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Bacomics: a comprehensive cross area originating in the studies of various brain–apparatus conversations

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A Correction to this article was published on 10 August 2022

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Abstract

The brain is the most important organ of the human body, and the conversations between the brain and an apparatus can not only reveal a normally functioning or a dysfunctional brain but also can modulate the brain. Here, the apparatus may be a nonbiological instrument, such as a computer, and the consequent brain–computer interface is now a very popular research area with various applications. The apparatus may also be a biological organ or system, such as the gut and muscle, and their efficient conversations with the brain are vital for a healthy life. Are there any common bases that bind these different scenarios? Here, we propose a new comprehensive cross area: Bacomics, which comes from brain–apparatus conversations (BAC) + omics. We take Bacomics to cover at least three situations: (1) The brain is normal, but the conversation channel is disabled, as in amyotrophic lateral sclerosis. The task is to reconstruct or open up new channels to reactivate the brain function. (2) The brain is in disorder, such as in Parkinson’s disease, and the work is to utilize existing or open up new channels to intervene, repair and modulate the brain by medications or stimulation. (3) Both the brain and channels are in order, and the goal is to enhance coordinated development between the brain and apparatus. In this paper, we elaborate the connotation of BAC into three aspects according to the information flow: the issue of output to the outside (BAC-1), the issue of input to the brain (BAC-2) and the issue of unity of brain and apparatus (BAC-3). More importantly, there are no less than five principles that may be taken as the cornerstones of Bacomics, such as feedforward and feedback control, brain plasticity, harmony, the unity of opposites and systems principles. Clearly, Bacomics integrates these seemingly disparate domains, but more importantly, opens a much wider door for the research and development of the brain, and the principles further provide the general framework in which to realize or optimize these various conversations.

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Fig. 1
Fig. 2

(the right panel is reproduced with permission from Wu et al. 2009)

Fig. 3
Fig. 4

(reproduced, with permission, from Gong et al. 2015)

Fig. 5

(reproduced, with permission, from Gong et al. 2015). (Color figure online)

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References

  • Abdulkader SN, Atia A, Mostafa M-SM (2015) Brain computer interfacing: applications and challenges. Egypt Inform J 16:213–230

    Google Scholar 

  • Agusti A, Garcia-Pardo MP, Lopez-Almela I, Campillo I, Maes M, Romani-Perez M, Sanz Y (2018) Interplay between the gut–brain axis, obesity and cognitive function. Front Neurosci 12:155

    PubMed  PubMed Central  Google Scholar 

  • Ajiboye AB et al (2017) 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

    PubMed  PubMed Central  Google Scholar 

  • Ang KK et al (2009) A clinical study of motor imagery-based brain-computer interface for upper limb robotic rehabilitation. In: Conference proceedings: annual international conference of the IEEE engineering in medicine and biology society, pp 5981–5984

  • Ang KK, Guan C (2013) Brain–computer interface in stroke rehabilitation. J Comput Sci Eng 7:139–146

    Google Scholar 

  • Ang KK, Guan C (2017) EEG-based strategies to detect motor imagery for control and rehabilitation. IEEE Trans Neural Syst Rehabil Eng Publ IEEE Eng Med Biol Soc 25:392–401

    Google Scholar 

  • Anguera JA et al (2013) Video game training enhances cognitive control in older adults. Nature 501:97–101

    CAS  PubMed  PubMed Central  Google Scholar 

  • Anumanchipalli GK, Chartier J, Chang EF (2019) Speech synthesis from neural decoding of spoken sentences. Nature 568:493–498

    CAS  PubMed  Google Scholar 

  • Arico P, Borghini G, Di Flumeri G, Sciaraffa N, Babiloni F (2018) Passive BCI beyond the lab: current trends and future directions. Physiol Meas 39:08TR02

    CAS  PubMed  Google Scholar 

  • Arns M, de Ridder S, Strehl U, Breteler M, Coenen A (2009) Efficacy of neurofeedback treatment in ADHD: the effects on inattention, impulsivity and hyperactivity: a meta-analysis. Clin EEG Neurosci 40:180–189

    PubMed  Google Scholar 

  • Bao C et al (2017) Effect of electro-acupuncture and moxibustion on brain connectivity in patients with Crohn’s disease: a resting-state fMRI study. Front Hum Neurosci 11:559

    PubMed  PubMed Central  Google Scholar 

  • Bashivan P, Kar K, DiCarlo JJ (2019) Neural population control via deep image synthesis. Science 364:eaav9436

    CAS  PubMed  Google Scholar 

  • Bassett DS, Wymbs NF, Porter MA, Mucha PJ, Carlson JM, Grafton ST (2011) Dynamic reconfiguration of human brain networks during learning. Proc Natl Acad Sci USA 108:7641–7646

    CAS  PubMed  PubMed Central  Google Scholar 

  • Bates FC, Horvath T (1971) Discrimination learning with rhythmic and nonrhythmic background music. Percept Mot Skills 33:1123–1126

    Google Scholar 

  • Benabid AL, Chabardes S, Mitrofanis J, Pollak P (2009) Deep brain stimulation of the subthalamic nucleus for the treatment of Parkinson’s disease. Lancet Neurol 8:67–81

    PubMed  Google Scholar 

  • Berman SM, Kuczenski R, McCracken JT, London ED (2009) Potential adverse effects of amphetamine treatment on brain and behavior: a review. Mol Psychiatry 14:123–142

    CAS  PubMed  Google Scholar 

  • Beveridge R, Wilson S, Callaghan M, Coyle D (2019) Neurogaming with motion-onset visual evoked potentials (mVEPs): adults versus teenagers. IEEE Trans Neural Syst Rehabil Eng Publ IEEE Eng Med Biol Soc 27:572–581

    Google Scholar 

  • Biasiucci A et al (2018) Brain-actuated functional electrical stimulation elicits lasting arm motor recovery after stroke. Nat Commun 9:2421

    CAS  PubMed  PubMed Central  Google Scholar 

  • Bierer JA, Middlebrooks JC (2002) Auditory cortical images of cochlear-implant stimuli: dependence on electrode configuration. J Neurophysiol 87:478–492

    PubMed  Google Scholar 

  • Blankertz B et al (2016) The Berlin brain–computer interface: progress beyond communication and control. Front Neurosci 10:530

    PubMed  PubMed Central  Google Scholar 

  • Bonaz B, Bazin T, Pellissier S (2018) The vagus nerve at the interface of the microbiota–gut–brain axis. Front Neurosci 12:49

    PubMed  PubMed Central  Google Scholar 

  • Bouton CE et al (2016) Restoring cortical control of functional movement in a human with quadriplegia. Nature 533:247–250

    CAS  PubMed  Google Scholar 

  • Bruhl AB, Sahakian BJ (2016) Drugs, games, and devices for enhancing cognition: implications for work and society. Ann N Y Acad Sci 1369:195–217

    PubMed  Google Scholar 

  • Capogrosso M et al (2016) A brain–spine interface alleviating gait deficits after spinal cord injury in primates. Nature 539:284–288

    PubMed  PubMed Central  Google Scholar 

  • Catrambone V, Greco A, Vanello N, Scilingo EP, Valenza G (2019) Time-resolved directional brain–heart interplay measurement through synthetic data generation models. Ann Biomed Eng 47:1479–1489

    PubMed  Google Scholar 

  • Caudill MA, Strupp BJ, Muscalu L, Nevins JEH, Canfield RL (2018) Maternal choline supplementation during the third trimester of pregnancy improves infant information processing speed: a randomized, double-blind, controlled feeding study. FASEB J Off Publ Fed Am Soc Exp Biol 32:2172–2180

    CAS  Google Scholar 

  • Cerdo T, Ruiz A, Suarez A, Campoy C (2017) Probiotic, prebiotic, and brain development. Nutrients 9:E1247

    PubMed  Google Scholar 

  • Chan MY, Na J, Agres PF, Savalia NK, Park DC, Wig GS (2018) Socioeconomic status moderates age-related differences in the brain’s functional network organization and anatomy across the adult lifespan. Proc Natl Acad Sci USA 115:E5144–E5153

    CAS  PubMed  PubMed Central  Google Scholar 

  • Chaudhary U, Birbaumer N, Ramos-Murguialday A (2016) Brain–computer interfaces for communication and rehabilitation. Nat Rev Neurol 12:513–525

    PubMed  Google Scholar 

  • Chen X, Wang Y, Nakanishi M, Gao X, Jung TP, Gao S (2015) High-speed spelling with a noninvasive brain–computer interface. Proc Natl Acad Sci USA 112:E6058–E6067

    CAS  PubMed  PubMed Central  Google Scholar 

  • Chin-Teng L, Che-Jui C, Bor-Shyh L, Shao-Hang H, Chih-Feng C, Wang IJ (2010) A real-time wireless brain–computer interface system for drowsiness detection. IEEE Trans Biomed Circuits Syst 4:214–222

    Google Scholar 

  • Cincotti F et al (2008) Non-invasive brain–computer interface system: towards its application as assistive technology. Brain Res Bull 75:796–803

    PubMed  PubMed Central  Google Scholar 

  • Cinel C, Valeriani D, Poli R (2019) Neurotechnologies for human cognitive augmentation: current state of the art and future prospects. Front Hum Neurosci 13:13

    PubMed  PubMed Central  Google Scholar 

  • Coogan CG, He B (2018) Brain–computer interface control in a virtual reality environment and applications for the internet of things. IEEE Access 6:10840–10849

    PubMed  PubMed Central  Google Scholar 

  • Cotman CW, Berchtold NC (2002) Exercise: a behavioral intervention to enhance brain health and plasticity. Trends Neurosci 25:295–301

    CAS  PubMed  Google Scholar 

  • Cryan JF, Dinan TG (2012) Mind-altering microorganisms: the impact of the gut microbiota on brain and behaviour. Nat Rev Neurosci 13:701–712

    CAS  PubMed  Google Scholar 

  • Curlik DM 2nd, Shors TJ (2013) Training your brain: do mental and physical (MAP) training enhance cognition through the process of neurogenesis in the hippocampus? Neuropharmacology 64:506–514

    CAS  PubMed  Google Scholar 

  • Dallman MF et al (2003) Chronic stress and obesity: a new view of “comfort food”. Proc Natl Acad Sci USA 100:11696–11701

    CAS  PubMed  PubMed Central  Google Scholar 

  • Daly JJ, Wolpaw JR (2008) Brain–computer interfaces in neurological rehabilitation. Lancet Neurol 7:1032–1043

    PubMed  Google Scholar 

  • d’Angelo L, Camilla S, Savulich G, Sahakian BJ (2017) Lifestyle use of drugs by healthy people for enhancing cognition, creativity, motivation and pleasure. Br J Pharmacol 174:3257–3267

    PubMed  PubMed Central  Google Scholar 

  • Deuschl G et al (2006) A randomized trial of deep-brain stimulation for Parkinson’s disease. N Engl J Med 355:896–908

    CAS  PubMed  Google Scholar 

  • Di X, Wolfer M, Kühn S, Zhang Z, Biswal BB (2019) Estimations of the weather effects on brain functions using functional MRI—a cautionary tale. bioRxiv:646695

  • Diaz Heijtz R et al (2011) Normal gut microbiota modulates brain development and behavior. Proc Natl Acad Sci USA 108:3047–3052

    PubMed  Google Scholar 

  • Dimitrakopoulos GN et al (2018) Functional connectivity analysis of mental fatigue reveals different network topological alterations between driving and vigilance tasks. IEEE Trans Neural Syst Rehabil Eng Publ IEEE Eng Med Biol Soc 26:740–749

    Google Scholar 

  • Do AH, Wang PT, King CE, Abiri A, Nenadic Z (2011) Brain–computer interface controlled functional electrical stimulation system for ankle movement. J Neuroeng Rehabil 8:49

    PubMed  PubMed Central  Google Scholar 

  • Donoghue JP (2002) Connecting cortex to machines: recent advances in brain interfaces. Nat Neurosci 5(Suppl):1085–1088

    CAS  PubMed  Google Scholar 

  • Donoghue JP (2008) Bridging the brain to the world: a perspective on neural interface systems. Neuron 60:511–521

    CAS  PubMed  Google Scholar 

  • Englot DJ, Rolston JD, Wright CW, Hassnain KH, Chang EF (2016) Rates and predictors of seizure freedom with vagus nerve stimulation for intractable epilepsy. Neurosurgery 79:345–353

    PubMed  Google Scholar 

  • Eun-Sun J et al (2018) Effect of acupuncture on patients with mild cognitive impairment assessed using functional near-infrared spectroscopy on week 12 (close-out): a pilot study protocol. Integr Med Res 7:287–295

    PubMed  PubMed Central  Google Scholar 

  • Faes L, Nollo G, Jurysta F, Marinazzo D (2014) Information dynamics of brain–heart physiological networks during sleep. New J Phys 16:105005

    Google Scholar 

  • Faes L, Marinazzo D, Jurysta F, Nollo G (2015) Linear and non-linear brain–heart and brain–brain interactions during sleep. Physiol Meas 36:683–698

    CAS  PubMed  Google Scholar 

  • Fan J, Wade JW, Key AP, Warren ZE, Sarkar N (2018) EEG-based affect and workload recognition in a virtual driving environment for ASD intervention. IEEE Trans Biomed Eng 65:43–51

    PubMed  Google Scholar 

  • Fingelkurts AA, Fingelkurts AA (2018) After human. Futura 4:60–74

    Google Scholar 

  • Foster JA, McVey Neufeld KA (2013) Gut–brain axis: how the microbiome influences anxiety and depression. Trends Neurosci 36:305–312

    CAS  PubMed  Google Scholar 

  • Franceschini S, Gori S, Ruffino M, Viola S, Molteni M, Facoetti A (2013) Action video games make dyslexic children read better. Curr Biol 23:462–466

    CAS  PubMed  Google Scholar 

  • Fregni F et al (2005) Anodal transcranial direct current stimulation of prefrontal cortex enhances working memory. Exp Brain Res 166:23–30

    PubMed  Google Scholar 

  • Friedrich EV, Suttie N, Sivanathan A, Lim T, Louchart S, Pineda JA (2014) Brain–computer interface game applications for combined neurofeedback and biofeedback treatment for children on the autism spectrum. Front Neuroeng 7:21

    PubMed  PubMed Central  Google Scholar 

  • Frisoli A, Loconsole C, Leonardis D, Banno F, Barsotti M, Chisari C, Bergamasco M (2012) A new gaze-BCI-driven control of an upper limb exoskeleton for rehabilitation in real-world tasks. IEEE Trans Syst Man Cybern Part C (Appl Rev) 42:1169–1179

    Google Scholar 

  • Gao S et al (2016) Oxytocin, the peptide that bonds the sexes also divides them. Proc Natl Acad Sci USA 113:7650–7654

    CAS  PubMed  PubMed Central  Google Scholar 

  • Garthe A, Roeder I, Kempermann G (2016) Mice in an enriched environment learn more flexibly because of adult hippocampal neurogenesis. Hippocampus 26:261–271

    PubMed  Google Scholar 

  • Gateau T, Durantin G, Lancelot F, Scannella S, Dehais F (2015) Real-time state estimation in a flight simulator using fNIRS. PLoS ONE 10:e0121279

    PubMed  PubMed Central  Google Scholar 

  • Gateau T, Ayaz H, Dehais F (2018) In silico vs. over the clouds: on-the-fly mental state estimation of aircraft pilots, using a functional near infrared spectroscopy based passive-BCI. Front Hum Neurosci 12:187

    PubMed  PubMed Central  Google Scholar 

  • Geddes L (2015) Brain stimulation in children spurs hope—and concern. Nature 525:436–437

    CAS  PubMed  Google Scholar 

  • George MS et al (2000) Vagus nerve stimulation: a new tool for brain research and therapy. Biol Psychiat 47:287–295

    CAS  PubMed  Google Scholar 

  • Gharabaghi A et al (2014) Coupling brain–machine interfaces with cortical stimulation for brain-state dependent stimulation: enhancing motor cortex excitability for neurorehabilitation. Front Hum Neurosci 8:122

    PubMed  PubMed Central  Google Scholar 

  • Gilja V, Chestek CA, Diester I, Henderson JM, Deisseroth K, Shenoy KV (2011) Challenges and opportunities for next-generation intracortically based neural prostheses. IEEE Trans Biomed Eng 58:1891–1899

    PubMed  PubMed Central  Google Scholar 

  • Gong D, He H, Liu D, Ma W, Dong L, Luo C, Yao D (2015) Enhanced functional connectivity and increased gray matter volume of insula related to action video game playing. Sci Rep 5:9763

    CAS  PubMed  PubMed Central  Google Scholar 

  • Gong J et al (2017) White matter connectivity pattern associate with characteristics of scalp EEG signals. Brain Topogr 30:797–809

    PubMed  Google Scholar 

  • Gordon RL, Fehd HM, McCandliss BD (2015) Does music training enhance literacy skills? A meta-analysis. Front Psychol 6:1777

    PubMed  PubMed Central  Google Scholar 

  • Gorelick PB et al (2017) Defining optimal brain health in adults: a presidential advisory from the American Heart Association/American Stroke Association. Stroke 48:e284–e303

    PubMed  PubMed Central  Google Scholar 

  • Grabinger RS, Dunlap JC (1995) Rich environments for active learning: a definition. ALT J 3:5–34

    Google Scholar 

  • Graf AB, Andersen RA (2014) Brain–machine interface for eye movements. Proc Natl Acad Sci USA 111:17630–17635

    CAS  PubMed  PubMed Central  Google Scholar 

  • Grau C et al (2014) Conscious brain-to-brain communication in humans using non-invasive technologies. PLoS ONE 9:e105225

    PubMed  PubMed Central  Google Scholar 

  • Grossman N et al (2017) Noninvasive deep brain stimulation via temporally interfering electric fields. Cell 169(1029–1041):e1016

    Google Scholar 

  • Guo F, Hong B, Gao X, Gao S (2008) A brain–computer interface using motion-onset visual evoked potential. J Neural Eng 5:477–485

    PubMed  Google Scholar 

  • Hallett M (2000) Transcranial magnetic stimulation and the human brain. Nature 406:147–150

    CAS  PubMed  Google Scholar 

  • Han X et al (2013) Forebrain engraftment by human glial progenitor cells enhances synaptic plasticity and learning in adult mice. Cell Stem Cell 12:342–353

    CAS  PubMed  PubMed Central  Google Scholar 

  • Hatsopoulos NG, Donoghue JP (2009) The science of neural interface systems. Annu Rev Neurosci 32:249–266

    CAS  PubMed  PubMed Central  Google Scholar 

  • He B, Baxter B, Edelman BJ, Cline CC, Wenjing WY (2015) Noninvasive brain–computer interfaces based on sensorimotor rhythms. Proc IEEE 103:907–925

    Google Scholar 

  • He H et al (2017) Music intervention leads to increased insular connectivity and improved clinical symptoms in schizophrenia. Front Neurosci 11:744

    PubMed  Google Scholar 

  • Hegde S (2014) Music-based cognitive remediation therapy for patients with traumatic brain injury. Front Neurol 5:34

    PubMed  PubMed Central  Google Scholar 

  • Herron JA, Thompson MC, Brown T, Chizeck HJ, Ojemann JG, Ko AL (2017) Cortical brain–computer interface for closed-loop deep brain stimulation. IEEE Trans Neural Syst Rehabil Eng Publ IEEE Eng Med Biol Soc 25:2180–2187

    Google Scholar 

  • Hochberg LR et al (2006) Neuronal ensemble control of prosthetic devices by a human with tetraplegia. Nature 442:164–171

    CAS  PubMed  Google Scholar 

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

    CAS  PubMed  PubMed Central  Google Scholar 

  • Hong G, Lieber CM (2019) Novel electrode technologies for neural recordings. Nat Rev Neurosci 20:330–345

    CAS  PubMed  PubMed Central  Google Scholar 

  • Hu Y, Hu Y, Li X, Pan Y, Cheng X (2017) Brain-to-brain synchronization across two persons predicts mutual prosociality. Soc Cogn Affect Neurosci 12:1835–1844

    PubMed  PubMed Central  Google Scholar 

  • Huang R et al (2016) The effects of customised brainwave music on orofacial pain induced by orthodontic tooth movement. Oral Dis 22:766–774

    CAS  PubMed  Google Scholar 

  • Huang H et al (2018) Increased resting-state global functional connectivity density of default mode network in schizophrenia subjects treated with electroconvulsive therapy. Schizophr Res 197:192–199

    PubMed  Google Scholar 

  • Iaccarino HF et al (2016) Gamma frequency entrainment attenuates amyloid load and modifies microglia. Nature 540:230–235

    CAS  PubMed  PubMed Central  Google Scholar 

  • Inman CS et al (2018) Direct electrical stimulation of the amygdala enhances declarative memory in humans. Proc Natl Acad Sci USA 115:98–103

    CAS  PubMed  Google Scholar 

  • Jayaram V, Alamgir M, Altun Y, Scholkopf B, Grosse-Wentrup M (2016) Transfer learning in brain–computer interfaces. IEEE Comput Intell Mag 11:20–31

    Google Scholar 

  • Jensen O, Bahramisharif A, Oostenveld R, Klanke S, Hadjipapas A, Okazaki YO, van Gerven MA (2011) Using brain–computer interfaces and brain-state dependent stimulation as tools in cognitive neuroscience. Front Psychol 2:100

    PubMed  PubMed Central  Google Scholar 

  • Jiang L, Stocco A, Losey DM, Abernethy JA, Prat CS, Rao RP (2019a) BrainNet: a multi-person brain-to-brain interface for direct collaboration between brains. Sci Rep 9:6115

    PubMed  PubMed Central  Google Scholar 

  • Jiang Y et al (2019b) Insular changes induced by electroconvulsive therapy response to symptom improvements in schizophrenia. Prog Neuropsychopharmacol Biol Psychiatry 89:254–262

    PubMed  Google Scholar 

  • Jiao Y, Zhang Y, Wang Y, Wang B, Jin J, Wang X (2018) A novel multilayer correlation maximization model for improving CCA-based frequency recognition in SSVEP brain–computer interface. Int J Neural Syst 28:1750039

    PubMed  Google Scholar 

  • Jiao Y, Zhang Y, Chen X, Yin E, Jin J, Wang X, Cichocki A (2019) Sparse group representation model for motor imagery EEG classification. IEEE J Biomed Health Inform 23:631–641

    PubMed  Google Scholar 

  • Jin J, Allison BZ, Sellers EW, Brunner C, Horki P, Wang X, Neuper C (2011) An adaptive P300-based control system. J Neural Eng 8:036006

    PubMed  PubMed Central  Google Scholar 

  • Jin J, Allison BZ, Kaufmann T, Kubler A, Zhang Y, Wang X, Cichocki A (2012a) The changing face of P300 BCIs: a comparison of stimulus changes in a P300 BCI involving faces, emotion, and movement. PLoS ONE 7:e49688

    CAS  PubMed  PubMed Central  Google Scholar 

  • Jin J, Allison BZ, Wang X, Neuper C (2012b) A combined brain–computer interface based on P300 potentials and motion-onset visual evoked potentials. J Neurosci Methods 205:265–276

    PubMed  Google Scholar 

  • Jin J, Zhang H, Daly I, Wang X, Cichocki A (2017) An improved P300 pattern in BCI to catch user’s attention. J Neural Eng 14:036001

    PubMed  Google Scholar 

  • Jin Z, Zhou G, Gao D, Zhang Y (2018) EEG classification using sparse Bayesian extreme learning machine for brain–computer interface. Neural Comput Appl. https://doi.org/10.1007/s00521-018-3735-3

    Article  Google Scholar 

  • Jin J, Miao Y, Daly I, Zuo C, Hu D, Cichocki A (2019) Correlation-based channel selection and regularized feature optimization for MI-based BCI. Neural Netw Off J Int Neural Netw Soc 118:262–270

    Google Scholar 

  • Jin J, Li S, Daly I, Miao Y, Liu C, Wang X, Cichocki A (2020) The study of generic model set for reducing calibration time in P300-based brain–computer interface. IEEE Trans Neural Syst Rehabil Eng Publ IEEE Eng Med Biol Soc 28:3–12

    Google Scholar 

  • Kendrick KM, Yao D (2017) Can computer-based cognitive therapy become a front-line option for prevention and treatment of mental disorders? Am J Psychiatry 174:303–304

    PubMed  Google Scholar 

  • Khushaba RN, Wise C, Kodagoda S, Louviere J, Kahn BE, Townsend C (2013) Consumer neuroscience: assessing the brain response to marketing stimuli using electroencephalogram (EEG) and eye tracking. Expert Syst Appl 40:3803–3812

    Google Scholar 

  • Kim P et al (2013) Effects of childhood poverty and chronic stress on emotion regulatory brain function in adulthood. Proc Natl Acad Sci USA 110:18442–18447

    CAS  PubMed  PubMed Central  Google Scholar 

  • Kock N (2005) Media richness or media naturalness? The evolution of our biological communication apparatus and its influence on our behavior toward e-communication tools. IEEE Trans Prof Commun 48:117–130

    Google Scholar 

  • Krusienski DJ, Sellers EW, McFarland DJ, Vaughan TM, Wolpaw JR (2008) Toward enhanced P300 speller performance. J Neurosci Methods 167:15–21

    CAS  PubMed  Google Scholar 

  • Kubler A, Mushahwar VK, Hochberg LR, Donoghue JP (2006) BCI meeting 2005—workshop on clinical issues and applications. IEEE Trans Neural Syst Rehabil Eng Publ IEEE Eng Med Biol Soc 14:131–134

    CAS  Google Scholar 

  • Kuhn S, Gleich T, Lorenz RC, Lindenberger U, Gallinat J (2014) Playing Super Mario induces structural brain plasticity: gray matter changes resulting from training with a commercial video game. Mol Psychiatry 19:265–271

    CAS  PubMed  Google Scholar 

  • Kuo MF, Paulus W, Nitsche MA (2014) Therapeutic effects of non-invasive brain stimulation with direct currents (tDCS) in neuropsychiatric diseases. NeuroImage 85(Pt 3):948–960

    PubMed  Google Scholar 

  • Lawhern VJ, Solon AJ, Waytowich NR, Gordon SM, Hung CP, Lance BJ (2018) EEGNet: a compact convolutional neural network for EEG-based brain–computer interfaces. J Neural Eng 15:056013

    PubMed  Google Scholar 

  • Lee TS et al (2013) A brain–computer interface based cognitive training system for healthy elderly: a randomized control pilot study for usability and preliminary efficacy. PLoS ONE 8:e79419

    PubMed  PubMed Central  Google Scholar 

  • Lee W, Kim S, Kim B, Lee C, Chung YA, Kim L, Yoo SS (2017) Non-invasive transmission of sensorimotor information in humans using an EEG/focused ultrasound brain-to-brain interface. PLoS ONE 12:e0178476

    PubMed  PubMed Central  Google Scholar 

  • Li G, Zhang D (2016) Brain–computer interface controlled cyborg: establishing a functional information transfer pathway from human brain to cockroach brain. PLoS ONE 11:e0150667

    PubMed  PubMed Central  Google Scholar 

  • Li Y, Long J, Yu T, Yu Z, Wang C, Zhang H, Guan C (2010) An EEG-based BCI system for 2-D cursor control by combining Mu/Beta rhythm and P300 potential. IEEE Trans Biomed Eng 57:2495–2505

    PubMed  Google Scholar 

  • Li X, Guan C, Zhang H, Ang KK, Ong SH (2014) Adaptation of motor imagery EEG classification model based on tensor decomposition. J Neural Eng 11:056020

    PubMed  Google Scholar 

  • Li G et al (2015) Identifying enhanced cortico-basal ganglia loops associated with prolonged dance training. Sci Rep 5:10271

    CAS  PubMed  PubMed Central  Google Scholar 

  • Li F et al (2016) The time-varying networks in P300: a task-evoked EEG study. IEEE Trans Neural Syst Rehabil Eng Publ IEEE Eng Med Biol Soc 24:725–733

    Google Scholar 

  • Li X, Guan C, Zhang H, Ang KK (2017) A unified Fisher’s ratio learning method for spatial filter optimization. IEEE Trans Neural Netw Learn Syst 28:2727–2737

    PubMed  Google Scholar 

  • Li G et al (2019) Increased insular connectivity and enhanced empathic ability associated with dance/music training. Neural Plast 2019:9693109

    Google Scholar 

  • Liao X, Yao D, Li C (2007a) Transductive SVM for reducing the training effort in BCI. J Neural Eng 4:246–254

    PubMed  Google Scholar 

  • Liao X, Yao D, Wu D, Li C (2007b) Combining spatial filters for the classification of single-trial EEG in a finger movement task. IEEE Trans Biomed Eng 54:821–831

    PubMed  Google Scholar 

  • Lim CG et al (2012) A brain–computer interface based attention training program for treating attention deficit hyperactivity disorder. PLoS ONE 7:e46692

    CAS  PubMed  PubMed Central  Google Scholar 

  • Liu C et al (2013) Responsive neurostimulation for the treatment of medically intractable epilepsy. Brain Res Bull 97:39–47

    PubMed  Google Scholar 

  • Liu P et al (2018) Altered topological patterns of brain functional networks in Crohn’s disease. Brain Imaging Behav 12:1466–1478

    PubMed  Google Scholar 

  • Long J, Gu Z, Li Y, Yu T, Li F, Fu M (2011) Semi-supervised joint spatio-temporal feature selection for P300-based BCI speller. Cogn Neurodyn 5:387–398

    PubMed  PubMed Central  Google Scholar 

  • Long J, Li Y, Wang H, Yu T, Pan J, Li F (2012) A hybrid brain computer interface to control the direction and speed of a simulated or real wheelchair. IEEE Trans Neural Syst Rehabil Eng Publ IEEE Eng Med Biol Soc 20:720–729

    Google Scholar 

  • Lopez-Larraz E et al (2016) Control of an ambulatory exoskeleton with a brain–machine interface for spinal cord injury gait rehabilitation. Front Neurosci 10:359

    PubMed  PubMed Central  Google Scholar 

  • Lotte F, Congedo M, Lecuyer A, Lamarche F, Arnaldi B (2007) A review of classification algorithms for EEG-based brain–computer interfaces. J Neural Eng 4:R1–R13

    CAS  PubMed  Google Scholar 

  • Lotte F, Bougrain L, Cichocki A, Clerc M, Congedo M, Rakotomamonjy A, Yger F (2018) A review of classification algorithms for EEG-based brain–computer interfaces: a 10 year update. J Neural Eng 15:031005

    CAS  PubMed  Google Scholar 

  • Lovell NH, Morley JW, Chen SC, Hallum LE, Suaning GJ (2010) Biological–machine systems integration: engineering the neural interface. Proc IEEE 98:418–431

    Google Scholar 

  • Lu J, Wu D, Yang H, Luo C, Li C, Yao D (2012) Scale-free brain-wave music from simultaneously EEG and fMRI recordings. PLoS ONE 7:e49773

    CAS  PubMed  PubMed Central  Google Scholar 

  • Lubianiker N et al (2019) Process-based framework for precise neuromodulation. Nat Hum Behav 3:436–445

    PubMed  Google Scholar 

  • Luu TP, He Y, Brown S, Nakagame S, Contreras-Vidal JL (2016) Gait adaptation to visual kinematic perturbations using a real-time closed-loop brain–computer interface to a virtual reality avatar. J Neural Eng 13:036006

    PubMed  PubMed Central  Google Scholar 

  • Ma T, Li F, Li P, Yao D, Zhang Y, Xu P (2018) An adaptive calibration framework for mVEP-based brain–computer interface. Comput Math Methods Med 2018:9476432

    PubMed  PubMed Central  Google Scholar 

  • Mahncke HW et al (2006) Memory enhancement in healthy older adults using a brain plasticity-based training program: a randomized, controlled study. Proc Natl Acad Sci USA 103:12523–12528

    CAS  PubMed  PubMed Central  Google Scholar 

  • Mak JN, Wolpaw JR (2009) Clinical applications of brain–computer interfaces: current state and future prospects. IEEE Rev Biomed Eng 2:187–199

    PubMed  PubMed Central  Google Scholar 

  • Martins NRB et al (2019) Human brain/cloud interface. Front Neurosci 13:112

    PubMed  PubMed Central  Google Scholar 

  • Maye A, Zhang D, Engel AK (2017) Utilizing retinotopic mapping for a multi-target SSVEP BCI with a single flicker frequency. IEEE Trans Neural Syst Rehabil Eng Publ IEEE Eng Med Biol Soc 25:1026–1036

    Google Scholar 

  • Mayer EA (2011) Gut feelings: the emerging biology of gut–brain communication. Nat Rev Neurosci 12:453–466

    CAS  PubMed  Google Scholar 

  • McEwen BS (2012) Brain on stress: how the social environment gets under the skin. Proc Natl Acad Sci USA 109(Suppl 2):17180–17185

    CAS  PubMed  PubMed Central  Google Scholar 

  • McEwen BS, Gianaros PJ (2010) Central role of the brain in stress and adaptation: links to socioeconomic status, health, and disease. Ann N Y Acad Sci 1186:190–222

    PubMed  PubMed Central  Google Scholar 

  • McFarland DJ, Daly J, Boulay C, Parvaz M (2017) Therapeutic applications of BCI technologies. Brain Comput Interfaces 47:37–52

    Google Scholar 

  • Meyer-Lindenberg A, Tost H (2012) Neural mechanisms of social risk for psychiatric disorders. Nat Neurosci 15:663–668

    CAS  PubMed  Google Scholar 

  • Miao Y et al (2019) An ERP-based BCI with peripheral stimuli: validation with ALS patients. Cogn Neurodyn 14:1–13

    CAS  Google Scholar 

  • Min BK, Marzelli MJ, Yoo SS (2010) Neuroimaging-based approaches in the brain–computer interface. Trends Biotechnol 28:552–560

    CAS  PubMed  Google Scholar 

  • Moldoveanu A, Ferche O-M, Moldoveanu F, Lupu RG, Cinteză D, Irimia DC, Toader C (2019) The TRAVEE system for a multimodal neuromotor rehabilitation. IEEE Access 7:8151–8171

    Google Scholar 

  • Moore DR, Shannon RV (2009) Beyond cochlear implants: awakening the deafened brain. Nat Neurosci 12:686–691

    CAS  PubMed  Google Scholar 

  • Moses DA, Leonard MK, Makin JG, Chang EF (2019) Real-time decoding of question-and-answer speech dialogue using human cortical activity. Nat Commun 10:3096

    PubMed  PubMed Central  Google Scholar 

  • Moxon KA, Foffani G (2015) Brain–machine interfaces beyond neuroprosthetics. Neuron 86:55–67

    CAS  PubMed  Google Scholar 

  • Muller-Putz GR, Pfurtscheller G (2008) Control of an electrical prosthesis with an SSVEP-based BCI. IEEE Trans Biomed Eng 55:361–364

    PubMed  Google Scholar 

  • Nakanishi M, Wang YT, Wei CS, Chiang KJ, Jung TP (2019) Facilitating calibration in high-speed BCI spellers via leveraging cross-device shared latent responses. IEEE Trans Biomed Eng. https://doi.org/10.1109/TBME.2019.2929745

    Article  PubMed  Google Scholar 

  • Nicolas-Alonso LF, Gomez-Gil J (2012) Brain computer interfaces, a review. Sensors 12:1211–1279

    PubMed  PubMed Central  Google Scholar 

  • Nitsche MA, Boggio PS, Fregni F, Pascual-Leone A (2009) Treatment of depression with transcranial direct current stimulation (tDCS): a review. Exp Neurol 219:14–19

    PubMed  Google Scholar 

  • Noble KG, Houston SM, Kan E, Sowell ER (2012) Neural correlates of socioeconomic status in the developing human brain. Dev Sci 15:516–527

    PubMed  PubMed Central  Google Scholar 

  • Nouchi R et al (2012) Brain training game improves executive functions and processing speed in the elderly: a randomized controlled trial. PLoS ONE 7:e29676

    CAS  PubMed  PubMed Central  Google Scholar 

  • O’Doherty JE, Lebedev MA, Ifft PJ, Zhuang KZ, Shokur S, Bleuler H, Nicolelis MA (2011) Active tactile exploration using a brain–machine–brain interface. Nature 479:228

    PubMed  PubMed Central  Google Scholar 

  • Ono T et al (2014) Brain–computer interface with somatosensory feedback improves functional recovery from severe hemiplegia due to chronic stroke. Front Neuroeng 7:19

    PubMed  PubMed Central  Google Scholar 

  • Pais-Vieira M, Chiuffa G, Lebedev M, Yadav A, Nicolelis MA (2015) Building an organic computing device with multiple interconnected brains. Sci Rep 5:11869

    PubMed  PubMed Central  Google Scholar 

  • Pan J, Li Y, Gu Z, Yu Z (2013) A comparison study of two P300 speller paradigms for brain–computer interface. Cogn Neurodyn 7:523–529

    PubMed  PubMed Central  Google Scholar 

  • Pascual-Leone A, Rubio B, Pallardo F, Catala MD (1996) Rapid-rate transcranial magnetic stimulation of left dorsolateral prefrontal cortex in drug-resistant depression. Lancet 348:233–237

    CAS  PubMed  Google Scholar 

  • Patron E, Mennella R, Messerotti Benvenuti S, Thayer JF (2019) The frontal cortex is a heart-brake: reduction in delta oscillations is associated with heart rate deceleration. NeuroImage 188:403–410

    PubMed  Google Scholar 

  • Pedersen BK (2019) Physical activity and muscle–brain crosstalk. Nat Rev Endocrinol 15:383–392

    PubMed  Google Scholar 

  • Pereira VH, Cerqueira JJ, Palha JA, Sousa N (2013) Stressed brain, diseased heart: a review on the pathophysiologic mechanisms of neurocardiology. Int J Cardiol 166:30–37

    PubMed  Google Scholar 

  • Perera T, George MS, Grammer G, Janicak PG, Pascual-Leone A, Wirecki TS (2016) The clinical TMS society consensus review and treatment recommendations for TMS therapy for major depressive disorder. Brain Stimul 9:336–346

    PubMed  PubMed Central  Google Scholar 

  • Pfurtscheller G, Muller GR, Pfurtscheller J, Gerner HJ, Rupp R (2003) ‘Thought’—control of functional electrical stimulation to restore hand grasp in a patient with tetraplegia. Neurosci Lett 351:33–36

    CAS  PubMed  Google Scholar 

  • Ponce CR, Xiao W, Schade PF, Hartmann TS, Kreiman G, Livingstone MS (2019) Evolving images for visual neurons using a deep generative network reveals coding principles and neuronal preferences. Cell 177(999–1009):e1010

    Google Scholar 

  • Qiu N et al (2018) Rapid improvement in visual selective attention related to action video gaming experience. Front Hum Neurosci 12:47

    PubMed  PubMed Central  Google Scholar 

  • Quitadamo LR, Cavrini F, Sbernini L, Riillo F, Bianchi L, Seri S, Saggio G (2017) Support vector machines to detect physiological patterns for EEG and EMG-based human–computer interaction: a review. J Neural Eng 14:011001

    CAS  PubMed  Google Scholar 

  • Ramachandran VS, Altschuler EL (2009) The use of visual feedback, in particular mirror visual feedback, in restoring brain function. Brain J Neurol 132:1693–1710

    CAS  Google Scholar 

  • Ramadan RA, Vasilakos AV (2017) Brain computer interface: control signals review. Neurocomputing 223:26–44

    Google Scholar 

  • Ramot M et al (2017) Direct modulation of aberrant brain network connectivity through real-time neurofeedback. eLife 6:e28974

    PubMed  PubMed Central  Google Scholar 

  • Rao RP (2013) Brain–computer interfacing: an introduction. Cambridge University Press, Cambridge

    Google Scholar 

  • Rao RP (2019) Towards neural co-processors for the brain: combining decoding and encoding in brain–computer interfaces. Curr Opin Neurobiol 55:142–151

    CAS  PubMed  PubMed Central  Google Scholar 

  • Rao RP, Stocco A, Bryan M, Sarma D, Youngquist TM, Wu J, Prat CS (2014) A direct brain-to-brain interface in humans. PLoS ONE 9:e111332

    PubMed  PubMed Central  Google Scholar 

  • Rebsamen B et al (2007) Controlling a wheelchair indoors using thought. IEEE Intell Syst 22:18–24

    Google Scholar 

  • Rebsamen B, Guan C, Zhang H, Wang C, Teo C, Ang MH Jr, Burdet E (2010) A brain controlled wheelchair to navigate in familiar environments. IEEE Trans Neural Syst Rehabil Eng Publ IEEE Eng Med Biol Soc 18:590–598

    Google Scholar 

  • Reinhart RMG, Nguyen JA (2019) Working memory revived in older adults by synchronizing rhythmic brain circuits. Nat Neurosci 22:820–827

    CAS  PubMed  PubMed Central  Google Scholar 

  • Ring H, Weingarden H (2007) Neuromodulation by functional electrical stimulation (FES) of limb paralysis after stroke. Acta Neurochir Suppl 97:375–380

    CAS  PubMed  Google Scholar 

  • Ron-Angevin R, Díaz-Estrella A (2009) Brain–computer interface: changes in performance using virtual reality techniques. Neurosci Lett 449:123–127

    CAS  PubMed  Google Scholar 

  • Rong P et al (2016) Effect of transcutaneous auricular vagus nerve stimulation on major depressive disorder: a nonrandomized controlled pilot study. J Affect Disord 195:172–179

    PubMed  PubMed Central  Google Scholar 

  • Routtenberg A, Cantallops I, Zaffuto S, Serrano P, Namgung U (2000) Enhanced learning after genetic overexpression of a brain growth protein. Proc Natl Acad Sci USA 97:7657–7662

    CAS  PubMed  PubMed Central  Google Scholar 

  • Sakaki M, Yoo HJ, Nga L, Lee TH, Thayer JF, Mather M (2016) Heart rate variability is associated with amygdala functional connectivity with MPFC across younger and older adults. NeuroImage 139:44–52

    PubMed  Google Scholar 

  • Sakhavi S, Guan C, Yan S (2018) Learning temporal information for brain–computer interface using convolutional neural networks. IEEE Trans Neural Netw Learn Syst 29:5619–5629

    PubMed  Google Scholar 

  • Salvaris M, Sepulveda F (2009) Visual modifications on the P300 speller BCI paradigm. J Neural Eng 6:046011

    CAS  PubMed  Google Scholar 

  • Samuels MA (2007) The brain–heart connection. Circulation 116:77–84

    PubMed  Google Scholar 

  • Sandberg A, Bostrom N (2006) Converging cognitive enhancements. Ann N Y Acad Sci 1093:201–227

    PubMed  Google Scholar 

  • Sapolsky RM (2015) Stress and the brain: individual variability and the inverted-U. Nat Neurosci 18:1344–1346

    CAS  PubMed  Google Scholar 

  • Schirrmeister RT et al (2017) Deep learning with convolutional neural networks for EEG decoding and visualization. Hum Brain Mapp 38:5391–5420

    PubMed  PubMed Central  Google Scholar 

  • Schwartz AB, Cui XT, Weber DJ, Moran DW (2006) Brain-controlled interfaces: movement restoration with neural prosthetics. Neuron 52:205–220

    CAS  PubMed  Google Scholar 

  • Seo D, Carmena JM, Rabaey JM, Alon E, Maharbiz MM (2013) Neural dust: an ultrasonic, low power solution for chronic brain–machine interfaces. arXiv:13072196

  • Seo D, Carmena JM, Rabaey JM, Maharbiz MM, Alon E (2015) Model validation of untethered, ultrasonic neural dust motes for cortical recording. J Neurosci Methods 244:114–122

    PubMed  Google Scholar 

  • Seo D et al (2016) Wireless recording in the peripheral nervous system with ultrasonic neural dust. Neuron 91:529–539

    CAS  PubMed  Google Scholar 

  • Shaffer F, McCraty R, Zerr CL (2014) A healthy heart is not a metronome: an integrative review of the heart’s anatomy and heart rate variability. Front Psychol 5:1040

    PubMed  PubMed Central  Google Scholar 

  • Shanechi MM (2017) Brain–machine interface control algorithms. IEEE Trans Neural Syst Rehabil Eng Publ IEEE Eng Med Biol Soc 25:1725–1734

    Google Scholar 

  • Shen R, Junhua L, Taya F, deSouza J, Thakor NV, Bezerianos A (2017) Dynamic functional segregation and integration in human brain network during complex tasks. IEEE Trans Neural Syst Rehabil Eng Publ IEEE Eng Med Biol Soc 25:547–556

    Google Scholar 

  • Shenoy KV, Carmena JM (2014) Combining decoder design and neural adaptation in brain–machine interfaces. Neuron 84:665–680

    CAS  PubMed  Google Scholar 

  • Si Y et al (2018) Different decision-making responses occupy different brain networks for information processing: a study based on EEG and TMS. Cereb Cortex 29:4119–4129

    Google Scholar 

  • Silvani A, Calandra-Buonaura G, Dampney RA, Cortelli P (2016) Brain–heart interactions: physiology and clinical implications. Philos Trans Ser A Math Phys Eng Sci 374:20150181

    Google Scholar 

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

    CAS  PubMed  Google Scholar 

  • Talwar SK, Xu S, Hawley ES, Weiss SA, Moxon KA, Chapin JK (2002) Rat navigation guided by remote control. Nature 417:37–38

    CAS  PubMed  Google Scholar 

  • Tang YP et al (1999) Genetic enhancement of learning and memory in mice. Nature 401:63–69

    CAS  PubMed  Google Scholar 

  • Taya F, Sun Y, Babiloni F, Thakor N, Bezerianos A (2015) Brain enhancement through cognitive training: a new insight from brain connectome. Front Syst Neurosci 9:44

    PubMed  PubMed Central  Google Scholar 

  • Thibault RT, Lifshitz M, Raz A (2016) The self-regulating brain and neurofeedback: experimental science and clinical promise. Cortex 74:247–261

    PubMed  Google Scholar 

  • Toppi J et al (2016) Investigating cooperative behavior in ecological settings: an EEG hyperscanning study. PLoS ONE 11:e0154236

    PubMed  PubMed Central  Google Scholar 

  • Torres-Berrio A, Nava-Mesa MO (2019) The opioid system in stress-induced memory disorders: from basic mechanisms to clinical implications in post-traumatic stress disorder and Alzheimer’s disease. Prog Neuropsychopharmacol Biol Psychiatry 88:327–338

    CAS  PubMed  Google Scholar 

  • Tost H, Champagne FA, Meyer-Lindenberg A (2015) Environmental influence in the brain, human welfare and mental health. Nat Neurosci 18:1421–1431

    CAS  PubMed  Google Scholar 

  • Van der Wall E, Van Gilst W (2013) Neurocardiology: close interaction between heart and brain. Netherlands Heart J 21:51–52

    Google Scholar 

  • Van Erp J, Lotte F, Tangermann M (2012) Brain–computer interfaces: beyond medical applications. Computer 45:26–34

    Google Scholar 

  • Vecchiato G et al (2011) Spectral EEG frontal asymmetries correlate with the experienced pleasantness of TV commercial advertisements. Med Biol Eng Comput 49:579–583

    PubMed  Google Scholar 

  • Velliste M, Perel S, Spalding MC, Whitford AS, Schwartz AB (2008) Cortical control of a prosthetic arm for self-feeding. Nature 453:1098–1101

    CAS  PubMed  Google Scholar 

  • Vidal JJ (1973) Toward direct brain–computer communication. Annu Rev Biophys Bioeng 2:157–180

    CAS  PubMed  Google Scholar 

  • Walker C, Brouwer BJ, Culham EG (2000) Use of visual feedback in retraining balance following acute stroke. Phys Ther 80:886–895

    CAS  PubMed  Google Scholar 

  • Wang Z et al (2012) Effect of acupuncture in mild cognitive impairment and Alzheimer disease: a functional MRI study. PLoS ONE 7:e42730

    CAS  PubMed  PubMed Central  Google Scholar 

  • Wang C, Wu X, Wang Z, Ma Y (2018) Implementation of a brain–computer interface on a lower-limb exoskeleton. IEEE Access 6:38524–38534

    Google Scholar 

  • Wang X, Yan J, Wang Z, Li X, Yuan Y (2020) Neuromodulation effects of ultrasound stimulation under different parameters on mouse motor cortex. IEEE Trans Biomed Eng 67:291–297

    PubMed  Google Scholar 

  • Weng CB, Qian RB, Fu XM, Lin B, Han XP, Niu CS, Wang YH (2013) Gray matter and white matter abnormalities in online game addiction. Eur J Radiol 82:1308–1312

    PubMed  Google Scholar 

  • Wolpaw JR, McFarland DJ (2004) Control of a two-dimensional movement signal by a noninvasive brain–computer interface in humans. Proc Natl Acad Sci USA 101:17849–17854

    CAS  PubMed  PubMed Central  Google Scholar 

  • Wolpaw JR, McFarland DJ, Neat GW, Forneris CA (1991) An EEG-based brain–computer interface for cursor control. Electroencephalogr Clin Neurophysiol 78:252–259

    CAS  PubMed  Google Scholar 

  • Wolpaw JR et al (2000) Brain–computer interface technology: a review of the first international meeting. IEEE Trans Neural Syst Rehabil Eng Publ IEEE Eng Med Biol Soc 8:164–173

    CAS  Google Scholar 

  • Wolpaw JR, Birbaumer N, McFarland DJ, Pfurtscheller G, Vaughan TM (2002) Brain–computer interfaces for communication and control. Clin Neurophysiol Off J Int Fed Clin Neurophysiol 113:767–791

    Google Scholar 

  • Wu D, Li CY, Yao DZ (2009) Scale-free music of the brain. PLoS ONE 4:e5915

    PubMed  PubMed Central  Google Scholar 

  • Wu D, Li C, Yao D (2013) Scale-free brain quartet: artistic filtering of multi-channel brainwave music. PLoS ONE 8:e64046

    PubMed  PubMed Central  Google Scholar 

  • Wu D, Kendrick KM, Levitin DJ, Li C, Yao D (2015) Bach is the father of harmony: revealed by a 1/f fluctuation analysis across musical genres. PLoS ONE 10:e0142431

    PubMed  PubMed Central  Google Scholar 

  • Wu Z et al (2016) Cyborg intelligence: recent progress and future directions. IEEE Intell Syst 31:44–50

    Google Scholar 

  • Xiao J, Qu J, Li Y (2019) An electrooculogram-based interaction method and its music-on-demand application in a virtual reality environment. IEEE Access 7:22059–22070

    Google Scholar 

  • Xing Y et al (2016a) Music exposure improves spatial cognition by enhancing the BDNF level of dorsal hippocampal subregions in the developing rats. Brain Res Bull 121:131–137

    CAS  PubMed  Google Scholar 

  • Xing Y et al (2016b) Exposure to Mozart music reduces cognitive impairment in pilocarpine-induced status epilepticus rats. Cogn Neurodyn 10:23–30

    PubMed  Google Scholar 

  • Xing Y et al (2016c) Mozart, Mozart rhythm and retrograde Mozart effects: evidences from behaviours and neurobiology bases. Sci Rep 6:18744

    CAS  PubMed  PubMed Central  Google Scholar 

  • Xu M, Qi H, Wan B, Yin T, Liu Z, Ming D (2013) A hybrid BCI speller paradigm combining P300 potential and the SSVEP blocking feature. J Neural Eng 10:026001

    PubMed  Google Scholar 

  • Yan T, Hui-Chan CW, Li LS (2005) Functional electrical stimulation improves motor recovery of the lower extremity and walking ability of subjects with first acute stroke: a randomized placebo-controlled trial. Stroke 36:80–85

    PubMed  Google Scholar 

  • Yang M et al (2018) The effects of music intervention on functional connectivity strength of the brain in schizophrenia. Neural Plast 2018:2821832

    PubMed  PubMed Central  Google Scholar 

  • Yao D (2017) Mesoscopic brainformatics. In: International conference on brain informatics, Springer, pp 315–324

  • Yin E, Zhou Z, Jiang J, Yu Y, Hu D (2015) A dynamically optimized SSVEP brain–computer interface (BCI) speller. IEEE Trans Biomed Eng 62:1447–1456

    PubMed  Google Scholar 

  • Yoo SS, Kim H, Filandrianos E, Taghados SJ, Park S (2013) Non-invasive brain-to-brain interface (BBI): establishing functional links between two brains. PLoS ONE 8:e60410

    CAS  PubMed  PubMed Central  Google Scholar 

  • Yu T, Li Y, Long J, Gu Z (2012) Surfing the internet with a BCI mouse. J Neural Eng 9:036012

    PubMed  Google Scholar 

  • Yuan P, Chen X, Wang Y, Gao X, Gao S (2015) Enhancing performances of SSVEP-based brain–computer interfaces via exploiting inter-subject information. J Neural Eng 12:046006

    PubMed  Google Scholar 

  • Zatorre RJ, Chen JL, Penhune VB (2007) When the brain plays music: auditory–motor interactions in music perception and production. Nat Rev Neurosci 8:547–558

    CAS  PubMed  Google Scholar 

  • Zerafa R, Camilleri T, Falzon O, Camilleri KP (2018) To train or not to train? A survey on training of feature extraction methods for SSVEP-based BCIs. J Neural Eng 15:051001

    CAS  PubMed  Google Scholar 

  • Zhang H, Guan C, Wang C (2008) Asynchronous P300-based brain–computer interfaces: a computational approach with statistical models. IEEE Trans Biomed Eng 55:1754–1763

    PubMed  Google Scholar 

  • Zhang D, Maye A, Gao X, Hong B, Engel AK, Gao S (2010) An independent brain–computer interface using covert non-spatial visual selective attention. J Neural Eng 7:16010

    PubMed  Google Scholar 

  • Zhang Y, Zhao Q, Jin J, Wang X, Cichocki A (2012) A novel BCI based on ERP components sensitive to configural processing of human faces. J Neural Eng 9:026018

    PubMed  Google Scholar 

  • Zhang Y, Xu P, Guo D, Yao D (2013a) Prediction of SSVEP-based BCI performance by the resting-state EEG network. J Neural Eng 10:066017

    PubMed  Google Scholar 

  • Zhang Y, Xu P, Huang Y, Cheng K, Yao D (2013b) SSVEP response is related to functional brain network topology entrained by the flickering stimulus. PLoS ONE 8:e72654

    CAS  PubMed  PubMed Central  Google Scholar 

  • Zhang R et al (2015) Efficient resting-state EEG network facilitates motor imagery performance. J Neural Eng 12:066024

    PubMed  Google Scholar 

  • Zhang R, Li Y, Yan Y, Zhang H, Wu S, Yu T, Gu Z (2016a) Control of a wheelchair in an indoor environment based on a brain–computer interface and automated navigation. IEEE Trans Neural Syst Rehabil Eng Publ IEEE Eng Med Biol Soc 24:128–139

    Google Scholar 

  • Zhang T et al (2016b) Structural and functional correlates of motor imagery BCI performance: insights from the patterns of fronto-parietal attention network. NeuroImage 134:475–485

    PubMed  Google Scholar 

  • Zhang Y, Wang Y, Jin J, Wang X (2017) Sparse Bayesian learning for obtaining sparsity of EEG frequency bands based feature vectors in motor imagery classification. Int J Neural Syst 27:1650032

    PubMed  Google Scholar 

  • Zhang Y, Wang Y, Zhou G, Jin J, Wang B, Wang X, Cichocki A (2018a) Multi-kernel extreme learning machine for EEG classification in brain–computer interfaces. Expert Syst Appl 96:302–310

    Google Scholar 

  • Zhang Y et al (2018b) Two-stage frequency recognition method based on correlated component analysis for SSVEP-based BCI. IEEE Trans Neural Syst Rehabil Eng Publ IEEE Eng Med Biol Soc 26:1314–1323

    Google Scholar 

  • Zhang S, Yuan S, Huang L, Zheng X, Wu Z, Xu K, Pan G (2019) Human mind control of rat cyborg’s continuous locomotion with wireless brain-to-brain interface. Sci Rep 9:1321

    PubMed  PubMed Central  Google Scholar 

  • Zoeller TR, Dowling AL, Herzig CT, Iannacone EA, Gauger KJ, Bansal R (2002) Thyroid hormone, brain development, and the environment. Environ Health Perspect 110(Suppl 3):355–361

    CAS  PubMed  PubMed Central  Google Scholar 

  • Zrenner C, Belardinelli P, Muller-Dahlhaus F, Ziemann U (2016) Closed-loop neuroscience and non-invasive brain stimulation: a tale of two loops. Front Cell Neurosci 10:92

    PubMed  PubMed Central  Google Scholar 

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Funding

This study was financially supported by National Natural Science Foundation of China (81861128001, 81771925, 61761166001, 81571759), Project of Science and Technology Department of Sichuan Province (2017HH0001) and 111 project (B12027).

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Authors and Affiliations

  1. Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, 611731, China

    Dezhong Yao, Yangsong Zhang, Tiejun Liu, Peng Xu, Diankun Gong, Jing Lu, Yang Xia, Cheng Luo, Daqing Guo, Li Dong, Yongxiu Lai, Ke Chen & Jianfu Li

  2. School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, 611731, China

    Dezhong Yao, Yangsong Zhang, Tiejun Liu, Peng Xu, Diankun Gong, Jing Lu, Yang Xia, Cheng Luo, Daqing Guo, Li Dong, Yongxiu Lai, Ke Chen & Jianfu Li

  3. School of Computer Science and Technology, Southwest University of Science and Technology, Mianyang, 621010, China

    Yangsong Zhang

  4. School of Electrical Engineering, Zhengzhou University, Zhengzhou, 450001, China

    Dezhong Yao & Jing Lu

  5. Sichuan Institute for Brain Science and Brain-Inspired Intelligence, Chengdu, 611731, China

    Dezhong Yao, Tiejun Liu, Peng Xu, Diankun Gong, Daqing Guo, Li Dong, Ke Chen & Jianfu Li

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  1. Dezhong Yao
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Contributions

DY conceived and designed this study. YZ and DY wrote the paper. YZ, TL, PX, DG, JL, YX, CL, LD, YL, KC, JL, DY contributed to the BAC examples. All of the authors have read and approved the manuscript.

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Correspondence to Dezhong Yao.

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The original online version of this article was revised: The reference “Andrew A, Fingelkurts AAF (2018) After human. Futura 4:60–74.” Is corrected to “Fingelkurts AA, Fingelkurts AA (2018) After human. Futura.4:60–74.” and respective citation in the text is corrected from “Andrew and Fingelkurts 2018” to “Fingelkurts and Fingelkurts 2018”.

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Yao, D., Zhang, Y., Liu, T. et al. Bacomics: a comprehensive cross area originating in the studies of various brain–apparatus conversations. Cogn Neurodyn 14, 425–442 (2020). https://doi.org/10.1007/s11571-020-09577-7

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