Our Research

Advancing the frontiers of neurotechnology through rigorous scientific inquiry and interdisciplinary collaboration.

Biosensing Technology

Development of high-fidelity sensors for capturing physiological signals in real-world conditions.

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Signal Processing

Advanced algorithms for extracting meaningful information from complex biosignals.

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Neural Interfaces

Non-invasive methods for interfacing with the nervous system for monitoring and intervention.

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Human Performance

Understanding cognitive and physiological states to optimize human performance across domains.

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Data Privacy & Security

Ensuring sensitive biometric data remains secure while enabling valuable insights.

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Wearable Integration

Seamless incorporation of sensing technology into everyday wearables without compromising comfort.

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Sample Literature (for layout preview)

Representative placeholders; curated citations available under NDA

Real-time Neural State Classification Using Edge-Optimized Machine Learning

Smith, J., et al. (2023). Journal of Neuroengineering. DOI: 10.xxxx/xxxxxx

This paper presents a novel approach to real-time classification of neural states using lightweight ML models optimized for edge computing in wearable devices.

High-Fidelity Biosignal Acquisition in Dynamic Environments

Johnson, A., et al. (2023). IEEE Transactions on Biomedical Engineering. DOI: 10.xxxx/xxxxxx

Our research demonstrates breakthrough techniques for maintaining signal integrity in challenging environmental conditions typical of field applications.

Privacy-Preserving Analytics for Wearable Neurotech

Chen, L., et al. (2023). ACM Conference on Health, Inference, and Learning. DOI: 10.xxxx/xxxxxx

We introduce a framework for performing meaningful analytics on biosignals while preserving individual privacy through advanced cryptographic techniques.

Interested in Collaborating?

We're always looking for talented researchers and institutions to push the boundaries of what's possible in neurotech.

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