Daniel Anaya, Gautam Batra, Peter Bracewell, Ryan Catoen, Dev Chakraborty, Mark Chevillet, Pradeep Damodara, Alvin Dominguez, Laurence Emms, Zifan Jiang, Ealgoo Kim, Keith Klumb, Frances Lau, Rosemary Le, Jamie Li, Brett Mateo, Laura Matloff, Asha Mehta, Emily M. Mugler, Akansh Murthy, Sho Nakagome, Ryan Orendorff, E-Fann Saung, Roland Schwarz, Ruben Sethi, Rudy Sevile, Ajay Srivastava, John Sundberg, Ying Yang, Allen Yin
SignificanceWe present a fiberless, portable, and modular continuous wave-functional near-infrared spectroscopy system, Spotlight, consisting of multiple palm-sized modules—each containing high-density light-emitting diode and silicon photomultiplier detector arrays embedded in a flexible membrane that facilitates optode coupling to scalp curvature.AimSpotlight’s goal is to be a more portable, accessible, and powerful functional near-infrared spectroscopy (fNIRS) device for neuroscience and brain–computer interface (BCI) applications. We hope that the Spotlight designs we share here can spur more advances in fNIRS technology and better enable future non-invasive neuroscience and BCI research.ApproachWe report sensor characteristics in system validation on phantoms and motor cortical hemodynamic responses in a human finger-tapping experiment, where subjects wore custom 3D-printed caps with two sensor modules.ResultsThe task conditions can be decoded offline with a median accuracy of 69.6%, reaching 94.7% for the best subject, and at a comparable accuracy in real time for a subset of subjects. We quantified how well the custom caps fitted to each subject and observed that better fit leads to more observed task-dependent hemodynamic response and better decoding accuracy.ConclusionsThe advances presented here should serve to make fNIRS more accessible for BCI applications.
KEYWORDS: Single photon avalanche diodes, Field programmable gate arrays, Cameras, Signal to noise ratio, Photons, Actinium, Autocorrelation, Principal component analysis, Data compression, Diffusers
SignificanceDiffuse correlation spectroscopy (DCS) is an indispensable tool for quantifying cerebral blood flow noninvasively by measuring the autocorrelation function (ACF) of the diffused light. Recently, a multispeckle DCS approach was proposed to scale up the sensitivity with the number of independent speckle measurements, leveraging the rapid development of single-photon avalanche diode (SPAD) cameras. However, the extremely high data rate from advanced SPAD cameras is beyond the data transfer rate commonly available and requires specialized high-performance computation to calculate large number of autocorrelators (ACs) for real-time measurements.AimWe aim to demonstrate a data compression scheme in the readout field-programmable gate array (FPGA) of a large-pixel-count SPAD camera. On-FPGA, data compression should democratize SPAD cameras and streamline system integration for multispeckle DCS.ApproachWe present a 192 × 128 SPAD array with 128 linear ACs embedded on an FPGA to calculate 12,288 ACFs in real time.ResultsWe achieved a signal-to-noise ratio (SNR) gain of 110 over a single-pixel DCS system and more than threefold increase in SNR with respect to the state-of-the-art multispeckle DCS.ConclusionsThe FPGA-embedded autocorrelation algorithm offers a scalable data compression method to large SPAD array, which can improve the sensitivity and usability of multispeckle DCS instruments.
Cerebral blood flow is an important biomarker of brain health and function, as it regulates the delivery of oxygen and substrates to tissue and the removal of metabolic waste products. Diffuse Correlation Spectroscopy (DCS) is a promising noninvasive optical technique for monitoring cerebral blood flow and for measuring cortex functional activation tasks. However, the current state-of-the-art DCS adoption is hindered by a trade-off between sensitivity to the cortex and signal-to-noise ratio (SNR). Here we report on a multi-speckle DCS (mDCS) system based on a 1024-pixel single-photon avalanche diode (SPAD) camera that removes this trade-off and demonstrated a 32-fold increase in SNR with respect to traditional single-speckle DCS.
Significance: Cerebral blood flow is an important biomarker of brain health and function as it regulates the delivery of oxygen and substrates to tissue and the removal of metabolic waste products. Moreover, blood flow changes in specific areas of the brain are correlated with neuronal activity in those areas. Diffuse correlation spectroscopy (DCS) is a promising noninvasive optical technique for monitoring cerebral blood flow and for measuring cortex functional activation tasks. However, the current state-of-the-art DCS adoption is hindered by a trade-off between sensitivity to the cortex and signal-to-noise ratio (SNR).
Aim: We aim to develop a scalable method that increases the sensitivity of DCS instruments.
Approach: We report on a multispeckle DCS (mDCS) approach that is based on a 1024-pixel single-photon avalanche diode (SPAD) camera. Our approach is scalable to >100,000 independent speckle measurements since large-pixel-count SPAD cameras are becoming available, owing to the investments in LiDAR technology for automotive and augmented reality applications.
Results: We demonstrated a 32-fold increase in SNR with respect to traditional single-speckle DCS.
Conclusion: A mDCS system that is based on a SPAD camera serves as a scalable method toward high-sensitivity DCS measurements, thus enabling both high sensitivity to the cortex and high SNR.
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