Resting state detection for gating movement of a neural prosthesis

Suway, Steven B.; Tien, Rex N.; Jeffries, S Morgan; Zohny, Zohny; Clanton, Samuel T.; McMorland, Angus J. C.; Velliste, Meel

doi:10.1109/ner.2013.6696022

Cited by 4 publications

(5 citation statements)

References 13 publications

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“…Furthermore, in most situations, the neural interface must automatically identify the rest or non-attention states to avoid producing undesirable control outputs. To address this challenge, different machine learning strategies were devoted to combining the state classifiers with continuous kinematic decoders [25], [26], [41], [42].…”

Section: Discussionmentioning

confidence: 99%

“…The total performance of the integrated discrete and continuous decoder is highly dependent on the accurate performance of the state decoder. The poor performance of the classifier, especially for the idle state detection, may produce a disturbing and noisy command signal [25], [31]. This problem can decrease the usability of the neural interface for human subjects.…”

Section: Discussionmentioning

confidence: 99%

“…Different combined state detectors and continuous decoder methods have been suggested for the kinematic-based BMI systems [25], [26]. Suway et al combined an idle state decoder with a continuous brain-controlled prosthetic arm.…”

Section: Introductionmentioning

confidence: 99%

“…They obtained spike activities from 96-channel arrays implanted in the primary motor cortex of monkeys to detect idle and active states and produce continuous movement commands during the active state. They found that incorporating a state detector with a continuous decoder can significantly decrease the unwanted or jitter movements during the idle state compared with the traditional continuous variable decoders [25]. Kao et al demonstrated that incorporating a hidden Markov model (HMM) into a continuous brain controller can enhance the performance of neural prosthesis.…”

Section: Introductionmentioning

confidence: 99%

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State-Based Decoding of Force Signals From Multi-Channel Local Field Potentials

et al. 2020

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The functional use of brain-machine interfaces (BMIs) in everyday tasks requires the accurate decoding of both movement and force information. In real-word tasks such as reach-to-grasp movements, a prosthetic hand should be switched between reaching and grasping modes, depending on the detection of the user intents in the decoder part of the BMI. Therefore, it is important to detect the rest or active states of different actions in the decoder to produce the corresponding continuous command output during the estimated state. In this study, we demonstrated that the resting and force-generating time-segments in a key pressing task could be accurately detected from local field potentials (LFPs) in rat's primary motor cortex. Common spatial pattern (CSP) algorithm was applied on different spectral LFP sub-bands to maximize the difference between the two classes of force and rest. We also showed that combining a discrete state decoder with linear or non-linear continuous force variable decoders could lead to a higher force decoding performance compared with the case we use a continuous variable decoder only. Moreover, the results suggest that gamma LFP signals (50-100 Hz) could be used successfully for decoding the discrete rest/force states as well as continuous values of the force variable. The results of this study can offer substantial benefits for the implementation of a self-paced, force-related command generator in BMI experiments without the need for manual external signals to select the state of the decoder.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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State-Based Decoding of Force Signals From Multi-Channel Local Field Potentials

et al. 2020

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“…Several different decoding strategies have been used to integrate NC support into biomimetic kinematic neural signal decoders. While most of them were validated with offline analyses, the integration of NC support into a kinematic decoder was nevertheless utilized for asynchronous control of a robotic arm in monkeys in Suway et al ( 2013 ).…”

Section: Discussionmentioning

confidence: 99%

Data-Driven Transducer Design and Identification for Internally-Paced Motor Brain Computer Interfaces: A Review

Schaeffer

Aksenova

2018

Front. Neurosci.

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Brain-Computer Interfaces (BCIs) are systems that establish a direct communication pathway between the users' brain activity and external effectors. They offer the potential to improve the quality of life of motor-impaired patients. Motor BCIs aim to permit severely motor-impaired users to regain limb mobility by controlling orthoses or prostheses. In particular, motor BCI systems benefit patients if the decoded actions reflect the users' intentions with an accuracy that enables them to efficiently interact with their environment. One of the main challenges of BCI systems is to adapt the BCI's signal translation blocks to the user to reach a high decoding accuracy. This paper will review the literature of data-driven and user-specific transducer design and identification approaches and it focuses on internally-paced motor BCIs. In particular, continuous kinematic biomimetic and mental-task decoders are reviewed. Furthermore, static and dynamic decoding approaches, linear and non-linear decoding, offline and real-time identification algorithms are considered. The current progress and challenges related to the design of clinical-compatible motor BCI transducers are additionally discussed.

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Hybrid Trajectory Decoding from ECoG Signals for Asynchronous BCIs

Schaeffer

Aksenova

2016

Lecture Notes in Computer Science

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Resting state detection for gating movement of a neural prosthesis

Cited by 4 publications

References 13 publications

State-Based Decoding of Force Signals From Multi-Channel Local Field Potentials

State-Based Decoding of Force Signals From Multi-Channel Local Field Potentials

Data-Driven Transducer Design and Identification for Internally-Paced Motor Brain Computer Interfaces: A Review

Hybrid Trajectory Decoding from ECoG Signals for Asynchronous BCIs

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