Detecting Neural-State Transitions Using Hidden Markov Models for Motor Cortical Prostheses
Open accessAbstract
Neural prosthetic interfaces use neural activity related to the planning and perimovement epochs of arm reaching to afford brain-directed control of external devices. Previous research has primarily centered on accurately decoding movement intention from either plan or perimovement activity, but has assumed that temporal boundaries between these epochs are known to the decoding system. In this work, we develop a technique to automatically differentiate between baseline, plan, and perimovement epochs of neural activity. Specifically, we use a generative model of neural activity to capture how neural activity varies between these three epochs. Our approach is based on a hidden Markov model (HMM), in which the latent variable (state) corresponds to the epoch of neural activity, coupled with a state-dependent Poisson firing model. Using an HMM, we demonstrate that the time of transition from baseline to plan epochs, a transition in neural activity that is not accompanied by any external behavior changes, can be detected using a threshold on the a posteriori HMM state probabilities. Following detection of the plan epoch, we show that the intended target of a center-out movement can be detected about as accurately as that by a maximum-likelihood estimator using a window of known plan activity. In addition, we demonstrate that our HMM can detect transitions in neural activity corresponding to targets not found in training data. Thus the HMM technique for automatically detecting transitions between epochs of neural activity enables prosthetic interfaces that can operate autonomously.
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LIFEs have been recently used to control artificial devices showing good results during short-term trials with human amputees.
“In fact, after appropriate control in experimental models to test their biocompatibility and efficacy [18], LIFEs have been recently used to control artificial devices [6,7,22-25] showing good results during short-term trials with human amputees.”
From Decoding of grasping information from neural signals recorded using peripheral intrafascicular interfaces (2011) · cites this paper as [23]
Subjects are able to control a one-degree of freedom hand prosthesis through online processing of efferent neural signals, and repeatable sensory feedback may be received by stimulating afferent fibers.
“These trials showed that subjects are able to control a one-degree of freedom hand prosthesis through online processing of the efferent neural signals, and that repeatable sensory feedback may be received by stimulating afferent fibers [22,23] corresponding to the missing hand/fingers territories.”
From Decoding of grasping information from neural signals recorded using peripheral intrafascicular interfaces (2011) · cites this paper as [23]
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- Authors
- Kemere, C., Santhanam, G., Yu, B. M., Afshar, A., Ryu, S. I., Meng, T. H., & Shenoy, K. V.
- Affiliations
- Stanford University (US)
- Venue
- Journal of Neurophysiology
- Publisher
- American Physiological Society
- Published
- 2008-10
- Volume
- 100
- Issue
- 4
- Pages
- 2441-2452
- ISSN
- 0022-3077 (ISSN-L)
- 1522-1598
- PMC
- PMC2576226
- OpenAlex
- W2165376801
- Cited by
- 157
- References
- 36
- Citations by year
- 2026: 3 · 2025: 7 · 2024: 7 · 2023: 4 · 2022: 9 · 2021: 8 · 2020: 5 · 2019: 7 · 2018: 6 · 2017: 8 · 2016: 14 · 2015: 11 · 2014: 10 · 2013: 8 · 2012: 8
- FWCI
- 7.23
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Journal of Neurophysiology (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neuroscience and Neural Engineering
- Physical Sciences › Engineering › Biomedical Engineering › Muscle activation and electromyography studies
- Keywords
- neural state transitions, hidden Markov models, arm reaching, target detection, neural decoding
- MeSH terms
- Algorithms, Animals, Cognition, Cues, Electrodes, Implanted, Forearm, Macaca mulatta, Male, Markov Chains, Models, Neurological, Motor Cortex, Space Perception, Stereotyped Behavior, Models, Statistical, Poisson Distribution, Prostheses and Implants
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