Progress in Brain Computer Interface: Challenges and Opportunities

Abstract

Brain computer interfaces (BCI) provide a direct communication link between the brain and a computer or other external devices. They offer an extended degree of freedom either by strengthening or by substituting human peripheral working capacity and have potential applications in various fields such as rehabilitation, affective computing, robotics, gaming, and neuroscience. Significant research efforts on a global scale have delivered common platforms for technology standardization and help tackle highly complex and non-linear brain dynamics and related feature extraction and classification challenges. Time-variant psycho-neurophysiological fluctuations and their impact on brain signals impose another challenge for BCI researchers to transform the technology from laboratory experiments to plug-and-play daily life. This review summarizes state-of-the-art progress in the BCI field over the last decades and highlights critical challenges.

Full text

Its licence does not allow us to show the full text. Read it at PubMed Central:

References 269

1. 1.

   Abrams D. A. Ryali S. Chen T. Chordia P. Khouzam A. Levitin D. J.. (2013 ). Inter-subject synchronization of brain responses during natural music listening. Eur. J. Neurosci. 37, 1458–1469.

   Not in our library10.1111/ejn.12173

2. 2.

   Abu-Alqumsan M. Peer A. (2016 ). Advancing the detection of steady-state visual evoked potentials in brain-computer interfaces. J. Neural Eng. 13:036005.

   Not in our library10.1088/1741-2560/13/3/036005

3. 3.

   Acqualagna L. Botrel L. Vidaurre C. Kübler A. Blankertz B. (2016 ). Large-scale assessment of a fully automatic co-adaptive motor imagery-based brain computer interface. PLoS ONE 11:e0148886.

   Not in our library10.1371/journal.pone.0148886

4. 4.

   Agarwal A. Dowsley R. McKinney N. D. Wu D. Lin C.-T. De Cock M.. (2019 ). Protecting privacy of users in brain-computer interface applications. IEEE Trans. Neural Syst. Rehabil. Eng. 27, 1546–1555.

   Not in our library10.1109/tnsre.2019.2926965

5. 5.

   Ahn M. Jun S. C. (2015 ). Performance variation in motor imagery brain-computer interface: a brief review. J. Neurosci. Methods 243, 103–110.

   Not in our library10.1016/j.jneumeth.2015.01.033

6. 6.

   Alcaide-Aguirre R. Warschausky S. Brown D. Aref A. Huggins J. (2017 ). Asynchronous brain-computer interface for cognitive assessment in people with cerebral palsy. J. Neural Eng. 14:066001.

   Not in our library10.1088/1741-2552/aa7fc4

7. 7.

   Allison B. Z. Brunner C. Kaiser V. Müller-Putz G. R. Neuper C. Pfurtscheller G. (2010 ). Toward a hybrid brain-computer interface based on imagined movement and visual attention. J. Neural Eng. 7:026007.

   Not in our library10.1088/1741-2560/7/2/026007

8. 8.

   Alomari R. Martin M. V. MacDonald S. Maraj A. Liscano R. Bellman C. (2019 ). Inside out-a study of users' perceptions of password memorability and recall. J. Inform. Security Appl. 47, 223–234.

   Not in our library10.1016/j.jisa.2019.05.009

9. 9.

   Amano K. Shibata K. Kawato M. Sasaki Y. Watanabe T. (2016 ). Learning to associate orientation with color in early visual areas by associative decoded fMRI neurofeedback. Curr. Biol. 26, 1861–1866.

   Not in our library10.1016/j.cub.2016.05.014

10. 10.

    Amaral C. P. Simões M. A. Mouga S. Andrade J. Castelo-Branco M. (2017 ). A novel brain computer interface for classification of social joint attention in autism and comparison of 3 experimental setups: a feasibility study. J. Neurosci. Methods 290, 105–115.

    Not in our library10.1016/j.jneumeth.2017.07.029

11. 11.

    Andersen L. M. Jerbi K. Dalal S. S. (2019 ). Can electro-and magnetoencephalography detect signals from the human cerebellum? PeerJ 7:e27901.

    Not in our library10.7287/peerj.preprints.27901

12. 12.
    Can EEG and MEG detect signals from the human cerebellum? · 2020

    Not checked
13. 13.

    Andujar M. Crawford C. S. Nijholt A. Jackson F. Gilbert J. E. (2015 ). Artistic brain-computer interfaces: the expression and stimulation of the user's affective state. Brain Comput. Interfaces 2, 60–69.

    Not in our library10.1080/2326263x.2015.1104613

14. 14.
    Adaptive Automation triggered by EEG-based mental workload index: a passive Brain-Computer Interface application in realistic Air Traffic Control environment · 2016

    Not checked
15. 15.

    Arpaia P. Duraccio L. Moccaldi N. Rossi S. (2020 ). Wearable brain-computer interface instrumentation for robot-based rehabilitation by augmented reality. IEEE Trans. Instrument. Meas. 69, 6362–6371.

    Not in our library10.1109/tim.2020.2970846

16. 16.

    Arvaneh M. Guan C. Ang K. K. Quek C. (2014 ). Mutual information-based optimization of sparse spatio-spectral filters in brain-computer interface. Neural Comput. Appl. 25, 625–634.

    Not in our library10.1007/s00521-013-1523-7

17. 17.

    Barachant A. Bonnet S. Congedo M. Jutten C. (2011 ). Multiclass brain-computer interface classification by riemannian geometry. IEEE Trans. Biomed. Eng. 59, 920–928.

    Not in our library10.1109/tbme.2011.2172210

18. 18.

    Bashashati A. Fatourechi M. Ward R. K. Birch G. E. (2007 ). A survey of signal processing algorithms in brain-computer interfaces based on electrical brain signals. J. Neural Eng. 4:R32.

    Not in our library10.1088/1741-2560/4/2/r03

19. 19.

    Baxter B. S. Edelman B. J. Sohrabpour A. He B. (2017 ). Anodal transcranial direct current stimulation increases bilateral directed brain connectivity during motor-imagery based brain-computer interface control. Front. Neurosci. 11:691.

    Not in our library10.3389/fnins.2017.00691

20. 20.

    Bell A. J. Sejnowski T. J. (1995 ). An information-maximization approach to blind separation and blind deconvolution. Neural Comput. 7, 1129–1159.

    Not in our library10.1162/neco.1995.7.6.1129

21. 21.

    Benabid A. L. Costecalde T. Eliseyev A. Charvet G. Verney A. Karakas S.. (2019 ). An exoskeleton controlled by an epidural wireless brain-machine interface in a tetraplegic patient: a proof-of-concept demonstration. Lancet Neurol. 18, 1112–1122.

    Not in our library10.1016/s1474-4422(19)30321-7

22. 22.

    Berger H. (1929 ). Über das elektroenkephalogramm des menschen. Arch. Psychiatr. Nervenkrankheiten 87, 527–570.

    Not in our library10.1007/bf01797193

23. 23.

    Bhattacharyya S. Clerc M. Hayashibe M. (2019 ). Augmenting motor imagery learning for brain-computer interfacing using electrical stimulation as feedback. IEEE Trans. Med. Robot. Bionics 1, 247–255.

    Not in our library10.1109/tmrb.2019.2949854

24. 24.

    Birbaumer N. Cohen L. G. (2007 ). Brain-computer interfaces: communication and restoration of movement in paralysis. J. Physiol. 579, 621–636.

    Not in our library10.1113/jphysiol.2006.125633

25. 25.

    Birbaumer N. Ghanayim N. Hinterberger T. Iversen I. Kotchoubey B. Kübler A.. (1999 ). A spelling device for the paralysed. Nature 398, 297–298.

    Not in our library10.1038/18581

26. 26.

    Blankertz B. Sanelli C. Halder S. Hammer E. Kübler A. Müller K.-R.. (2009 ). Predicting BCI performance to study BCI illiteracy. BMC Neurosci. 10(Suppl. 1):P84. 24551050

    Not in our library10.1186/1471-2202-10-s1-p84

27. 27.

    Bockbrader M. Annetta N. Friedenberg D. Schwemmer M. Skomrock N. Colachis I. V.. (2019 ). Clinically significant gains in skillful grasp coordination by an individual with tetraplegia using an implanted brain-computer interface with forearm transcutaneous muscle stimulation. Arch. Phys. Med. Rehabil. 100, 1201–1217.

    Not in our library10.1016/j.apmr.2018.07.445

28. 28.

    Bostrom N. Sandberg A. (2009 ). Cognitive enhancement: methods, ethics, regulatory challenges. Sci. Eng. Ethics 15, 311–341.

    Not in our library10.1007/s11948-009-9142-5

29. 29.

    Botrel L. Holz E. Kübler A. (2015 ). Brain painting v2: evaluation of p300-based brain-computer interface for creative expression by an end-user following the user-centered design. Brain Comput. Interfaces 2, 135–149.

    Not in our library10.1080/2326263x.2015.1100038

30. 30.

    Brickwedde M. Krüger M. C. Dinse H. R. (2019 ). Somatosensory alpha oscillations gate perceptual learning efficiency. Nat. Commun. 10, 1–9.

    Not in our library10.1038/s41467-018-08012-0

31. 31.

    Brown R. E. Milner P. M. (2003 ). The legacy of Donald O. Hebb: more than the Hebb synapse. Nat. Rev. Neurosci. 4, 1013–1019.

    Not in our library10.1038/nrn1257

32. 32.

    Brunner C. Birbaumer N. Blankertz B. Guger C. Kübler A. Mattia D.. (2015 ). BNCI horizon 2020: towards a roadmap for the BCI community. Brain Comput. Interfaces 2, 1–10.

    Not in our library10.1080/2326263x.2015.1008956

33. 33.

    Buccino A. P. Keles H. O. Omurtag A. (2016 ). Hybrid EEG-fNIRS asynchronous brain-computer interface for multiple motor tasks. PLoS ONE 11:e0146610.

    Not in our library10.1371/journal.pone.0146610

34. 34.

    Buzsáki G. Stark E. Berényi A. Khodagholy D. Kipke D. R. Yoon E.. (2015 ). Tools for probing local circuits: high-density silicon probes combined with optogenetics. Neuron 86, 92–105.

    Not in our library10.1016/j.neuron.2015.01.028

35. 35.

    Calhoun V. D. Adali T. (2016 ). Time-varying brain connectivity in fMRI data: whole-brain data-driven approaches for capturing and characterizing dynamic states. IEEE Signal Process. Mag. 33, 52–66.

    Not in our library10.1109/msp.2015.2478915

36. 36.

    Cecotti H. (2020 ). Adaptive time segment analysis for steady-state visual evoked potential based brain-computer interfaces. IEEE Trans. Neural Syst. Rehabil. Eng. 28, 552–560.

    Not in our library10.1109/tnsre.2020.2968307

37. 37.

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

    Not in our library10.1073/pnas.1508080112

38. 38.

    Chi Y. M. Wang Y.-T. Wang Y. Maier C. Jung T.-P. Cauwenberghs G. (2011 ). Dry and noncontact EEG sensors for mobile brain-computer interfaces. IEEE Trans. Neural Syst. Rehabil. Eng. 20, 228–235.

    Not in our library10.1109/tnsre.2011.2174652

39. 39.

    Chiarelli A. M. Croce P. Merla A. Zappasodi F. (2018 ). Deep learning for hybrid EEG-fNIRS brain-computer interface: application to motor imagery classification. J. Neural Eng. 15:036028.

    Not in our library10.1088/1741-2552/aaaf82

40. 40.

    Choi B. Jo S. (2013 ). A low-cost EEG system-based hybrid brain-computer interface for humanoid robot navigation and rECOGnition. PLoS ONE 8:e74583.

    Not in our library10.1371/journal.pone.0074583

Reads

A person counts once a day per paper. We keep no cookie and no address: a reader is a hash that changes every day. Any month under five reads shows no number.