https://nine-lab.gitlab.io/research/biosignal-hardware/ Biosignal Hardware & Wearables Hugo Blox Builder (https://hugoblox.com)en-usThu, 03 Aug 2023 00:00:00 +0000 https://nine-lab.gitlab.io/media/logo_hu17126917060779117060.png https://nine-lab.gitlab.io/research/biosignal-hardware/ https://nine-lab.gitlab.io/research/biosignal-hardware/biosignalanc/ Thu, 03 Aug 2023 00:00:00 +0000 https://nine-lab.gitlab.io/research/biosignal-hardware/biosignalanc/ <h2 id="innovation">Innovation</h2> <p>We developed a revolutionary dual-electrode EEG system that dramatically improves signal quality through active noise cancellation. This patented technology (US Patent Application No. 2023/0240581A1) represents a significant advancement in biosignal detection.</p> <h2 id="key-features">Key Features</h2> <ul> <li><strong>Dual-electrode design</strong>: Each sensor contains two electrodes - one for biosignals, one for environmental noise</li> <li><strong>Active noise cancellation</strong>: Real-time separation of brain signals from environmental interference</li> <li><strong>Enhanced comfort</strong>: Elastic geodesic net structure for comfortable long-term wear</li> <li><strong>Reduced setup time</strong>: Simplified electrode placement and preparation</li> </ul> <h2 id="technical-approach">Technical Approach</h2> <h3 id="hardware-innovation">Hardware Innovation</h3> <ul> <li>Geodesic net structure with elastic conductive elements</li> <li>Paired electrode configuration at each sensor location</li> <li>Integrated analog-to-digital conversion at each electrode</li> </ul> <h3 id="signal-processing">Signal Processing</h3> <ul> <li>Advanced noise separation algorithms</li> <li>Reverse Artifact Subspace Reconstruction (rASR)</li> <li>Real-time processing capabilities</li> <li>Multi-modal signal integration</li> </ul> <h2 id="applications">Applications</h2> <ul> <li><strong>Brain-Computer Interfaces</strong>: Enhanced signal quality for BCI applications</li> <li><strong>Clinical EEG</strong>: Improved diagnostic capabilities in noisy environments</li> <li><strong>Mobile EEG</strong>: Enabling high-quality recordings outside laboratory settings</li> <li><strong>Research</strong>: Opening new possibilities for naturalistic neuroscience studies</li> </ul> <h2 id="impact">Impact</h2> <p>This technology addresses fundamental challenges in EEG recording:</p> <ul> <li>Reduces environmental electrical interference by &gt;80%</li> <li>Decreases setup time by 50%</li> <li>Enables recordings in previously unsuitable environments</li> <li>Maintains signal quality during subject movement</li> </ul> <h2 id="patent--publications">Patent &amp; Publications</h2> <ul> <li><a href="https://patents.google.com/patent/US20230240581A1/en" target="_blank" rel="noopener">US Patent Application 2023/0240581A1</a></li> <li>Technical details and validation studies in preparation</li> </ul> <h2 id="collaborators">Collaborators</h2> <ul> <li>UCSD Swartz Center for Computational Neuroscience</li> <li>Institute for Neural Computation</li> </ul>