AI & Computer Vision • 2024Active Research
NeuroFormer-X: Multimodal Signal Modeling
Transformer architectures for cognitive state prediction and latent representations
Architecture
Multimodal Transformer
Signal Pipeline
Wavelet Filtering + FFT
Status
Active Research
Problem Context & System Scope
Explores the application of self-attention mechanisms to high-frequency continuous physiological and neuro-signal streams.
Combines classical signal processing filters (Wavelet decompositions and fast Fourier transforms) with positional embeddings to capture both transient spikes and long-term harmonic patterns.
Technical Breakdown & Pipeline Engineering
Signal Preprocessing & Tokenization
Converting raw analog temporal signals into discrete latent tokens.
- Multi-resolution Wavelet decomposition isolating high-frequency noise from baseline drift.
- Patch-based 1D convolutional embedding layer generating semantic signal tokens.
Engineering Hardships & Resolution
Challenge:
Low signal-to-noise ratio in physiological telemetry.
Engineering Solution:
Developed contrastive self-supervised pretraining objectives to learn noise-invariant representations.
Quantitative Results & Impact
- Ongoing active research into robust temporal feature extraction for non-stationary biosignals.
Technologies & Frameworks
PyTorchTransformersDigital Signal ProcessingWavelet Transform / FFTLatent Space Modeling