TYLER GALLOWAY

Greetings. I am Tyler Galloway, a neuroengineering researcher dedicated to bridging macroscale brain dynamics with millisecond-level neural intent decoding. With a Ph.D. in Biomedical Cybernetics (Johns Hopkins University, 2024) and leadership roles at the NIH BRAIN Initiative, my work pioneers cross-modal fusion architectures that empower paralyzed patients to control assistive devices with sub-centimeter spatial precision and <150 ms latency.

Theoretical and Technical Foundations

1. Multiscale Signal Fusion Rationale

• Problem: Single-modal BCIs face fundamental limitations:

  • EEG: High temporal resolution (ms) but poor spatial specificity (~10 cm³).

  • fMRI: Ultrahigh spatial resolution (1 mm³) but slow sampling (2-3 Hz).

• Breakthrough: My Cortico-Dynamic Fusion Theorem (published in Neuron, 2024) mathematically formalizes how:

  • fMRI-derived cortical parcellation guides EEG source localization.

  • EEG phase-amplitude coupling modulates fMRI-derived default mode network dynamics.

2. NeuroFuse-X Architecture

My three-tiered decoding framework integrates:

• Layer 1: Hardware Co-Registration

  • Custom 256-channel EEG/fNIRS cap with 3T MRI-compatible preamplifiers.

  • Motion-compensated fMRI sequences (EPI acceleration factor = 16).

• Layer 2: Adaptive Feature Fusion

  • Spatiotemporal Graph Convolution: Aligns fMRI voxel clusters with EEG microstates.

  • Dynamic Weighting Mechanism:
    w(t)=σ(∂EEGθ∂t⋅fMRIBOLD(t−Δ))w(t)=σ(∂t∂EEGθ​​⋅fMRIBOLD​(t−Δ))

  • Cross-Modal Contrastive Learning: Eliminates scanner-induced artifacts.

• Layer 3: Intent Translation

  • Hierarchical RNN decoders for discrete commands (e.g., "grasp cup") and continuous trajectories (robotic arm path planning).

Clinical Translation and Impact

1. ALS Patient Trials (2023-2025)

• Cohort: 45 participants (C4-C6 spinal injuries, Locked-In Syndrome).

• 2. Real-World Deployment

  • Wheelchair Navigation: 93% success rate in cluttered environments (vs. 61% for EEG-only).

  • Neuroprosthetic Hands: Achieved 14-degree-of-freedom control matching able-bodied dexterity.

  Ethical and Technical Challenges Overcome

  1. Data Heterogeneity:

     • Developed Meta-Scaling, a federated learning protocol preserving patient privacy across 7 hospital networks.

  2. Real-Time Constraints:

     • Designed Edge-Cloud Hybrid Pipeline with MRI-compatible GPUs (NVIDIA Clara Holoscan), reducing processing latency to 89 ms.

  3. Individual Variability:

     • Created NeuroID Embeddings – low-dimensional representations of user-specific neuroanatomical features.

  Future Frontiers

  1. Consciousness-Aware Decoding:
     Integrating global neuronal workspace theory to distinguish intentional vs. spontaneous signals.

  2. Closed-Loop Neuroplasticity:
     Using fused feedback to rewire corticospinal tracts in chronic paralysis (FDA Phase I trial starting Q3 2025).

  3. Quantum-Enhanced Fusion:
     Collaborating with Google Quantum AI to develop tensor network decoders for ultrahigh-dimensional data.