Software-Hardware Ecosystems for AI Edge Devices (SHE)

Learning based motion artifacts processing in fNIRS: a mini review (2023)
Journal Article
Zhao, Y., Luo, H., Chen, J., Loureiro, R., Yang, S., & Zhao, H. (2023). Learning based motion artifacts processing in fNIRS: a mini review. Frontiers in Neuroscience, 17, Article 1280590. https://doi.org/10.3389/fnins.2023.1280590

This paper provides a concise review of learning-based motion artifacts (MA) processing methods in functional near-infrared spectroscopy (fNIRS), highlighting the challenges of maintaining optimal contact during subject movement, which can lead to MA... Read More about Learning based motion artifacts processing in fNIRS: a mini review.

Edge Acceleration for Machine Learning-based Motion Artifact Detection on fNIRS Dataset (2023)
Presentation / Conference Contribution
Zhao, Y., Xia, Y., Loureiro, R., Zhao, H., Dolinsky, U., & Yang, S. (2023, April). Edge Acceleration for Machine Learning-based Motion Artifact Detection on fNIRS Dataset. Presented at IWOCL '23: International Workshop on OpenCL, Cambridge, UK

Machine Learning has potential applications across a wide spectrum of devices. However, current approaches for domain-specific accelerators have encountered difficulties in satisfying the most recent computational demands for machine learning applica... Read More about Edge Acceleration for Machine Learning-based Motion Artifact Detection on fNIRS Dataset.