Efficient Decoding With Steady-State Kalman Filter in Neural Interface Systems

Malik, W.Q.; Truccolo, Wilson; Brown, Emery N.; Hochberg, Leigh R.

doi:10.1109/tnsre.2010.2092443

Cited by 88 publications

(69 citation statements)

References 34 publications

(39 reference statements)

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“…For computational efficiency, we used the steady-state Kalman filter (Dethier et al, 2013; Malik et al, 2011) for online decoding. The only difference between the steady-state filter and the standard filter used offline is that the steady-state Kalman gain is pre-computed during training.…”

Section: Methodsmentioning

confidence: 99%

Neural control of finger movement via intracortical brain–machine interface

Irwin

Schroeder

et al. 2017

J. Neural Eng.

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Objective Intracortical brain-machine interfaces (BMIs) are a promising source of prosthesis control signals for individuals with severe motor disabilities. Previous BMI studies have primarily focused on predicting and controlling whole-arm movements; precise control of hand kinematics, however, has not been fully demonstrated. Here, we investigate the continuous decoding of precise finger movements in rhesus macaques. Approach In order to elicit precise and repeatable finger movements, we have developed a novel behavioral task paradigm which requires the subject to acquire virtual fingertip position targets. In the physical control condition, four rhesus macaques performed this task by moving all four fingers together in order to acquire a single target. This movement was equivalent to controlling the aperture of a power grasp. During this task performance, we recorded neural spikes from intracortical electrode arrays in primary motor cortex. Main Results Using a standard Kalman filter, we could reconstruct continuous finger movement offline with an average correlation of ρ = 0.78 between actual and predicted position across four rhesus macaques. For two of the monkeys, this movement prediction was performed in real-time to enable direct brain control of the virtual hand. Compared to physical control, neural control performance was slightly degraded; however, the monkeys were still able to successfully perform the task with an average target acquisition rate of 83.1%. The monkeys’ ability to arbitrarily specify fingertip position was also quantified using an information throughput metric. During brain control task performance, the monkeys achieved an average 1.01 bits/s throughput, similar to that achieved in previous studies which decoded upper-arm movements to control computer cursors using a standard Kalman filter. Significance This is, to our knowledge, the first demonstration of brain control of finger-level fine motor skills. We believe that these results represent an important step towards full and dexterous control of neural prosthetic devices.

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Section: Methodsmentioning

confidence: 99%

Neural control of finger movement via intracortical brain–machine interface

Irwin

Schroeder

et al. 2017

J. Neural Eng.

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“…To find each channel's contribution to a particular decoder we first converted that Kalman filter to a closed-form steady-state [57],…”

Section: Methodsmentioning

confidence: 99%

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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“…if the linear system model is time-invariant. It has been shown in Malik et al (2011) that the steady-state Kalman filter significantly reduces the computational burden at almost no sacrifice of estimation accuracy. Hence, for the ease of implementation and in order to keep a time-invariant observer gain approach, the observer gain L i in (15) for each linear subsystem i is determined by the steady-state solution of the timevariant Kalman filter, see e.g.…”

Section: Ts Kalman Filtermentioning

confidence: 99%