and noisy channels removed. Time-domain (mean, RMS, envelope, zero-crossing) and frequency-domain (STFT-based) features were extracted, followed by robust scaling and outlier correction to maximize data quality for neural alignment.
Leveraging Multimodal AI Embeddings
To capture the rich, multimodal nature of language understanding, we extracted embeddings from two distinct pre-trained models: Wav2Vec2 for acoustic-to-linguistic features and CLIP's text encoder for visual associations evoked by words.
| Feature | Wav2Vec2 (Audio-to-Text) | CLIP (Text Encoder) |
|---|---|---|
| Model Type | Self-supervised speech model | Multimodal (language & vision) |
| Primary Function | Encodes sound into language representations | Maps words to image concepts (visual associations) |
| Layers Extracted | 13 (Feature extractor + 12 transformer encoders) | 13 (Input projection + transformer blocks) |
| Initial Dim. (per layer) | High-dimensional (e.g., 122112) | High-dimensional (e.g., 81408) |
| Reduced Dim. (PCA) | Top 10 Principal Components | Top 10 Principal Components |
| Purpose in Study | Low-level acoustic to high-level lexical features | Visual mental imagery during story comprehension |
Dimensionality reduction via PCA to the top 10 components was crucial for robustness and computational efficiency, transforming dimensions like (13, 122112) to (13, 10) for Wav2Vec2 embeddings and (13, 81408) to (13, 10) for CLIP embeddings.
Evaluating Individual Layer Alignment
Our single-layer regression analysis (Method 1) revealed that while training correlations were consistently high (~0.784), the model struggled to generalize, resulting in negative test R² values. This indicates a significant challenge in robustly aligning individual layers with EEG signals using this method.
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Average Training R² (PCA Single-Layer)
This finding highlights the difficulty of achieving strong predictive power for EEG responses from individual embedding layers alone. The models tended to overfit the training data, failing to capture generalizable patterns in neural activity. Certain layers, particularly early wav2vec2 and mid CLIP layers, showed more centralized activations in topographic maps, suggesting they might capture more salient features.
Optimal Embedding Aggregation Strategies
We explored two strategies for combining embeddings across layers: progressive concatenation and progressive summation. While concatenation improved training performance, it led to worse generalization (decreasing test R²). Progressive summation, however, showed more promise.
Improved
Progressive Summation Strategy
The progressive summation approach, which preserves dimensionality while amplifying shared features, proved more robust. Test R² and correlation values increased alongside training metrics as more layers were added, indicating better generalization. This suggests that early-to-mid layers encode features most aligned with EEG, and their combined sum provides a richer, more generalizable neural representation than simple concatenation.
Key Insights & Future Directions
This study demonstrates that combining multimodal, layer-aware representations, especially through progressive summation of early-to-mid layers, shows potential for decoding how the brain understands language beyond mere acoustics. However, significant challenges in generalization persist.
Challenges in Brain Signal Decoding Generalization
Despite achieving strong correlations on training data, our models struggled to generalize, indicated by negative test R² values across all conditions. This reflects the broader challenge of overfitting in EEG decoding and highlights the difficulty of modeling shared neural patterns across individuals. Fundamental limitations in current embedding spaces and decoding architectures prevent full generalization.
Future Work: We plan to explore subject-invariant architectures and leverage larger multi-subject datasets to enhance generalization. Further analysis of alternative embedding spaces and advanced feature extraction will be critical to improve alignment between EEG and audio features, paving the way for more robust brain-to-audio alignment models.
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