Skip to main content

Thank you for visiting You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Brain–machine interfaces from motor to mood


Brain–machine interfaces (BMIs) create closed-loop control systems that interact with the brain by recording and modulating neural activity and aim to restore lost function, most commonly motor function in paralyzed patients. Moreover, by precisely manipulating the elements within the control loop, motor BMIs have emerged as new scientific tools for investigating the neural mechanisms underlying control and learning. Beyond motor BMIs, recent work highlights the opportunity to develop closed-loop mood BMIs for restoring lost emotional function in neuropsychiatric disorders and for probing the neural mechanisms of emotion regulation. Here we review significant advances toward functional restoration and scientific discovery in motor BMIs that have been guided by a closed-loop control view. By focusing on this unifying view of BMIs and reviewing recent work, we then provide a perspective on how BMIs could extend to the neuropsychiatric domain.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.

Fig. 1: BMIs create closed-loop control systems.
Fig. 2: Motor BMIs for functional restoration and scientific discovery.
Fig. 3: Steps toward realizing mood BMIs for functional restoration and scientific discovery.


  1. 1.

    Green, A. M. & Kalaska, J. F. Learning to move machines with the mind. Trends Neurosci. 34, 61–75 (2011).

    CAS  PubMed  Google Scholar 

  2. 2.

    Orsborn, A. L. & Pesaran, B. Parsing learning in networks using brain-machine interfaces. Curr. Opin. Neurobiol. 46, 76–83 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  3. 3.

    Shenoy, K. V. & Carmena, J. M. Combining decoder design and neural adaptation in brain-machine interfaces. Neuron 84, 665–680 (2014).

    CAS  PubMed  Google Scholar 

  4. 4.

    Shanechi, M. M. Brain-machine interface control algorithms. IEEE Trans. Neural Syst. Rehabil. Eng. 25, 1725–1734 (2017).

    PubMed  Google Scholar 

  5. 5.

    Golub, M. D., Chase, S. M., Batista, A. P. & Yu, B. M. Brain-computer interfaces for dissecting cognitive processes underlying sensorimotor control. Curr. Opin. Neurobiol. 37, 53–58 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  6. 6.

    Moxon, K. A. & Foffani, G. Brain-machine interfaces beyond neuroprosthetics. Neuron 86, 55–67 (2015).

    CAS  PubMed  Google Scholar 

  7. 7.

    Hoang, K. B., Cassar, I. R., Grill, W. M. & Turner, D. A. Biomarkers and stimulation algorithms for adaptive brain stimulation. Front. Neurosci. 11, 564 (2017).

    PubMed  PubMed Central  Google Scholar 

  8. 8.

    Widge, A. S. et al. Treating refractory mental illness with closed-loop brain stimulation: Progress towards a patient-specific transdiagnostic approach. Exp. Neurol. 287, 461–472 (2017).

    PubMed  Google Scholar 

  9. 9.

    Sani, O. G. et al. Mood variations decoded from multi-site intracranial human brain activity. Nat. Biotechnol. 36, 954–961 (2018).

    CAS  PubMed  Google Scholar 

  10. 10.

    Yang, Y., Connolly, A. T. & Shanechi, M. M. A control-theoretic system identification framework and a real-time closed-loop clinical simulation testbed for electrical brain stimulation. J. Neural Eng. 15, 066007 (2018).

    PubMed  Google Scholar 

  11. 11.

    Provenza, N. R. et al. The case for adaptive neuromodulation to treat severe intractable mental disorders. Front. Neurosci. 13, 152 (2019).

    PubMed  PubMed Central  Google Scholar 

  12. 12.

    Whiteford, H. A. et al. Global burden of disease attributable to mental and substance use disorders: findings from the Global Burden of Disease Study 2010. Lancet 382, 1575–1586 (2013).

    PubMed  Google Scholar 

  13. 13.

    Rush, A. J. et al. Acute and longer-term outcomes in depressed outpatients requiring one or several treatment steps: a STAR*D report. Am. J. Psychiatry 163, 1905–1917 (2006).

    PubMed  Google Scholar 

  14. 14.

    Mayberg, H. S. et al. Deep brain stimulation for treatment-resistant depression. Neuron 45, 651–660 (2005).

    CAS  PubMed  Google Scholar 

  15. 15.

    Lozano, A. M. et al. Subcallosal cingulate gyrus deep brain stimulation for treatment-resistant depression. Biol. Psychiatry 64, 461–467 (2008).

    PubMed  Google Scholar 

  16. 16.

    Schlaepfer, T. E. et al. Deep brain stimulation to reward circuitry alleviates anhedonia in refractory major depression. Neuropsychopharmacology 33, 368–377 (2008).

    PubMed  Google Scholar 

  17. 17.

    Malone, D. A. Jr. et al. Deep brain stimulation of the ventral capsule/ventral striatum for treatment-resistant depression. Biol. Psychiatry 65, 267–275 (2009).

    PubMed  Google Scholar 

  18. 18.

    Schlaepfer, T. E., Bewernick, B. H., Kayser, S., Mädler, B. & Coenen, V. A. Rapid effects of deep brain stimulation for treatment-resistant major depression. Biol. Psychiatry 73, 1204–1212 (2013).

    PubMed  Google Scholar 

  19. 19.

    Dougherty, D. D. et al. A randomized sham-controlled trial of deep brain stimulation of the ventral capsule/ventral striatum for chronic treatment-resistant depression. Biol. Psychiatry 78, 240–248 (2015).

    PubMed  Google Scholar 

  20. 20.

    Holtzheimer, P. E. et al. Subcallosal cingulate deep brain stimulation for treatment-resistant depression: a multisite, randomised, sham-controlled trial. Lancet Psychiatry 4, 839–849 (2017).

    PubMed  Google Scholar 

  21. 21.

    Riva-Posse, P. et al. Defining critical white matter pathways mediating successful subcallosal cingulate deep brain stimulation for treatment-resistant depression. Biol. Psychiatry 76, 963–969 (2014).

    PubMed  PubMed Central  Google Scholar 

  22. 22.

    Rao, V. R. et al. Direct electrical stimulation of lateral orbitofrontal cortex acutely improves mood in individuals with symptoms of depression. Curr. Biol. 28, 3893–3902.e4 (2018).

    CAS  PubMed  Google Scholar 

  23. 23.

    Sitaram, R. et al. Closed-loop brain training: the science of neurofeedback. Nat. Rev. Neurosci. 18, 86–100 (2017).

    CAS  PubMed  Google Scholar 

  24. 24.

    Kupfer, D. J., Frank, E. & Phillips, M. L. Major depressive disorder: new clinical, neurobiological, and treatment perspectives. Lancet 379, 1045–1055 (2012).

    PubMed  Google Scholar 

  25. 25.

    Ochsner, K. N., Silvers, J. A. & Buhle, J. T. Functional imaging studies of emotion regulation: a synthetic review and evolving model of the cognitive control of emotion. Ann. NY Acad. Sci. 1251, E1–E24 (2012).

    PubMed  Google Scholar 

  26. 26.

    Gross, J. J. Emotion regulation: current status and future prospects. Psychol. Inq. 26, 1–26 (2015).

    Google Scholar 

  27. 27.

    Etkin, A., Büchel, C. & Gross, J. J. The neural bases of emotion regulation. Nat. Rev. Neurosci. 16, 693–700 (2015).

    CAS  PubMed  Google Scholar 

  28. 28.

    Yuste, R. et al. Four ethical priorities for neurotechnologies and AI. Nature 551, 159–163 (2017).

    CAS  PubMed  Google Scholar 

  29. 29.

    Drevets, W. C. Neuroimaging and neuropathological studies of depression: implications for the cognitive-emotional features of mood disorders. Curr. Opin. Neurobiol. 11, 240–249 (2001).

    CAS  PubMed  Google Scholar 

  30. 30.

    Williams, L. M. Defining biotypes for depression and anxiety based on large-scale circuit dysfunction: a theoretical review of the evidence and future directions for clinical translation. Depress. Anxiety 34, 9–24 (2017).

    PubMed  Google Scholar 

  31. 31.

    Shadmehr, R., Smith, M. A. & Krakauer, J. W. Error correction, sensory prediction, and adaptation in motor control. Annu. Rev. Neurosci. 33, 89–108 (2010).

    CAS  PubMed  Google Scholar 

  32. 32.

    Krakauer, J. W. & Mazzoni, P. Human sensorimotor learning: adaptation, skill, and beyond. Curr. Opin. Neurobiol. 21, 636–644 (2011).

    CAS  PubMed  Google Scholar 

  33. 33.

    Haith, A.M. & Krakauer, J.W. Model-based and model-free mechanisms of human motor learning. in Progress in Motor Control (eds. Richardson, M. J., Riley, M. A. & Shockley, K.) 1–21 (Springer, 2013).

  34. 34.

    Linden, D. E. The challenges and promise of neuroimaging in psychiatry. Neuron 73, 8–22 (2012).

    CAS  PubMed  Google Scholar 

  35. 35.

    Hsieh, H.-L., Wong, Y. T., Pesaran, B. & Shanechi, M. M. Multiscale modeling and decoding algorithms for spike-field activity. J. Neural Eng. 16, 016018 (2019).

    PubMed  Google Scholar 

  36. 36.

    Stavisky, S. D., Kao, J. C., Nuyujukian, P., Ryu, S. I. & Shenoy, K. V. A high performing brain-machine interface driven by low-frequency local field potentials alone and together with spikes. J. Neural Eng. 12, 036009 (2015).

    PubMed  PubMed Central  Google Scholar 

  37. 37.

    Taylor, D. M., Tillery, S. I. H. & Schwartz, A. B. Direct cortical control of 3D neuroprosthetic devices. Science 296, 1829–1832 (2002).

    CAS  PubMed  Google Scholar 

  38. 38.

    Gilja, V. et al. A high-performance neural prosthesis enabled by control algorithm design. Nat. Neurosci. 15, 1752–1757 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  39. 39.

    Hochberg, L. R. et al. Reach and grasp by people with tetraplegia using a neurally controlled robotic arm. Nature 485, 372–375 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  40. 40.

    Collinger, J. L. et al. High-performance neuroprosthetic control by an individual with tetraplegia. Lancet 381, 557–564 (2013).

    PubMed  PubMed Central  Google Scholar 

  41. 41.

    Orsborn, A. L. et al. Closed-loop decoder adaptation shapes neural plasticity for skillful neuroprosthetic control. Neuron 82, 1380–1393 (2014).

    CAS  PubMed  Google Scholar 

  42. 42.

    Gilja, V. et al. Clinical translation of a high-performance neural prosthesis. Nat. Med. 21, 1142–1145 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  43. 43.

    Shanechi, M. M., Orsborn, A. L. & Carmena, J. M. Robust brain-machine interface design using optimal feedback control modeling and adaptive point process filtering. PLoS Comput. Biol. 12, e1004730 (2016).

    PubMed  PubMed Central  Google Scholar 

  44. 44.

    Shanechi, M. M. et al. Rapid control and feedback rates enhance neuroprosthetic control. Nat. Commun. 8, 13825 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  45. 45.

    Shanechi, M. M. et al. A real-time brain-machine interface combining motor target and trajectory intent using an optimal feedback control design. PLoS One 8, e59049 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  46. 46.

    Willett, F. R., Suminski, A. J., Fagg, A. H. & Hatsopoulos, N. G. Improving brain-machine interface performance by decoding intended future movements. J. Neural Eng. 10, 026011 (2013).

    PubMed  PubMed Central  Google Scholar 

  47. 47.

    Cunningham, J. P. et al. A closed-loop human simulator for investigating the role of feedback control in brain-machine interfaces. J. Neurophysiol. 105, 1932–1949 (2011).

    PubMed  Google Scholar 

  48. 48.

    Abbaspourazad, H., Hsieh, H. L. & Shanechi, M. M. A multiscale dynamical modeling and identification framework for spike-field activity. IEEE Trans. Neural Syst. Rehabil. Eng. 27, 1128–1138 (2019).

    PubMed  Google Scholar 

  49. 49.

    Wang, C. & Shanechi, M. M. Estimating multiscale direct causality graphs in neural spike-field networks. IEEE Trans. Neural Syst. Rehabil. Eng. 27, 857–866 (2019).

    PubMed  Google Scholar 

  50. 50.

    Bighamian, R., Wong, Y.T., Pesaran, B. & Shanechi, M.M. Sparse model-based estimation of functional dependence in high-dimensional field and spike multiscale networks. J. Neural Eng. (2019).

    PubMed  Google Scholar 

  51. 51.

    Carmena, J. M. et al. Learning to control a brain-machine interface for reaching and grasping by primates. PLoS Biol. 1, E42 (2003).

    PubMed  PubMed Central  Google Scholar 

  52. 52.

    Moritz, C. T., Perlmutter, S. I. & Fetz, E. E. Direct control of paralysed muscles by cortical neurons. Nature 456, 639–642 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  53. 53.

    Ganguly, K. & Carmena, J. M. Emergence of a stable cortical map for neuroprosthetic control. PLoS Biol. 7, e1000153 (2009).

    PubMed  PubMed Central  Google Scholar 

  54. 54.

    Wander, J. D. et al. Distributed cortical adaptation during learning of a brain-computer interface task. Proc. Natl Acad. Sci. USA 110, 10818–10823 (2013).

    CAS  PubMed  Google Scholar 

  55. 55.

    Ganguly, K., Dimitrov, D. F., Wallis, J. D. & Carmena, J. M. Reversible large-scale modification of cortical networks during neuroprosthetic control. Nat. Neurosci. 14, 662–667 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  56. 56.

    Chase, S. M., Kass, R. E. & Schwartz, A. B. Behavioral and neural correlates of visuomotor adaptation observed through a brain-computer interface in primary motor cortex. J. Neurophysiol. 108, 624–644 (2012).

    PubMed  PubMed Central  Google Scholar 

  57. 57.

    Hwang, E. J., Bailey, P. M. & Andersen, R. A. Volitional control of neural activity relies on the natural motor repertoire. Curr. Biol. 23, 353–361 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  58. 58.

    Koralek, A. C., Costa, R. M. & Carmena, J. M. Temporally precise cell-specific coherence develops in corticostriatal networks during learning. Neuron 79, 865–872 (2013).

    CAS  PubMed  Google Scholar 

  59. 59.

    Sadtler, P. T. et al. Neural constraints on learning. Nature 512, 423–426 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  60. 60.

    Golub, M. D. et al. Learning by neural reassociation. Nat. Neurosci. 21, 607–616 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  61. 61.

    Gulati, T., Guo, L., Ramanathan, D. S., Bodepudi, A. & Ganguly, K. Neural reactivations during sleep determine network credit assignment. Nat. Neurosci. 20, 1277–1284 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  62. 62.

    Todorov, E. & Jordan, M. I. Optimal feedback control as a theory of motor coordination. Nat. Neurosci. 5, 1226–1235 (2002).

    CAS  PubMed  Google Scholar 

  63. 63.

    Golub, M. D., Yu, B. M. & Chase, S. M. Internal models for interpreting neural population activity during sensorimotor control. eLife 4, e10015 (2015).

    PubMed  PubMed Central  Google Scholar 

  64. 64.

    Suminski, A. J., Tkach, D. C., Fagg, A. H. & Hatsopoulos, N. G. Incorporating feedback from multiple sensory modalities enhances brain-machine interface control. J. Neurosci. 30, 16777–16787 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  65. 65.

    O’Doherty, J. E. et al. Active tactile exploration using a brain-machine-brain interface. Nature 479, 228–231 (2011).

    PubMed  PubMed Central  Google Scholar 

  66. 66.

    Klaes, C. et al. A cognitive neuroprosthetic that uses cortical stimulation for somatosensory feedback. J. Neural Eng. 11, 056024 (2014).

    PubMed  PubMed Central  Google Scholar 

  67. 67.

    Ethier, C., Oby, E. R., Bauman, M. J. & Miller, L. E. Restoration of grasp following paralysis through brain-controlled stimulation of muscles. Nature 485, 368–371 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  68. 68.

    Shanechi, M. M., Hu, R. C. & Williams, Z. M. A cortical-spinal prosthesis for targeted limb movement in paralysed primate avatars. Nat. Commun. 5, 3237 (2014).

    PubMed  PubMed Central  Google Scholar 

  69. 69.

    Bouton, C. E. et al. Restoring cortical control of functional movement in a human with quadriplegia. Nature 533, 247–250 (2016).

    CAS  PubMed  Google Scholar 

  70. 70.

    Knudsen, E. B. & Moxon, K. A. Restoration of hindlimb movements after complete spinal cord injury using brain-controlled functional electrical stimulation. Front. Neurosci. 11, 715 (2017).

    PubMed  PubMed Central  Google Scholar 

  71. 71.

    Ajiboye, A. B. et al. Restoration of reaching and grasping movements through brain-controlled muscle stimulation in a person with tetraplegia: a proof-of-concept demonstration. Lancet 389, 1821–1830 (2017).

    PubMed  PubMed Central  Google Scholar 

  72. 72.

    Vaz, A. P., Inati, S. K., Brunel, N. & Zaghloul, K. A. Coupled ripple oscillations between the medial temporal lobe and neocortex retrieve human memory. Science 363, 975–978 (2019).

    CAS  PubMed  Google Scholar 

  73. 73.

    Yang, Y., Sani, O., Chang, E. F. & Shanechi, M. M. Dynamic network modeling and dimensionality reduction for human ECoG activity. J. Neural Eng. 16, 056014 (2019).

    PubMed  Google Scholar 

  74. 74.

    Kirkby, L. A. et al. An amygdala-hippocampus subnetwork that encodes variation in human mood. Cell 175, 1688–1700.e14 (2018).

    CAS  PubMed  Google Scholar 

  75. 75.

    McIntyre, C. C. & Hahn, P. J. Network perspectives on the mechanisms of deep brain stimulation. Neurobiol. Dis. 38, 329–337 (2010).

    PubMed  Google Scholar 

  76. 76.

    Stefanescu, R. A., Shivakeshavan, R. G. & Talathi, S. S. Computational models of epilepsy. Seizure 21, 748–759 (2012).

    PubMed  Google Scholar 

  77. 77.

    Liu, J., Khalil, H. K. & Oweiss, K. G. Model-based analysis and control of a network of basal ganglia spiking neurons in the normal and parkinsonian states. J. Neural Eng. 8, 045002 (2011).

    PubMed  PubMed Central  Google Scholar 

  78. 78.

    Santaniello, S., Fiengo, G., Glielmo, L. & Grill, W. M. Closed-loop control of deep brain stimulation: a simulation study. IEEE Trans. Neural Syst. Rehabil. Eng. 19, 15–24 (2011).

    PubMed  Google Scholar 

  79. 79.

    Yang, Y. & Sani, O.G. Sellers, K., Chang, E.F. & Shanechi, M.M. A novel framework for dynamic modeling of brain-network response to electrical stimulation. Computational and Systems Neuroscience (Cosyne) abstr. II-64 (2018).

  80. 80.

    Yang, Y. et al. Developing a personalized closed-loop controller of medically-induced coma in a rodent model. J. Neural Eng. 16, 036022 (2019).

    PubMed  Google Scholar 

  81. 81.

    Linden, D. E. et al. Real-time self-regulation of emotion networks in patients with depression. PLoS One 7, e38115 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  82. 82.

    Scheinost, D. et al. Orbitofrontal cortex neurofeedback produces lasting changes in contamination anxiety and resting-state connectivity. Transl. Psychiatry 3, e250 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  83. 83.

    Young, K. D. et al. Randomized clinical trial of real-time fMRI amygdala neurofeedback for major depressive disorder: effects on symptoms and autobiographical memory recall. Am. J. Psychiatry 174, 748–755 (2017).

    PubMed  PubMed Central  Google Scholar 

  84. 84.

    Keynan, J. N. et al. Limbic activity modulation guided by functional magnetic resonance imaging-inspired electroencephalography improves implicit emotion regulation. Biol. Psychiatry 80, 490–496 (2016).

    PubMed  Google Scholar 

  85. 85.

    LaConte, S. M., Peltier, S. J. & Hu, X. P. Real-time fMRI using brain-state classification. Hum. Brain Mapp. 28, 1033–1044 (2007).

    PubMed  Google Scholar 

  86. 86.

    Sitaram, R. et al. Real-time support vector classification and feedback of multiple emotional brain states. Neuroimage 56, 753–765 (2011).

    PubMed  Google Scholar 

  87. 87.

    Yang, Y. & Shanechi, M. M. An adaptive and generalizable closed-loop system for control of medically induced coma and other states of anesthesia. J. Neural Eng. 13, 066019 (2016).

    PubMed  Google Scholar 

  88. 88.

    Yang, Y., Chang, E.F. & Shanechi, M.M. Dynamic tracking of non-stationarity in human ECoG activity. in Conf. Proc. IEEE Eng. Med. Biol. Soc. 1660–1663 (IEEE, 2017).

  89. 89.

    Hsieh, H.-L. & Shanechi, M. M. Optimizing the learning rate for adaptive estimation of neural encoding models. PLoS Comput. Biol. 14, e1006168 (2018).

    PubMed  PubMed Central  Google Scholar 

  90. 90.

    Calhoon, G. G. & Tye, K. M. Resolving the neural circuits of anxiety. Nat. Neurosci. 18, 1394–1404 (2015).

    CAS  PubMed  Google Scholar 

  91. 91.

    Dayan, E. & Cohen, L. G. Neuroplasticity subserving motor skill learning. Neuron 72, 443–454 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  92. 92.

    Zavala, B. et al. Cognitive control involves theta power within trials and beta power across trials in the prefrontal-subthalamic network. Brain 141, 3361–3376 (2018).

    PubMed  PubMed Central  Google Scholar 

  93. 93.

    Rudebeck, P. H. & Murray, E. A. The orbitofrontal oracle: cortical mechanisms for the prediction and evaluation of specific behavioral outcomes. Neuron 84, 1143–1156 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  94. 94.

    Kim, S.-P., Simeral, J. D., Hochberg, L. R., Donoghue, J. P. & Black, M. J. Neural control of computer cursor velocity by decoding motor cortical spiking activity in humans with tetraplegia. J. Neural Eng. 5, 455–476 (2008).

    PubMed  PubMed Central  Google Scholar 

  95. 95.

    Eden, U. T., Frank, L. M., Barbieri, R., Solo, V. & Brown, E. N. Dynamic analysis of neural encoding by point process adaptive filtering. Neural Comput. 16, 971–998 (2004).

    PubMed  Google Scholar 

  96. 96.

    Truccolo, W., Eden, U. T., Fellows, M. R., Donoghue, J. P. & Brown, E. N. A point process framework for relating neural spiking activity to spiking history, neural ensemble, and extrinsic covariate effects. J. Neurophysiol. 93, 1074–1089 (2005).

    PubMed  Google Scholar 

  97. 97.

    Kao, J. C. et al. Single-trial dynamics of motor cortex and their applications to brain-machine interfaces. Nat. Commun. 6, 7759 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  98. 98.

    Geller, E. B. et al. Brain-responsive neurostimulation in patients with medically intractable mesial temporal lobe epilepsy. Epilepsia 58, 994–1004 (2017).

    PubMed  Google Scholar 

  99. 99.

    Meidahl, A. C. et al. Adaptive deep brain stimulation for movement disorders: the long road to clinical therapy. Mov. Disord. 32, 810–819 (2017).

    PubMed  PubMed Central  Google Scholar 

  100. 100.

    Shanechi, M. M. et al. Neural population partitioning and a concurrent brain-machine interface for sequential motor function. Nat. Neurosci. 15, 1715–1722 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

Download references


I thank O. G. Sani and Y. Yang in the Shanechi lab for their helpful feedback and contributions. This work was supported in part by the following to M.M.S.: Army Research Office (ARO) under contract W911NF-16-1-0368 as part of the collaboration between US DOD, UK MOD and UK Engineering and Physical Research Council (EPSRC) under the Multidisciplinary University Research Initiative (MURI); Defense Advanced Research Projects Agency (DARPA) under Cooperative Agreement Number W911NF-14-2-0043 issued by the ARO contracting office in support of DARPA’s SUBNETS program; Office of Naval Research (ONR) Young Investigator Program (YIP) under contract N00014-19-1-2128; National Science Foundation (NSF) CAREER Award CCF-1453868; and US National Institutes of Health (NIH) BRAIN grant R01-NS104923. The views, opinions, and/or findings expressed are those of the author and should not be interpreted as representing the official views or policies of the Department of Defense or the US Government.

Author information



Corresponding author

Correspondence to Maryam M. Shanechi.

Ethics declarations

Competing interests

The author declares no competing financial interests.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Shanechi, M.M. Brain–machine interfaces from motor to mood. Nat Neurosci 22, 1554–1564 (2019).

Download citation

Further reading


Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing