Bacomics——a new discipline integrating brain and the outside_Journal of Biomedical Engineering

Authors：

QIN Yun ^1,2 , LIU Tiejun ^1,2 ,  YAO Dezhong ^1,2

1. MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, P.R.China;
2. Sichuan Institute for Brain Science and Brain-Inspired Intelligence, Chengdu 611731, P.R.China;

Corresponding author：

Keywords：

brain-apparatus conversation; Bacomics; interactive fusion agents

DOI：

10.7507/1001-5515.202101039

Video：

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Bacomics is a unified framework for the interactions of the brain and the outside world, integrating the subject, method, and application mode of brain-apparatus conversation. This article divides the brain-apparatus conversation modes from the perspective of biological and non-biological apparatus, including the brain-biological organ interaction (BAC-1), brain-external non-living equipment and environment interaction (BAC-2), and the fusion agents of these two interactions (BAC-3), and explains the ways and potential applications in different modes.

Citation： QIN Yun, LIU Tiejun, YAO Dezhong. Bacomics——a new discipline integrating brain and the outside. Journal of Biomedical Engineering, 2021, 38(3): 507-511. doi: 10.7507/1001-5515.202101039 Copy

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32.	Ullman S. Using neuroscience to develop artificial intelligence. Science, 2019, 363(6428): 692-693.
33.	Qin Y, Zhang N, Chen Y, et al. Rhythmic network modulation to thalamocortical couplings in epilepsy. Int J Neural Syst, 2020, 30(11): 2050014.
34.	Li Fali, Chen Bei, Li He, et al. The time-varying networks in p300: a task-evoked EEG study. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2016, 24(7): 725-733.
35.	Moses D A, Leonard M K, Makin J G, et al. Real-time decoding of question-and-answer speech dialogue using human cortical activity. Nat Commun, 2019, 10(1): 3096.

1. Yao D, Zhang Y, Liu T, et al. Bacomics: a comprehensive cross area originating in the studies of various brain-apparatus conversations. Cogn Neurodyn, 2020, 14(4): 425-442.
2. Fan L, Xiang B, Xiong J, et al. Use of viruses for interrogating viscera-specific projections in central nervous system. J Neurosci Methods, 2020, 341: 108757.
3. Li H, Cao W, Zhang X, et al. BOLD-fMRI reveals the association between renal oxygenation and functional connectivity in the aging brain. Neuroimage, 2019, 186: 510-517.
4. Mather M, Thayer J. How heart rate variability affects emotion regulation brain networks. Curr Opin Behav Sci, 2018, 19: 98-104.
5. Liang S, Wu X, Hu X, et al. Recognizing depression from the microbiota-gut-brain axis. Int J Mol Sci, 2018, 19(6): 1592.
6. Gao D, Long S, Yang H, et al. SWS Brain-wave music may improve the quality of sleep: an EEG study. Front Neurosci, 2020, 14: 67.
7. Guo S, Lu J, Wang Y, et al. Sad music modulates pain perception: an EEG study. J Pain Res, 2020, 13: 2003-2012.
8. Miao Y, Yin E, Allison B Z, et al. An ERP-based BCI with peripheral stimuli: validation with ALS patients. Cogn Neurodyn, 2020, 14(1): 21-33.
9. Jin J, Miao Y, Daly I, et al. Correlation-based channel selection and regularized feature optimization for MI-based BCI. Neural Netw, 2019, 118: 262-270.
10. Jiao Y, Zhang Y, Wang Y, et al. A novel multilayer correlation maximization model for improving CCA-based frequency recognition in SSVEP brain-computer interface. Int J Neural Syst, 2018, 28(4): 1750039.
11. Zhang Y, Yin E, Li F, et al. Two-stage frequency recognition method based on correlated component analysis for SSVEP-based BCI. IEEE Trans Neural Syst Rehabil Eng, 2018, 26(7): 1314-1323.
12. Jin J, Zhang H, Daly I, et al. An improved P300 pattern in BCI to catch user's attention. J Neural Eng, 2017, 14(3): 036001.
13. Ma T, Li F, Li P, et al. An adaptive calibration framework for mVEP-based brain-computer interface. Comput Math Methods Med, 2018, 2018: 9476432.
14. Pan J, Li Y, Gu Z, et al. A comparison study of two P300 speller paradigms for brain-computer interface. Cogn Neurodyn, 2013, 7(6): 523-529.
15. Zhao Z, Yao S, Li K, et al. Real-time functional connectivity-informed neurofeedback of amygdala-frontal pathways reduces anxiety. Psychother Psychosom, 2019, 88(1): 5-15.
16. Gan X, Yao Y, Liu H, et al. Action Real-time strategy gaming experience related to increased attentional resources: an attentional blink study. Front Hum Neurosci, 2020, 14: 101.
17. Qiu N, Ma W, Fan X, et al. Rapid improvement in visual selective attention related to action video gaming experience. Front Hum Neurosci, 2018, 12: 47.
18. Vázquez F L, Otero P, García-Casal J A, et al. Efficacy of video game-based interventions for active aging. A systematic literature review and meta-analysis. PLoS One, 2018, 13(12): e0208192.
19. Gong D, He H, Liu D, et al. Enhanced functional connectivity and increased gray matter volume of insula related to action video game playing. Sci Rep, 2015, 5: 9763.
20. Bewernick B H, Hurlemann R, Matusch A, et al. Nucleus accumbens deep brain stimulation decreases ratings of depression and anxiety in treatment-resistant depression. Biol Psychiatry, 2010, 67(2): 110-116.
21. Si Y, Wu X, Li F, et al. Different decision-making responses occupy different brain networks for information processing: a study based on EEG and TMS. Cereb Cortex, 2019, 29(10): 4119-4129.
22. Polanía R, Nitsche M, Ruff C C. Studying and modifying brain function with non-invasive brain stimulation. Nat Neurosci, 2018, 21(2): 174-187.
23. Huang H, Jiang Y, Xia M, et al. Increased resting-state global functional connectivity density of default mode network in schizophrenia subjects treated with electroconvulsive therapy. Schizophr Res, 2018, 197: 192-199.
24. Jiang Y, Xia M, Li X, et al. Insular changes induced by electroconvulsive therapy response to symptom improvements in schizophrenia. Prog Neuropsychopharmacol Biol Psychiatry, 2019, 89: 254-262.
25. Meyers E C, Solorzano B R, James J, et al. Vagus nerve stimulation enhances stable plasticity and generalization of stroke recovery. Stroke, 2018, 49(3): 710-717.
26. Meng H J, Cao N, Lin Y T, et al. Motor learning enhanced by combined motor imagery and noninvasive brain stimulation is associated with reduced short-interval intracortical inhibition. Brain Behav, 2019, 9(4): e01252.
27. Ono Y, Wada K, Kurata M, et al. Enhancement of motor-imagery ability via combined action observation and motor-imagery training with proprioceptive neurofeedback. Neuropsychologia, 2018, 114: 134-142.
28. Andersen R A, Kellis S, Klaes C, et al. Toward more versatile and intuitive cortical brain-machine interfaces. Curr Biol, 2014, 24(18): R885-R897.
29. Raspopovic S, Capogrosso M, Petrini F M, et al. Restoring natural sensory feedback in real-time bidirectional hand prostheses. Sci Transl Med, 2014, 6(222): 222ra19.
30. Bouton C E, Shaikhouni A, Annetta N V, et al. Restoring cortical control of functional movement in a human with quadriplegia. Nature, 2016, 533(762): 247.
31. Roy K, Jaiswal A, Panda P. Towards spike-based machine intelligence with neuromorphic computing. Nature, 2019, 575(7784): 607-617.
32. Ullman S. Using neuroscience to develop artificial intelligence. Science, 2019, 363(6428): 692-693.
33. Qin Y, Zhang N, Chen Y, et al. Rhythmic network modulation to thalamocortical couplings in epilepsy. Int J Neural Syst, 2020, 30(11): 2050014.
34. Li Fali, Chen Bei, Li He, et al. The time-varying networks in p300: a task-evoked EEG study. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2016, 24(7): 725-733.
35. Moses D A, Leonard M K, Makin J G, et al. Real-time decoding of question-and-answer speech dialogue using human cortical activity. Nat Commun, 2019, 10(1): 3096.

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Bacomics——a new discipline integrating brain and the outside

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