A functional BCI model by the P2731 working group: psychology

References

• Lynch JJ, Paskewitz DA. On the mechanisms of the feedback control of human brain-wave activity. In: Peper E, Ancoli S, Quinn M, editors. Mind/Body Integration. Boston: Springer; 1979. p. 325–340.
   Google Scholar
• Ahn M, Jun SC. Performance variation in motor imagery brain–computer interface: a brief review. J Neurosci Methods. 2015;243:103–110.
   PubMed Web of Science ®Google Scholar
• Collura TF, Siever F. Chapter 8 - Audio-visual entrainment in relation to mental health and EEG. In: Budzynski TH, Budzynski HK, Evans JR, et al., editors. Introduction to Quantitative EEG and Neurofeedback. Second ed. Cambridge: Academic Press; 2009. p. 195–224.
   Google Scholar
• Hughes JR. Responses from the visual cortex of unanesthetized monkeys. In: Pfeiffer CC, Smythies JR, editors. International review of neurobiology. Vol. 7. New York: Academic Press; 1964. p. 99–152.
   Google Scholar
• Timofeev I, Chauvette S. Chapter 1 – Neuronal activity during the Sleep-Wake Cycle. In: Dringenberg HC, editor. Handbook of behavioral neuroscience. Vol. 30. London: Academic Press; 2019. p. 3–17.
   Google Scholar
• Nicolás B, Sala-Padró J, Cucurell D, et al. Theta rhythm supports hippocampus-dependent integrative encoding in schematic/semantic memory networks. NeuroImage. 2021;226:117558.
   PubMed Web of Science ®Google Scholar
• Wang C, Kang M, Li Z, et al. Altered relation of resting-state alpha rhythm with blood oxygen level dependent signal in healthy aging: evidence by EEG-fMRI fusion analysis. Clin Neurophysiol. 2020;131(9):2105–2114.
   PubMed Web of Science ®Google Scholar
• Kropotov JD. Chapter 3 - Beta Rhythms. In: Kropotov JD, editor. Quantitative EEG, Event-Related Potentials and Neurotherapy. London: Academic Press; 2009. p. 59–76.
   Google Scholar
• Yassa MA. Brain Rhythms: higher-Frequency Theta Oscillations Make Sense in Moving Humans. Curr Biol. 2018;28(2):R70–R72.
   PubMed Web of Science ®Google Scholar
• Chan AT, Quiroz JC, Dascalu S, et al. An overview of brain computer interfaces. In: Proceedings of the 30th International Conference on Computers and Their Applications, CATA. 2015; Honolulu, US, p. 9–11.
   Google Scholar
• Wolpaw JR, McFarland DJ, Neat GW, et al. An EEG-based brain-computer interface for cursor control. Electroencephalogr Clin Neurophysiol. 1991;78(3):252–259. 10.1016/0013-4694(91)90040-B.
   PubMedGoogle Scholar
• Khan RA, Naseer N, Saleem S, et al. Cortical tasks-based optimal filter selection: an fNIRS study. J Healthc Eng. 2020;(2020:9152369.
   Web of Science ®Google Scholar
• Jeunet C, N’Kaoua B, Subramanian S, et al. Predicting mental imagery-based BCI performance from personality, cognitive profile and neurophysiological patterns. PloS One. 2015;10(12):e0143962.
   PubMed Web of Science ®Google Scholar
• Guger C, Daban S, Sellers E, et al. How many people are able to control a P300-based brain–computer interface (BCI)? Neurosci Lett. 2009;462(1):94–98.
   PubMed Web of Science ®Google Scholar
• Guger C, Allison BZ, Großwindhager B, et al. How many people could use an SSVEP BCI? Front Neurosci. 2012;6:169.
   PubMed Web of Science ®Google Scholar
• Easttom C, Bianchi L, Valeriani D, et al. Functional Model for Unifying Brain Computer Interface Terminology. IEEE Open Journal of Engineering in Medicine and Biology. 2021;2:65–70.
   Google Scholar
• Middendorf M, McMillan G, Calhoun G, et al. Brain-computer interfaces based on the steady-state visual-evoked response. IEEE Trans Neural Syst Rehabil Eng. 2000;8(2):211–214. 2000.
   Google Scholar
• Farwell LA, Donchin E. Talking off the top of your head: toward a mental prosthesis utilizing event-related brain potentials. Electroencephalogr Clin Neurophysiol. 1988;70(6):510–523.
   PubMedGoogle Scholar
• Kim DW, Hwang HJ, Lim JH, et al. Classification of selective attention to auditory stimuli: toward vision-free brain-computer interfacing. J Neurosci Methods. 2011;197(1):180‒185.
   PubMed Web of Science ®Google Scholar
• Müller-Putz GR, Scherer R, Neuper C, et al. Steady-state somatosensory evoked potentials: suitable brain signals for brain-computer interfaces? Neural systems and rehabilitation engineering. IEEE Trans Ultrason Ferroelectr Freq Control. 2006;14(1):30‒37.
   Google Scholar
• Allison BZ, Faller J, Neuper C. BCIs that use steady-state visual evoked potentials or slow cortical potentials. In: Wolpaw JR, Wolpaw EW, editors. Brain-Computer Interfaces: principles and practice. New York: Oxford University Press; 2012. p. 241‒249.
   Google Scholar
• Neuper C, Scherer R, Wriessnegger S, et al. Motor imagery and action observation: modulation of sensorimotor brain rhythms during mental control of a brain–computer interface. Clin Neurophysiol. 2009;120(2):239–247.
   PubMed Web of Science ®Google Scholar
• Coyle SM, Ward TE, Markham CM. Brain–computer interface using a simplified functional near-infrared spectroscopy system. J Neural Eng. 2007;4(3):219–226.
   PubMed Web of Science ®Google Scholar
• Zapała D, Francuz P, Zapała E, et al. The impact of different visual feedbacks in user training on motor imagery control in BCI. Appl Psychophysiol Biofeedback. 2018;43(1):23–35.
   PubMed Web of Science ®Google Scholar
• Christophe E, Frey J, Kronland-Martinet R, et al. Evaluation of a congruent auditory feedback for Motor Imagery BCI. arXiv preprint. 2018. 1805.07064.
   Google Scholar
• Gomez-Rodriguez M, Peters J, Hill J, et al. Closing the sensorimotor loop: haptic feedback facilitates decoding of motor imagery. J Neural Eng. 2011;8(3):036005.
   PubMed Web of Science ®Google Scholar
• Wolpaw JR, McFarland DJ. Control of a two-dimensional movement signal by a noninvasive brain-computer interface in humans. Proc Nat Acad Sci. 2004;101(51):17849–17854.
   PubMed Web of Science ®Google Scholar
• McFarland DJ, Sarnacki WA, Wolpaw JR. Electroencephalographic (EEG) control of three-dimensional movement. J Neural Eng. 2010;7(3):036007.
   PubMed Web of Science ®Google Scholar
• Sellers EW, Arbel Y, Donchin E. BCIs that use P300 event- related potentials. In: Wolpaw JR, Wolpaw EW, editors. Brain-Computer Interfaces: principles and Practice. New York: Oxford University Press; 2012. p. 215‒226.
   Google Scholar
• Ramos-Murguialday A, Schürholz M, Caggiano V, et al. Proprioceptive feedback and brain computer interface (BCI) based neuroprostheses. PloS One. 2012;7(10):e47048.
   PubMed Web of Science ®Google Scholar
• Guger C, Spataro R, Allison BZ, et al. Complete locked-in and locked-in patients: command following assessment and communication with vibro-tactile P300 and motor imagery brain-computer interface tools. Front Neurosci. 2017;11:251.
   PubMed Web of Science ®Google Scholar
• Syrov NV, Novichikhina KA, Kir'yanov DA, et al. The Changes of Corticospinal Excitability During the Control of Artificial Hand Through the Brain–Computer Interface Based on the P300 Component of Visual Evoked Potential. Human Physiol. 2019;45(2):152–157.
   Google Scholar
• Quiroga R, Reddy L, Kreiman G, et al. Invariant visual representation by single neurons in the human brain. Nature. 2005;435(7045):1102–1107.
   PubMed Web of Science ®Google Scholar
• Kropotov JD. Chapter 3.1 – sensory Systems and attention modulation. In: Kropotov JD, editor. Functional Neuromarkers for Psychiatry. London: Academic Press; 2016. p. 145–146.
   Google Scholar
• Treisman AM. Selective attention in man. Br Med Bull. 1964;20(1):12–16.
   PubMed Web of Science ®Google Scholar
• Jensen O, Mazaheri A. Shaping functional architecture by oscillatory alpha activity: gating by inhibition. Frontiers in human neuroscience. 2010; 4:186.
   Google Scholar
• Jokisch D, Jensen O. Modulation of gamma and alpha activity during a working memory task engaging the dorsal or ventral stream. J Neurosci. 2007;27(12):3244–3251.
   PubMed Web of Science ®Google Scholar
• Soon C, Brass M, Heinze HJ, et al. Unconscious determinants of free decisions in the human brain. Nat Neurosci. 2008;11(5):543–545.
   PubMed Web of Science ®Google Scholar
• Jo HG, Naranjo JR, Hinterberger T, et al. Phase synchrony in slow cortical potentials is decreased in both expert and trained novice meditators. Neurosci Lett. 2019;701:142–145.
   PubMed Web of Science ®Google Scholar
• Buzsáki G, Da Silva FL. High frequency oscillations in the intact brain. Prog Neurobiol. 2012;98(3):241–249.
   PubMed Web of Science ®Google Scholar
• Kosslyn SM, Thompson WL, Ganis G. The case for mental imagery. Oxford: Oxford University Press; 2006. p. 4.
   Google Scholar
• Schreuder M, Blankertz B, Tangermann M. A new auditory multi-class brain-computer interface paradigm: spatial hearing as an informative cue. PloS One. 2010;5(4):e9813.
   PubMedGoogle Scholar
• Hwang HJ, Kim S, Choi S, et al. EEG-based brain-computer interfaces: a thorough literature survey. International Journal of Human-Computer Interaction. 2013;29(12):814–826.
   Web of Science ®Google Scholar
• Pfurtscheller G, Neuper C, Flotzinger D, et al. EEG-based discrimination between imagination of right and left hand movement. Electroencephalogr Clin Neurophysiol. 1997;103(6):642‒651.
   PubMedGoogle Scholar
• Pfurtscheller G, Brunner C, Schlögl A, et al. Mu rhythm (de) synchronization and EEG single-trial classification of different motor imagery tasks. Neuroimage. 2006;31(1):153‒159.
   PubMed Web of Science ®Google Scholar
• López-Larraz E, Antelis JM, Montesano L, et al. Continuous decoding of motor attempt and motor imagery from EEG activity in spinal cord injury patients. In: 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society. 2012, San Diego, US; pp. 1798–1801.
   Google Scholar
• Neuper C, Scherer R, Reiner M, et al. Imagery of motor actions: differential effects of kinesthetic and visual–motor mode of imagery in single-trial EEG. Cognit Brain Res. 2005;25(3):668–677.
   PubMedGoogle Scholar
• Friedrich EV, Scherer R, Neuper C. The effect of distinct mental strategies on classification performance for brain computer interfaces. Int J Psychophysiol. 2012;84(1):86–94.
   PubMed Web of Science ®Google Scholar
• Zhang S, Wang S, Zheng D, et al. A novel pattern with high-level commands for encoding motor imagery-based brain computer interface. Pattern Recognit Lett. 2019;125:28–34.
   Web of Science ®Google Scholar
• Lugo ZR, Pokorny C, Pellas F, et al. Mental imagery for brain-computer interface control and communication in non-responsive individuals. Ann Phys Rehabil Med. 2020;63(1):21–27.
   PubMed Web of Science ®Google Scholar
• Kleih SC, Nijboer F, Halder S, et al. Motivation modulates the P300 amplitude during brain-computer interface use. Clin Neurophysiol. 2010;121(7):1023–1031.
   PubMed Web of Science ®Google Scholar
• Lim Y, Ayesh A, Stacey M. Using mouse and keyboard dynamics to detect cognitive stress during mental arithmetic. In: Arai K, Kapoor S, Bhatia R, editors. Intelligent systems in science and information 2014. studies in computational intelligence. Vol. 591. Cham: Springer International Publishing; 2015. p. 335–350.
   Google Scholar
• Lim Y, Ayesh A, Stacey M. The Effects of Typing Demand on Emotional Stress, Mouse and Keystroke Behaviours. In:K. Arai, S. Kapoor, R. Bhatia, editors. Intelligent systems in science and information 2014. Studies in computational intelligence. Cham: Springer International Publishing. 2015; 591: p. 209–225
   Google Scholar
• Vidaurre C, Blankertz B. Towards a cure for BCI illiteracy. Brain Topogr. 2010;23(2):194–198.
   PubMed Web of Science ®Google Scholar
• Zapała D, Małkiewicz M, Francuz P, et al. Temperament Predictors of Motor Imagery Control in BCI. Journal of Psychophysiology. 2020;34(4):246–254.
   Web of Science ®Google Scholar
• Evans N, Gale S, Schurger A, et al. Visual feedback dominates the sense of agency for brain-machine actions. PloS One. 2015;10(6):e0130019.
   PubMed Web of Science ®Google Scholar
• Burde W, Blankertz B Is the locus of control of reinforcement a predictor of brain–computer interface performance? In:Proc 3rd int brain–computer inter- face workshop train course. 2006; Graz, Austria; p. 76–77.
   Google Scholar
• Hammer EM, Halder S, Blankertz B, et al. Psychological predictors of SMR-BCI performance. Biol Psychol. 2012;89(1):80–86.
   PubMed Web of Science ®Google Scholar
• Witte M, Kober SE, Ninaus M, et al. Control beliefs can predict the ability to up-regulate sensorimotor rhythm during neurofeedback training. Front Hum Neurosci. 2013;7:478.
   PubMed Web of Science ®Google Scholar
• Rimbert S, Gayraud N, Bougrain L, et al. Can a subjective questionnaire be used as brain-computer interface performance predictor? Front Hum Neurosci. 2019;12:529.
   PubMed Web of Science ®Google Scholar
• Benaroch C, Jeunet C, Lotte F Are users’ traits informative enough to predict/explain their mental-imagery based BCI performances? In: 8th Graz BCI Conference2019;Graz, Austria
   Google Scholar
• Sannelli C, Vidaurre C, Müller, KR, et al. A large scale screening study with a SMR-based BCI: categorization of BCI users and differences in their SMR activity. PLoS One. 2019;14(1):e0207351.
   PubMed Web of Science ®Google Scholar
• Marchetti M, Priftis K. Brain–computer interfaces in amyotrophic lateral sclerosis: a metanalysis. Clin Neurophysiol. 2015;126(6):1255–1263.
   PubMed Web of Science ®Google Scholar
• Vidaurre C, Murguialday AR, Haufe S, et al. Enhancing sensorimotor BCI performance with assistive afferent activity: an online evaluation. Neuroimage. 2019;199:375–386.
   PubMed Web of Science ®Google Scholar
• Shahriari Y, Vaughan TM, McCane LM, et al. An exploration of BCI performance variations in people with amyotrophic lateral sclerosis using longitudinal EEG data. J Neural Eng. 2019;16(5):056031.
   PubMed Web of Science ®Google Scholar
• Zapała D, Zabielska-Mendyk E, Augustynowicz P, et al. The effects of handedness on sensorimotor rhythm desynchronization and motor-imagery BCI control. Sci Rep. 2020;10(1):1–11.
   PubMed Web of Science ®Google Scholar
• Vuckovic A, Osuagwu BA. Using a motor imagery questionnaire to estimate the performance of a brain–computer interface based on object oriented motor imagery. Clin Neurophysiol. 2013;124(8):1586–1595.
   PubMed Web of Science ®Google Scholar
• Leeb R, Lee F, Keinrath C, et al. Brain–computer communication: motivation, aim, and impact of exploring a virtual apartment. IEEE Trans Neural Syst Rehabil Eng. 2007;15(4):473–482.
   PubMed Web of Science ®Google Scholar
• Mladenovic J. Standardization of protocol design for user training in EEG-based Brain-Computer Interface. J Neural Eng. 2021;18(1):011003.
   Web of Science ®Google Scholar
• Lotte F, Jeunet C, Towards improved BCI based on human learning principle. The 3rd International Winter Conference on Brain-Computer Interface, 2015; Gangwon, Korea (South); p. 1–4.
   Google Scholar
• Kübler A, Holz EM, Riccio A, et al. The user-centered design as novel perspective for evaluating the usability of BCI-controlled applications. PLoS One. 2014;9(12):e112392.
   PubMed Web of Science ®Google Scholar
• Ros T, Enriquez-Geppert S, Zotev V, et al. Consensus on the reporting and experimental design of clinical and cognitive-behavioural neurofeedback studies (CRED-nf checklist). Brain. 2020;143(6):1674–1685.
   PubMedGoogle Scholar