Brown University bioengineers have developed a new algorithm that could clear a path to more adaptive deep brain stimulation (DBS) technology.
The algorithm helps DBS systems more easily detect brain signals while concurrently delivering stimulation, by identifying and eliminating electrical artifacts.
The Brown team was able to use the algorithm to stitch fragments of low-resolution data into a high-resolution picture of an artifact waveform, which outperformed other approaches in distinguishing brain signals from artifacts in laboratory experiments and computer simulations.
Brown's Nicole Provenza said this differentiation holds up even when the signal of interest is very similar to simulation artifacts.
The researchers also said the algorithm is computationally inexpensive, which suggests the possibility of real-time artifact-filtering, and simultaneous recording and stimulation.
From News from Brown University
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