Dana H. Brooks
Papers
1
Total Citations
8
H-Index
1
About
Dana H. Brooks is a leading researcher in biomedical signal processing and computational neuroscience, with a particular focus on advancing non-invasive brain stimulation and neuroimaging techniques. Her work bridges engineering and clinical neuroscience, notably through the development of efficient algorithms for transcranial magnetic stimulation (TMS)-based motor cortex mapping. In her highly cited 2021 paper, Brooks introduced a Gaussian process active learning framework that dramatically improves the efficiency of cortical motor topography mapping—replacing traditional, time-intensive sampling strategies with adaptive, data-driven approaches that intelligently select stimulation sites. This innovation reduces experimental time while maintaining high spatial resolution, enabling more practical and scalable brain mapping for both research and clinical applications. With over 8 citations on this work alone, Brooks’ contributions have been recognized for their potential to transform preoperative planning and rehabilitation assessment. Her broader portfolio includes pioneering work in inverse problems, electrophysiological source imaging, and statistical signal processing, making her a key figure in the integration of machine learning with neural engineering. Brooks continues to push the boundaries of how computational methods can unlock the brain’s functional organization.
Research Focus
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Top Papers
- 1