Project number
27004
Organization
UA Department of Biomedical Engineering
Offering
ENGR498-F2026-S2027
Recent work shows that sensory stimulation locked to endogenous physiological rhythms can causally modulate brain function—for example, auditory stimuli timed to EEG slow-wave peaks during sleep enhance cerebrospinal fluid flow in a phase-dependent manner. However, such closed-loop systems typically require specialized, costly equipment, limiting their accessibility. This project proposes a low-cost, modular biofeedback platform that acquires physiological signals, detects rhythm features in real time, and triggers stimulation at user-selected phases of ongoing cardiac or neural activity.
Development will take place outside the MRI environment using open, consumer-grade hardware—a pulse sensor (PPG) for cardiac monitoring and an OpenBCI Ganglion board for EEG/EMG/ECG. Key engineering challenges include real-time feature detection, prediction of upcoming rhythm targets to compensate for system latency, signal-quality gating to suppress unreliable triggers, and synchronized event logging. Validation will use simulated and recorded waveforms to quantify detection accuracy, end-to-end latency, timing variability, and missed/false trigger rates, followed by supervised live demonstrations. Extensions may include integration with MRI-compatible stimulation hardware.
Deliverables include a documented working prototype with a simple researcher interface and a performance validation report, establishing whether consumer-grade sensors can support sufficiently precise physiological timing for future closed-loop neuroscience and neuromodulation studies.
Development will take place outside the MRI environment using open, consumer-grade hardware—a pulse sensor (PPG) for cardiac monitoring and an OpenBCI Ganglion board for EEG/EMG/ECG. Key engineering challenges include real-time feature detection, prediction of upcoming rhythm targets to compensate for system latency, signal-quality gating to suppress unreliable triggers, and synchronized event logging. Validation will use simulated and recorded waveforms to quantify detection accuracy, end-to-end latency, timing variability, and missed/false trigger rates, followed by supervised live demonstrations. Extensions may include integration with MRI-compatible stimulation hardware.
Deliverables include a documented working prototype with a simple researcher interface and a performance validation report, establishing whether consumer-grade sensors can support sufficiently precise physiological timing for future closed-loop neuroscience and neuromodulation studies.