Intention estimation in brain–machine interfaces

Fan, Joline M.; Nuyujukian, Paul; Kao, Jennifer L.; Chestek, Cynthia A.; Ryu, Stephen I.; Shenoy, Krishna V.

doi:10.1088/1741-2560/11/1/016004

Cited by 64 publications

(128 citation statements)

References 65 publications

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“…Importantly, however, we found that our LMP-driven BMI performance was not as good as spike-driven performance using the state-of-the-art FIT-KF algorithm [49]. Thus, while our results demonstrate that LMP is a viable alternative control signal to spikes, we do not suggest employing LFP-only decoding if good spikes are available.…”

Section: Discussionmentioning

confidence: 61%

“…We felt that it would be most impactful to try to improve performance and robustness above that of a current state-of-the-art spikes decoder. We therefore used as our baseline the FIT-KF algorithm [49] and then extended it to decode both spikes and LMP. When both spikes and LMP were decoded from all available electrodes, hybrid decoding enabled a modest improvement in closed-loop performance in monkey R, but was marginally worse than spikes-only decoding in monkey J.…”

Section: Discussionmentioning

confidence: 99%

“…For the spikes-only decoder we used our previously reported Feedback Intention Trained Kalman filter (FIT-KF) [49]. The FIT-KF improves upon a standard velocity KF by adjusting kinematics of the training data to attempt to better match the subject's true intent: cursor velocity is rotated to always point towards the target and is set to zero during target hold epochs.…”

Section: Methodsmentioning

confidence: 99%

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A high performing brain–machine interface driven by low-frequency local field potentials alone and together with spikes

et al. 2015

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Objective Brain-machine interfaces (BMIs) seek to enable people with movement disabilities to directly control prosthetic systems with their neural activity. Current high performance BMIs are driven by action potentials (spikes), but access to this signal often diminishes as sensors degrade over time. Decoding local field potentials (LFPs) as an alternative or complementary BMI control signal may improve performance when there is a paucity of spike signals. To date only a small handful of LFP decoding methods have been tested online; there remains a need to test different LFP decoding approaches and improve LFP-driven performance. There has also not been a reported demonstration of a hybrid BMI that decodes kinematics from both LFP and spikes. Here we first evaluate a BMI driven by the local motor potential (LMP), a low-pass filtered time-domain LFP amplitude feature. We then combine decoding of both LMP and spikes to implement a hybrid BMI. Approach Spikes and LFP were recorded from two macaques implanted with multielectrode arrays in primary and premotor cortex while they performed a reaching task. We then evaluated closed-loop BMI control using biomimetic decoders driven by LMP, spikes, or both signals together. Main Results LMP decoding enabled quick and accurate cursor control which surpassed previously reported LFP BMI performance. Hybrid decoding of both spikes and LMP improved performance when spikes signal quality was mediocre to poor. Significance These findings show that LMP is an effective BMI control signal which requires minimal power to extract and can substitute for or augment impoverished spikes signals. Use of this signal may lengthen the useful lifespan of BMIs and is therefore an important step towards clinically viable BMIs.

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Section: Discussionmentioning

confidence: 61%

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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A high performing brain–machine interface driven by low-frequency local field potentials alone and together with spikes

et al. 2015

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“…This task has previously been described in prior work (e.g., [5], [15], [16], [36], [37]). The virtual cursor and targets were presented in a 3D environment (MSMS, MDDF, USC, Los Angeles, CA) [38].…”

Section: A Experimental Setup and Data Acquisitionmentioning

confidence: 99%

A High-Performance Neural Prosthesis Incorporating Discrete State Selection With Hidden Markov Models

Kao

Nuyujukian

Ryu

et al. 2017

IEEE Trans. Biomed. Eng.

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Communication neural prostheses aim to restore efficient communication to people with motor neurological injury or disease by decoding neural activity into control signals. These control signals are both analog (e.g., the velocity of a computer mouse) and discrete (e.g., clicking an icon with a computer mouse) in nature. Effective, high-performing, and intuitive-to-use communication prostheses should be capable of decoding both analog and discrete state variables seamlessly. However, to date, the highest-performing autonomous communication prostheses rely on precise analog decoding and typically do not incorporate high-performance discrete decoding. In this report, we incorporated a hidden Markov model (HMM) into an intracortical communication prosthesis to enable accurate and fast discrete state decoding in parallel with analog decoding. In closed-loop experiments with nonhuman primates implanted with multielectrode arrays, we demonstrate that incorporating an HMM into a neural prosthesis can increase state-of-the-art achieved bitrate by 13.9% and 4.2% in two monkeys ( ). We found that the transition model of the HMM is critical to achieving this performance increase. Further, we found that using an HMM resulted in the highest achieved peak performance we have ever observed for these monkeys, achieving peak bitrates of 6.5, 5.7, and 4.7 bps in Monkeys J, R, and L, respectively. Finally, we found that this neural prosthesis was robustly controllable for the duration of entire experimental sessions. These results demonstrate that high-performance discrete decoding can be beneficially combined with analog decoding to achieve new state-of-the-art levels of performance.

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“…Presumably the more accurate the decoder, the simpler, and more intuitive will be the user adaptation. Further performance improvements could be achieved through user adaptation, combined with continued decoder adaptation and retraining [2, 9, 12, 15]. …”

Section: Discussionmentioning

confidence: 99%

Adaptive neuron-to-EMG decoder training for FES neuroprostheses

Éthier

Acuña²,

Solla

et al. 2016

J. Neural Eng.

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Objective We have previously demonstrated a brain-machine interface (BMI) neuroprosthetic system that provided continuous control of functional electrical stimulation (FES) and restoration of grasp in a primate model of spinal cord injury (SCI). Predicting intended EMG directly from cortical recordings provides a flexible high-dimensional control signal for FES. However, no peripheral signal such as force or EMG is available for training EMG decoders in paralyzed individuals. Approach Here we present a method for training an EMG decoder in the absence of muscle activity recordings; the decoder relies on mapping behaviorally-relevant cortical activity to the inferred EMG activity underlying an intended action. Monkeys were trained at a 2D isometric wrist force task to control a computer cursor by applying force in the flexion, extension, ulnar, and radial directions and execute a center-out task. We used a generic muscle-to-force model based on muscle pulling directions to relate each target force to an optimal EMG pattern that attained the target force while minimizing overall muscle activity. We trained EMG decoders during the target hold periods using a gradient descent algorithm that compared EMG predictions to optimal EMG patterns. Main results We tested this method both offline and online. We quantified both the accuracy of offline force predictions and the ability of a monkey to use these real-time force predictions for closed-loop brain-control. We compared both offline and online results to those obtained with several other force decoders, including an optimal decoder computed from concurrently measured neural and force signals. Significance This novel approach to training an adaptive EMG decoder could make a brain-control FES neuroprosthesis an effective tool to restore the hand function of paralyzed individuals. Clinical implementation would make use of individualized muscle-to-force models. Broad generalization could be achieved by including data from multiple grasping tasks in the training of the neuron-to-muscle decoder. Our approach would make it possible for persons with SCI to grasp objects with their own hands, using near-normal motor intent.

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Intention estimation in brain–machine interfaces

Cited by 64 publications

References 65 publications

A high performing brain–machine interface driven by low-frequency local field potentials alone and together with spikes

A high performing brain–machine interface driven by low-frequency local field potentials alone and together with spikes

A High-Performance Neural Prosthesis Incorporating Discrete State Selection With Hidden Markov Models

Adaptive neuron-to-EMG decoder training for FES neuroprostheses

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