Brain–machine interface articles within Nature Communications

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  • Article
    | Open Access

    Previous work has shown speech decoding in the human brain for the development of neural speech prostheses. Here the authors show that high density µECoG electrodes can record at micro-scale spatial resolution to improve neural speech decoding.

    • Suseendrakumar Duraivel
    • , Shervin Rahimpour
    •  & Gregory B. Cogan

  • Article
    | Open Access

    In-ear visual and auditory brain-computer interfaces typically have issues with poor interfacial adhesion or user irritation. Here, Wang et al. presents an in-ear hollow bioelectronic device that adaptively conforms to the ear canal, under electrothermal actuation, for electroencephalogram sensing.

    • Zhouheng Wang
    • , Nanlin Shi
    •  & Xue Feng

  • Article
    | Open Access

    Despite the rapid progress and interest in brain-machine interfaces that restore motor function, the performance of prosthetic fingers and limbs has yet to mimic native function. Here, the authors demonstrate that shallow-layer neural network decoders outperform and enable higher velocity finger movements than the current linear decoding standard.

    • Matthew S. Willsey
    • , Samuel R. Nason-Tomaszewski
    •  & Cynthia A. Chestek

  • Comment
    | Open Access

    Two papers published in June 2021 used a two-photon microscope or one-photon miniature microscope to interrogate the motor cortex in behaving macaque monkeys. The imaging was performed over several months, and the direction of natural arm reaching was decoded from the population activity.

    • Masanori Matsuzaki
    •  & Teppei Ebina

  • Article
    | Open Access

    Surface two-photon imaging of the brain cannot access somatic calcium signals of neurons from deep layers of the macaque cortex. Here, the authors present an implant and imaging system for chronic motion-stabilized two-photon imaging of dendritic calcium signals to drive an optical brain-computer interface in macaques.

    • Eric M. Trautmann
    • , Daniel J. O’Shea
    •  & Krishna V. Shenoy

  • Article
    | Open Access

    Deep brain stimulation and epidural electrical stimulation of the spinal cord enable locomotion in humans with spinal cord injury (SCI) but the potential synergy between both approaches is unclear. The authors show that a complex technological approach is required to enable volitional walking in rats with SCI.

    • Marco Bonizzato
    • , Nicholas D. James
    •  & Gregoire Courtine

  • Article
    | Open Access

    How does the brain control the complex movements of hands? Here, by tracking human hand kinematics and applying multidimensional reduction techniques, the authors provide evidence that grasping involves a complex control system that regulates even the most subtle aspects of hand movement.

    • Yuke Yan
    • , James M. Goodman
    •  & Sliman J. Bensmaia

  • Article
    | Open Access

    Brain-computer interface (BCI) can improve motor skills on stroke patients. This study shows that BCI-controlled neuromuscular electrical stimulation therapy can cause cortical reorganization due to activation of efferent and afferent pathways, and this effect can be long lasting in a brain region specific manner.

    • A. Biasiucci
    • , R. Leeb
    •  & J. d. R. Millán

  • Article
    | Open Access

    Previous studies have shown short-term plasticity in single neurons or local field potentials during brain-machine interface (BMI) training. Here the authors report long-term changes in functional connectivity of motor cortex neuronal ensemble activity as chronically amputated monkeys learn to operate a BMI.

    • Karthikeyan Balasubramanian
    • , Mukta Vaidya
    •  & Nicholas G. Hatsopoulos

  • Article
    | Open Access

    Brain machine interfaces (BMI) enable sensorimotor control of movement yet the parameters that may affect these pathways are not known. Here the authors show systematically that increasing the rate of control from brain as well as feedback rates to the subject results in better performance on a BMI task in monkeys.

    • Maryam M. Shanechi
    • , Amy L. Orsborn
    •  & Jose M. Carmena

  • Article
    | Open Access

    Brain-machine interfaces (BMI) depend on algorithms to decode neural signals, but these decoders cope poorly with signal variability. Here, authors report a BMI decoder which circumvents these problems by using a large and perturbed training dataset to improve performance with variable neural signals.

    • David Sussillo
    • , Sergey D. Stavisky
    •  & Krishna V. Shenoy

  • Article
    | Open Access

    In online experiments with monkeys the authors demonstrate, for the first time, that incorporating neural dynamics substantially improves brain–machine interface performance. This result is consistent with a framework hypothesizing that motor cortex is a dynamical machine that generates movement.

    • Jonathan C. Kao
    • , Paul Nuyujukian
    •  & Krishna V. Shenoy

  • Article
    | Open Access

    The use of local field potential (LFP) brain signals may allow development of more efficient and robust neural prosthetic devices. Here, Hall et al. develop a method for estimation and biofeedback control of single-neuron firing rates using signals extracted from multiple low-frequency LFPs.

    • Thomas M. Hall
    • , Kianoush Nazarpour
    •  & Andrew Jackson

  • Article |

    Speech is encoded by the firing patterns of speech-controlling neurons in different regions of the brain, which Tankus and colleagues analyse in this study. They find highly specific encoding of vowels in medial–frontal neurons and nonspecific tuning in superior temporal gyrus neurons.

    • Ariel Tankus
    • , Itzhak Fried
    •  & Shy Shoham

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