Around 61 million people in the United States suffer from different forms of disability; of these, 13.7% suffer from a motor disability. Research in brain–computer interfaces (BCIs) has focused on curbing communication disabilities due to motor impairment. Many challenges and future developments lie ahead in the BCI world. The research on paradigms that guide the way to make communication easy for a person with motor impairment is the pillar of the BCI system. The purpose of this paper is to synthesize the developments in non-invasive brain–computer interface and evaluate them. The authors discuss components of the BCI system and how it is formed. Neurofeedback based on different modalities is also analyzed. The results from initial studies have been successful, but paradigms and neurofeedback technologies have immense development potential, which can change the face of BCI systems for rehabilitation of motor disability.
Get full access to this article
View all access options for this article.
References
1.
RezaAbiriSoheilBorhaniEricSellersYangJiangZhaoXiaopeng. (2018). A comprehensive review of EEG-based brain- computer interface paradigms. Journal of Neural Engineering.
2.
Angulo-ShermanI. N.GutiérrezD. (2014). Effect of different feedback modalities in the performance of brain-computer interfaces. 2014 International Conference on Electronics, Communications and Computers
3.
BaykaraC.A.RufC.FioravantiI.KäthnerN.SimonS.C.KleihA.KüblerS. Halder “Effects of training and motivation on auditory P300 brain– computer interface performance”- Clinical Neurophysiology, Elsevier, 2016
4.
BrumbergJ. S.PittK. M.BurnisonJ. D. (2018). A Noninvasive Brain-Computer Interface for Real-Time Speech Synthesis: The Importance of Multimodal Feedback. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 26, 874–881.
5.
ChaudharyUXiaBSilvoniSCohenL GBirbaumerN2017Brain– computer interface–based communication in the completely locked-in state.
6.
Christoph GugerBrendan Z.Allison, Günter Edlinger” Brain– Computer Interface Research” A State-of-the-Art Summary, 2011
7.
FriedrichE. V.McFarlandD. J.NeuperC.VaughanT. M.BrunnerP.WolpawJ. R. (2009). A scanning protocol for a sensorimotor rhythm- based brain-computer interface. Biological psychology.
8.
HochbergLRSerruyaMDFriehsGMMukandJASalehMCaplanAHBrannerAChenDPennRDDonoghueJP.Neuronal ensemble control of prosthetic devices by a human with tetraplegia. Nature. 2006
9.
IshiharaMoxonEhrmanYarboroughPanontinNathan-Roberts“Feedback Modalities in Brain–Computer Interfaces: A Systematic Review”
10.
IturrateR. ChavarriagaMontesanoL.MinguezJ.MillánJ. d. R. “Teaching brain-machine interfaces as an alternative paradigm to neuroprosthetics control,” Scientific reports, vol. 5, 2015.
11.
JayaramV.AlamgirM.AltunY.ScholkopfB.Grosse-WentrupM. (2016). Transfer learning in brain-computer interfaces. IEEE Computational Intelligence Magazine, 11(1), 20–31.
12.
KalcherJ.FlotzingerD.NeuperC.GöllyS.PfurtschellerG.: Graz brain-computer interface II: towards communication between humans and computers based on online classification of three different EEG patterns. Med. & Biol. Eng. & Comput. 34, 382–388 (1996)
13.
Kim Dremstrup Nielsen, Alvaro Fuentes Cabrera, and Omar Feix do Nascimento (2006) “EEG Based BCI—Towards a Better Control.Brain- Computer Interface Research at Aalborg University.”
14.
KleihS.C.KüblerA.2013. Empathy, motivation, and P300 BCI performance. Frontiers in human neuroscience, 7, p.642.
15.
KorikA.SosnikR.SiddiqueN.CoyleD. (2019). Decoding Imagined 3D Arm Movement Trajectories from EEG to Control Two Virtual Arms-A Pilot Study. Frontiers in neurorobotics, 13, 94.
16.
LiKunSankarRaviArbelYaelDonchinEmanuel“P300 Based Single Trial Independent Component Analysis on EEG Signal”- Department of Electrical Engineering, Psychology, University of South Florida, Tampa, FL, USA, 2010
17.
LebedevM ANicolelisM A2017Brain–machine interfaces: from basic science to neuroprostheses and neurorehabilitation
18.
LinY. P.WangY.WeiC. S.JungT. P. (2014). Assessing the quality of steady-state visual-evoked potentials for moving humans using a mobile electroencephalogram headset. Frontiers in human neuroscience, 8
19.
Mak JosephNWolpawJonathan R. “Clinical Applications of Brain- Computer Interfaces: Current State and Future Prospects.” IEEE reviews in biomedical engineeringvol. 2 (2009)
20.
MantiniD.PerrucciM.G.Del GrattaC.RomaniG.L.CorbettaM.2007. Electrophysiological signatures of resting state networks in the human brain. Proceedings of the National Academy of Sciences, 104(32), pp.13170–13175
21.
McCreadieKarlDamienCoylePrasadGirijesh. (2012). Sensorimotor learning with stereo auditory feedback for a brain–computer interface. Medical & biological engineering & computing. 51. 10.1007/s11517- 012-0992-7.
22.
MorashV.BaiO.FurlaniS.LinP.HallettM. (2008). Classifying EEG signals preceding right hand, left hand, tongue, and right foot movements and motor imageries. Clinical neurophysiology: official journal of the International Federation of Clinical Neurophysiology, 119(11), 2570–2578.
23.
NijboerF.BirbaumerN.KublerA.2010. The influence of psychological state and motivation on brain–computer interface performance in patients with amyotrophic lateral sclerosis–a longitudinal study. Frontiers in neuroscience, 4, p.55.)
24.
ParkW.KwonG.H.KimY.H.LeeJ.H.KimL.2016. EEG response varies with lesion location in patients with chronic stroke. Journal of neuroengineering and rehabilitation, 13(1), p.21.
25.
PfurtschellerGertBrendanAllisonGüntherBauernfeindClemensBrunnerTeodoroSolis EscalanteReinholdSchererThorstenZanderGernotMueller-PutzChristaNeuperNielsBirbaumer“The hybrid BCI” Frontiers in Neuroscience-vol-4, 2010
26.
PfurtschellerG. R.Müller-Putz SchlöglA.GraimannB.SchererR.LeebR.BrunnerC.KeinrathC.LeeF.TownsendG.VidaurreC.NeuperC. (2006) “15 Years of BCI Research at Graz University of Technology: Current Projects”
27.
PfurtschellerGNeuperC1997Motor imagery activates primary sensorimotor area in humans Neurosci. Lett. 239 65–8
28.
FerrezP.W.J. d. R. Millán, “Error-related EEG potentials generated during simulated brain–computer interaction,”IEEE transactions on biomedical engineering, vol. 55, no. 3, pp. 923–929, 2008.
29.
ChavarriagaR.SobolewskiA.MillánJ. d. R. “Errare machinale est: the use of error-related potentials in brain-machine interfaces,” Using Neurophysiological Signals that Reflect Cognitive or Affective State
30.
ChavarriagaR.J. d. R. Millán, “Learning from EEG error-related potentials in noninvasive brain-computer interfaces,”IEEE transactions on neural systems and rehabilitation engineering, vol. 18, no. 4, pp. 381–388, 2010.
31.
SahaS.AhmedK.I.U.MostafaR.HadjileontiadisL.KhandokerA.2018. Evidence of Variabilities in EEG Dynamics During Motor Imagery-Based Multiclass Brain–Computer Interface. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 26(2), pp.371–382.
32.
SahaSimantoKhondakerA.Mamun, Khawza Ahmed, Raqibul Mostaf, Ganesh R.Naik, Ahsan Khandoker,Sam Darvishi, and Mathias Baumert1” Progress in Brain Computer Interfaces: Challenges and Trends”, 2017Vialatte
33.
FrancoisMauriceMoniqueDauwelsJustinAndrzejCichocki. (2009). Steady-state visually evoked potentials: Focus on essential paradigms and future perspectives. Progress in neurobiology. 90. 418-38. 10.1016/j.pneurobio.2009.11.005.
34.
WolpawJRLoebGEAllisonBZDonchinEdo NascimentoOFHeetderksWJNijboerFShainWGTurnerJN.BCI Meeting 2005-workshop on signals and recording methods. IEEE Trans Neural Syst Rehabil Eng. 2006
35.
ArtusiX.NiaziI. K.LucasM.-F.FarinaD. “Performance of a simulated adaptive BCI based on experimental classification of movement- related and error potentials,” IEEE Journal on Emerging and Selected Topics in Circuits and Systems, vol. 1, no. 4, pp. 480–488, 2011.
