Increased beta band synchrony has been demonstrated to be a biomarker of Parkinson’s disease (PD). This abnormal synchrony can often be prolonged in long bursts of beta activity, which may interfere with normal sensorimotor processing. Previous closed loop deep brain stimulation (DBS) algorithms used averaged beta power to drive neurostimulation, which were indiscriminate to physiological (short) versus pathological (long) beta burst durations. We present a closed-loop DBS algorithm using beta burst duration as the control signal. Benchtop validation results demonstrate the feasibility of the algorithm in real-time by responding to pre-recorded STN data from a PD participant. These results provide the basis for future improved closed-loop algorithms focused on burst durations for in mitigating symptoms of PD.
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