Jose M Carmena

CSU Mentor, Long Beach, California, United States

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Publications (112)327.43 Total impact

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    ABSTRACT: Brain-machine interfaces (BMI) hold great po-tential to improve the quality of life of many patients with disabilities. The neural decoder, which expresses the mapping between the neural signals and the subject's motion, plays an important role in BMI systems. Conventional neural decoders are generally in the form of a kinematic Kalman filter which does not possess an explicit mechanism to deal with the unavoid-able mismatch between the biological system and the model of the system used by the decoder. This paper presents a novel design of a neural decoder that uses a one-step model predictive controller to generate a control signal that compensates for the inherent model mismatch. The effectiveness of the proposed decoding algorithm compares favorably to the state-of-the-art Kalman filter in numerical simulations with different degrees of model mismatch.
    Control and Decisions Conference; 12/2014
  • Krishna V Shenoy, Jose M Carmena
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    ABSTRACT: Brain-machine interfaces (BMIs) aim to help people with paralysis by decoding movement-related neural signals into control signals for guiding computer cursors, prosthetic arms, and other assistive devices. Despite compelling laboratory experiments and ongoing FDA pilot clinical trials, system performance, robustness, and generalization remain challenges. We provide a perspective on how two complementary lines of investigation, that have focused on decoder design and neural adaptation largely separately, could be brought together to advance BMIs. This BMI paradigm should also yield new scientific insights into the function and dysfunction of the nervous system. VIDEO ABSTRACT: Copyright © 2014 Elsevier Inc. All rights reserved.
    Neuron 11/2014; 84(4):665-680. · 15.77 Impact Factor
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    ABSTRACT: A major hurdle in brain-machine interfaces (BMI) is the lack of an implantable neural interface system that remains viable for a substantial fraction of the user's lifetime. Recently, sub-mm implantable, wireless electromagnetic (EM) neural interfaces have been demonstrated in an effort to extend system longevity. However, EM systems do not scale down in size well due to the severe inefficiency of coupling radio-waves at those scales within tissue. This paper explores fundamental system design trade-offs as well as size, power, and bandwidth scaling limits of neural recording systems built from low-power electronics coupled with ultrasonic power delivery and backscatter communication. Such systems will require two fundamental technology innovations: 1) 10-100μm scale, free-floating, independent sensor nodes, or neural dust, that detect and report local extracellular electrophysiological data via ultrasonic backscattering, and 2) a sub-cranial ultrasonic interrogator that establishes power and communication links with the neural dust. We provide experimental verification that the predicted scaling effects follow theory; (127μm)(3) neural dust motes immersed in water 3cm from the interrogator couple with 0.002064% power transfer efficiency and 0.04246ppm backscatter, resulting in a maximum received power of ∼0.5μW with ∼1 nW of change in backscatter power with neural activity. The high efficiency of ultrasonic transmission can enable the scaling of the sensing nodes down to 10's of μm. We conclude with a brief discussion of the application of neural dust for both central and peripheral nervous system recordings, and perspectives on future research directions.
    Journal of neuroscience methods. 08/2014;
  • IEEE International Symposium on Antennas and Propagation; 07/2014
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    ABSTRACT: Brain-Machine Interfaces (BMI) have strong po-tential to benefit a large number of disabled people but current decoding algorithms suffer from the following shortcomings. First, BMI decoding algorithms are often trained offline, but this paradigm ignores the discrepancy between the Manual Control (MC) and the Brain Control (BC) modes of operation. Second, the standard neural decoder, the Kalman filter, does not explicitly take into account the control of movements by neural activity. To address these problems, we propose a biologically motivated neural decoder structure by explicitly adding a control signal and unmeasureable neural activity. Since the parameter estimation problem is underdetermined, we propose a new parameter estimation method that minimizes the discrepancy between the MC and BC. We demonstrate the effectiveness of our methods by synthesizing MC and BC data in a Linear Quadratic (LQ) optimal control setting with a partial loss of neural control in BC, and show that the proposed decoder is more robust to a partial loss of neural control than a standard Kalman filter that does not utilize any reparameterizations.
    European Control Conference, Strasbourg, France; 06/2014
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    ABSTRACT: Neuroplasticity may play a critical role in developing robust, naturally controlled neuroprostheses. This learning, however, is sensitive to system changes such as the neural activity used for control. The ultimate utility of neuroplasticity in real-world neuroprostheses is thus unclear. Adaptive decoding methods hold promise for improving neuroprosthetic performance in nonstationary systems. Here, we explore the use of decoder adaptation to shape neuroplasticity in two scenarios relevant for real-world neuroprostheses: nonstationary recordings of neural activity and changes in control context. Nonhuman primates learned to control a cursor to perform a reaching task using semistationary neural activity in two contexts: with and without simultaneous arm movements. Decoder adaptation was used to improve initial performance and compensate for changes in neural recordings. We show that beneficial neuroplasticity can occur alongside decoder adaptation, yielding performance improvements, skill retention, and resistance to interference from native motor networks. These results highlight the utility of neuroplasticity for real-world neuroprostheses.
    Neuron 06/2014; 82(6):1380-93. · 15.77 Impact Factor
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    ABSTRACT: Closed-loop decoder adaptation (CLDA) is an emerging paradigm for both improving and maintaining online performance in brain-machine interfaces (BMIs). The time required for initial decoder training and any subsequent decoder recalibrations could be potentially reduced by performing continuous adaptation, in which decoder parameters are updated at every time step during these procedures, rather than waiting to update the decoder at periodic intervals in a more batch-based process. Here, we present recursive maximum likelihood (RML), a CLDA algorithm that performs continuous adaptation of a Kalman filter decoder's parameters. We demonstrate that RML possesses a variety of useful properties and practical algorithmic advantages. First, we show how RML leverages the accuracy of updates based on a batch of data while still adapting parameters on every time step. Second, we illustrate how the RML algorithm is parameterized by a single, intuitive half-life parameter that can be used to adjust the rate of adaptation in real time. Third, we show how even when the number of neural features is very large, RML's memory-efficient recursive update rules can be reformulated to be computationally fast so that continuous adaptation is still feasible. To test the algorithm in closed-loop experiments, we trained three macaque monkeys to perform a center-out reaching task by using either spiking activity or local field potentials to control a 2D computer cursor. RML achieved higher levels of performance more rapidly in comparison to a previous CLDA algorithm that adapts parameters on a more intermediate timescale. Overall, our results indicate that RML is an effective CLDA algorithm for achieving rapid performance acquisition using continuous adaptation.
    Neural Computation 06/2014; · 1.76 Impact Factor
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    ABSTRACT: Wireless body-centric sensing systems have an important role in the fields of biomedicine, personal healthcare, safety, and security. Body-centric radio-frequency identification (RFID) technology provides a wireless and maintenance-free communication link between the human body and the surroundings through wearable and implanted antennas. This enables real-time monitoring of human vital signs everywhere. Seamlessly integrated wearable and implanted miniaturized antennas thus have the potential to revolutionize the everyday life of people, and to contribute to independent living. Low-cost and low-power system solutions will make widespread use of such technology become reality. The primary target applications for this research are body-centric sensing systems and the relatively new interdisciplinary field of wireless brain-machine interface (BMI) systems. Providing a direct wireless pathway between the brain and an external device, a wireless brain-machine interface holds an enormous potential for helping people suffering from severely disabling neurological conditions to communicate and manage their everyday life more independently. In this paper, we discuss RFID-inspired wireless brain-machine interface systems. We demonstrate that mm-size loop implanted antennas are capable of efficiently coupling to an external transmitting loop antenna through an inductive link. In addition, we focus on wearable antennas based on electrically conductive textiles and threads, and present design guidelines for their use as wearable-antenna conductive elements. Overall, our results constitute an important milestone in the development of wireless brain-machine interface systems, and a new era of wireless body-centric systems.
    IEEE Antennas and Propagation Magazine 06/2014; 56(1):271. · 1.18 Impact Factor
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    ABSTRACT: Brain-machine interfaces are not only promising for neurological applications, but also powerful for investigating neuronal ensemble dynamics during learning. We trained mice to operantly control an auditory cursor using spike-related calcium signals recorded with two-photon imaging in motor and somatosensory cortex. Mice rapidly learned to modulate activity in layer 2/3 neurons, evident both across and within sessions. Learning was accompanied by modifications of firing correlations in spatially localized networks at fine scales.
    Nature Neuroscience 04/2014; · 15.25 Impact Factor
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    ABSTRACT: Brain-machine interfaces (BMIs) are dynamical systems whose properties ultimately influence performance. For instance, a 2D BMI in which cursor position is controlled using a Kalman filter (KF) will, by default, create an attractor point that "pulls" the cursor to particular points in the workspace. If created unintentionally, such effects may not be beneficial for BMI performance. However, there have been few empirical studies exploring how various dynamical effects of closed-loop BMIs ultimately influence performance. In this work, we utilize experimental data from two macaque monkeys operating a closed-loop BMI to reach to 2D targets and show that certain dynamical properties correlate with performance loss. We also show that other dynamical properties represent tradeoffs between naturally competing objectives, such as speed versus accuracy. These findings highlight the importance of fine-tuning the dynamical properties of closed-loop BMIs to optimize taskspecific performance.
    IEEE transactions on neural systems and rehabilitation engineering: a publication of the IEEE Engineering in Medicine and Biology Society 03/2014; · 2.42 Impact Factor
  • IEEE International Solid-State Circuits Conference (ISSCC); 02/2014
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    ABSTRACT: Objective. Intracortical brain-machine interfaces (BMIs) have predominantly utilized spike activity as the control signal. However, an increasing number of studies have shown the utility of local field potentials (LFPs) for decoding motor related signals. Currently, it is unclear how well different LFP frequencies can serve as features for continuous, closed-loop BMI control. Approach. We demonstrate 2D continuous LFP-based BMI control using closed-loop decoder adaptation, which adapts decoder parameters to subject-specific LFP feature modulations during BMI control. We trained two macaque monkeys to control a 2D cursor in a center-out task by modulating LFP power in the 0-150 Hz range. Main results. While both monkeys attained control, they used different strategies involving different frequency bands. One monkey primarily utilized the low-frequency spectrum (0-80 Hz), which was highly correlated between channels, and obtained proficient performance even with a single channel. In contrast, the other monkey relied more on higher frequencies (80-150 Hz), which were less correlated between channels, and had greater difficulty with control as the number of channels decreased. We then restricted the monkeys to use only various sub-ranges (0-40, 40-80, and 80-150 Hz) of the 0-150 Hz band. Interestingly, although both monkeys performed better with some sub-ranges than others, they were able to achieve BMI control with all sub-ranges after decoder adaptation, demonstrating broad flexibility in the frequencies that could potentially be used for LFP-based BMI control. Significance. Overall, our results demonstrate proficient, continuous BMI control using LFPs and provide insight into the subject-specific spectral patterns of LFP activity modulated during control.
    Journal of Neural Engineering 02/2014; 11(2):026002. · 3.28 Impact Factor
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    ABSTRACT: Electrical microstimulation studies provide some of the most direct evidence for the neural representation of muscle synergies. These synergies, i.e., coordinated activations of groups of muscles, have been proposed as building blocks for the construction of motor behaviors by the nervous system. Intraspinal or intracortical microstimulation (ICMS) has been shown to evoke muscle patterns that can be resolved into a small set of synergies similar to those seen in natural behavior. However, questions remain about the validity of microstimulation as a probe of neural function, particularly given the relatively long trains of supratheshold stimuli used in these studies. Here, we examined whether muscle synergies evoked during ICMS in two rhesus macaques were similarly encoded by nearby motor cortical units during a purely voluntary behavior involving object reach, grasp, and carry movements. At each microstimulation site we identified the synergy most strongly evoked among those extracted from muscle patterns evoked over all microstimulation sites. For each cortical unit recorded at the same microstimulation site, we then identified the synergy most strongly encoded among those extracted from muscle patterns recorded during the voluntary behavior. We found that the synergy most strongly evoked at an ICMS site matched the synergy most strongly encoded by proximal units more often than expected by chance. These results suggest a common neural substrate for microstimulation-evoked motor responses and for the generation of muscle patterns during natural behaviors.
    Frontiers in Computational Neuroscience 01/2014; 8:20. · 2.48 Impact Factor
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    ABSTRACT: Young Hwan Chang1 (yhchangatberkeley.edu), Jim Korkola2 (korkolaatohsu.edu), Dhara N. Amin3 (dhara.aminatucsf.edu), Mark M. Moasser3 (mmoasseratmedicine.ucsf.edu), Jose M. Carmena4 (carmenaateecs.berkeley.edu), Joe W. Gray2 (grayjoatohsu.edu) and Claire J. Tomlin1,5 (tomlinateecs.berkeley.edu) 1 Department of Electrical Engineering and Computer Sciences, University of California, Berkeley; 2 Dept. of Biomedical Engineering and the Center for Spatial Systems Biomedicine, OHSU; 3 Department of Medicine, Helen Diller Family Comprehensive Cancer Center, UCSF; 4 Dept. of EECS, Helen Wills Neuroscience Institute,UC Berkeley and UCB/UCSF Graduate program in BIOE ↵* Corresponding author; email: tomlinateecs.berkeley.edu AbstractWith the advent of high-throughput measurement techniques, scientists and engineers are starting to grapple with massive data sets and encountering challenges with how to organize, process and extract information into meaningful structures. Multidimensional spatio-temporal biological data sets such as time series gene expression with various perturbations with different cell lines, or neural spike data sets across many experimental trials have the potential to acquire insight across multiple dimensions. For this potential to be realized, we need a suitable representation to turn data into insight. Since a wide range of experiments and the (unknown) complexity of underlying system make biological data more heterogeneous than those in other fields, we propose the method based on Robust Principal Component Analysis (RPCA), which is well suited for extracting principal components where we have corrupted observations. The proposed method provides us a new representation of these data sets which consists of its common and aberrant response. This representation might help users to acquire a new insight from data. %For example, identifying common event-related neural features across many experimental trials can be used as a signature to detect discrete events or state transitions. Also, the proposed method can be useful to biologists in clustering and analyzing gene expression time series data with a new perspective, for example, it can not only extract canonical cell signaling response but also inform them to get insight into the heterogeneity of different responses across different cell lines.Received April 22, 2014.Accepted April 28, 2014.textcopyright 2014, Published by Cold Spring Harbor Laboratory PressThe copyright holder for this pre-print is the author. All rights reserved. The material may not be redistributed, re-used or adapted without the authortextquoterights permission.
    bioRxiv. 01/2014;
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    ABSTRACT: We analyze the power and voltage transfer in a wireless link from an on-body transmit antenna to 1×1×1 mm3 antenna in a cortical implant to provide power and data telemetry for a battery-free brain-machine interface microelectronic system. We compare the wireless link performance with regular, segmented, and tilted transmit loop antennas. Moreover, we analyze the performance improvement achieved by inserting a magneto-dielectric core in the implant antenna. We also attest the simulation model through measurements in a liquid head phantom.
    IEEE MTT-S International Microwave Workshop Series on RF and Wireless Technologies for Biomedical and Healthcare Applications, Singapore; 12/2013
  • Aaron C Koralek, Rui M Costa, Jose M Carmena
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    ABSTRACT: It has been postulated that selective temporal coordination between neurons and development of functional neuronal assemblies are fundamental for brain function and behavior. Still, there is little evidence that functionally relevant coordination emerges preferentially in neuronal assemblies directly controlling behavioral output. We investigated coherence between primary motor cortex and the dorsal striatum as rats learn an abstract operant task. Striking coherence developed between these regions during learning. Interestingly, coherence was selectively increased in cells controlling behavioral output relative to adjacent cells. Furthermore, the temporal offset of these interactions aligned closely with corticostriatal conduction delays, demonstrating highly precise timing. Spikes from either region were followed by a consistent phase in the other, suggesting that network feedback reinforces coherence. Together, these results demonstrate that temporally precise coherence develops during learning specifically in output-relevant neuronal populations and further suggest that correlations in oscillatory activity serve to synchronize widespread brain networks to produce behavior.
    Neuron 08/2013; · 15.77 Impact Factor
  • Source
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    ABSTRACT: A major hurdle in brain-machine interfaces (BMI) is the lack of an implantable neural interface system that remains viable for a lifetime. This paper explores the fundamental system design trade-offs and ultimate size, power, and bandwidth scaling limits of neural recording systems built from low-power CMOS circuitry coupled with ultrasonic power delivery and backscatter communication. In particular, we propose an ultra-miniature as well as extremely compliant system that enables massive scaling in the number of neural recordings from the brain while providing a path towards truly chronic BMI. These goals are achieved via two fundamental technology innovations: 1) thousands of 10 - 100 \mu m scale, free-floating, independent sensor nodes, or neural dust, that detect and report local extracellular electrophysiological data, and 2) a sub-cranial interrogator that establishes power and communication links with the neural dust.
    07/2013;
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    ABSTRACT: We present a method for decreasing the duration of artifacts present during intra-cortical microstimulation (ICMS) recordings by using techniques developed for digital communications. We replace the traditional monophasic or biphasic current stimulation pulse with a patterned pulse stream produced by a Zero Forcing Equalizer (ZFE) filter after characterizing the artifact as a communications channel. The results find that using the ZFE stimulus has the potential to reduce artifact width by more than 70%. Considerations for the hardware implementation of the equalizer are presented.
    Conference proceedings: ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference 07/2013; 2013:245-248.
  • Siddharth Dangi, Suraj Gowda, Jose M Carmena
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    ABSTRACT: Closed-loop decoder adaptation (CLDA) is an emerging paradigm for improving or maintaining the online performance of brain-machine interfaces (BMIs). Here, we present Likelihood Gradient Ascent (LGA), a novel CLDA algorithm for a Kalman filter (KF) decoder that uses stochastic, gradient-based corrections to update KF parameters during closed-loop BMI operation. LGA's gradient-based paradigm presents a variety of potential advantages over other "batch" CLDA methods, including the ability to update decoder parameters on any time-scale, even on every decoder iteration. Using a closed-loop BMI simulator, we compare the LGA algorithm to the Adaptive Kalman Filter (AKF), a partially gradient-based CLDA algorithm that has been previously tested in non-human primate experiments. In contrast to the AKF's separate mean-squared error objective functions, LGA's update rules are derived directly from a single log likelihood objective, making it one step towards a potentially optimal continuously adaptive CLDA algorithm for BMIs.
    Conference proceedings: ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference 07/2013; 2013:2768-2771.
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    ABSTRACT: Volitional control of neural activity lies at the heart of the Brain-Machine Interface (BMI) paradigm. In this work we investigated if subdural field potentials recorded by electrodes < 1mm apart can be decoupled through closed-loop BMI learning. To this end, we fabricated custom, flexible microelectrode arrays with 200 µm electrode pitch and increased the effective electrode area by electrodeposition of platinum black to reduce thermal noise. We have chronically implanted these arrays subdurally over primary motor cortex (M1) of 5 male Long-Evans Rats and monitored the electrochemical electrode impedance in vivo to assess the stability of these neural interfaces. We successfully trained the rodents to perform a one-dimensional center-out task using closed-loop brain control to adjust the pitch of an auditory cursor by differentially modulating high gamma (70-110 Hz) power on pairs of surface microelectrodes that were separated by less than 1 mm.
    Conference proceedings: ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference 07/2013; 2013:5622-5625.

Publication Stats

2k Citations
327.43 Total Impact Points

Institutions

  • 2011–2014
    • CSU Mentor
      Long Beach, California, United States
  • 2007–2014
    • University of California, Berkeley
      • Department of Electrical Engineering and Computer Sciences
      Berkeley, California, United States
    • Technion - Israel Institute of Technology
      • Faculty of Mechanical Engineering
      Haifa, Haifa District, Israel
  • 2012
    • University of California, San Francisco
      • Department of Neurology
      San Francisco, California, United States
  • 2010–2011
    • San Francisco VA Medical Center
      San Francisco, California, United States
    • Eawag: Das Wasserforschungs-Institut des ETH-Bereichs
      Duebendorf, Zurich, Switzerland
    • Atomic Energy and Alternative Energies Commission
      Fontenay, Île-de-France, France
  • 2006–2007
    • Duke University
      • Department of Biomedical Engineering (BME)
      Durham, North Carolina, United States
    • University of Florida
      • Department of Electrical and Computer Engineering
      Gainesville, FL, United States
  • 2001–2005
    • Duke University Medical Center
      • Department of Neurobiology
      Durham, NC, United States
  • 2004
    • University of Southern Denmark
      Odense, South Denmark, Denmark
  • 2000–2002
    • The University of Edinburgh
      • • School of Informatics
      • • Institute of Perception, Action and Behaviour (IPAB)
      Edinburgh, SCT, United Kingdom