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A Closed-Loop Brain-Machine Interface with One-Shot Learning and Online Tuning for Patient-Specific Neurological Disorder Treatment
Cited 1 time in
Web of Science
Cited 1 time in Scopus
- Authors
- Issue Date
- 2022
- Publisher
- IEEE
- Citation
- 2022 IEEE INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE CIRCUITS AND SYSTEMS (AICAS 2022): INTELLIGENT TECHNOLOGY IN THE POST-PANDEMIC ERA, pp.186-189
- Abstract
- Treatment of neurological disorders such as epilepsy, Parkinson's tremor, and Alzheimer's disease require energy-efficient Machine-Learning (ML) on-the-edge with one-shot learning, particularly in wearable form factor for pervasiveness. In many cases, patient-to-patient variations on neurological biomarkers are huge. Thus, patient-specific training with one-shot learning and online tuning is crucial. This paper introduces a wearable closed-loop brain-machine interface system targeting one-shot learning low-power high-accuracy seizure detection classifiers, with a special focus on a low-power online-tuning scheme to effectively track each patient's symptoms.
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Related Researcher
- College of Engineering
- Department of Electrical and Computer Engineering
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