Optimal Control Signal for an EEG Based Casual BCI

  • Authors

    • Satyajit Sen Purkayastha
    • V K Jain
    • H K Sardana
    2018-07-20
    https://doi.org/10.14419/ijet.v7i3.12.17867
  • Brain computer Interface, Optimal control signal, BCI, EEG, SSVEP, SSEP, SSSEP, ASSR, P300, Motor Rhythms, Sensorimotor rhythms, Slow cortical potentials, Non motor cognitive task, User friendly.

  • Abstract

    BCI (Brain computer interface) is a control and communication system which allows electrophysiological activity to control a computer or a peripheral device directly, without taking the natural route of peripheral nerves and muscles. The prime motive behind developing BCI technology was its ability to act as the only interactive link for people disabled by amyotrophic lateral sclerosis (ALS), cerebral palsy, spinal cord injury, stroke and similar neuromuscular disorders of high severity. However in the last decade, a gradual shift in BCI end-users from patients to casual (healthy) individuals has increased significantly. Because of this shift, BCI community has recognized the need for EEG based casual BCI to be more efficient and user friendly, keeping in mind the customized needs of healthy (Casual) user. So for increasing the performance of such BCIs, the selection of optimal control signal plays a very significant role. Hence, in this work, we evaluate various EEG control signals (CS) in accordance with considerations relevant to user-friendliness of casual BCIs and point up their neuro-physiological origins as well as their effectiveness in current applications. Finally, we recommend a set of parameters for selection of optimal EEG based control signal for casual BCIs and the best suitable option available among the present day control signals.

     

     

     

  • References

    1. [1] Niedermeyer, Ernst, and FH Lopes da Silva, eds. Electroencephalography: basic principles, clinical applications, and related fields. Lippincott Williams & Wilkins, 2005.

      [2] Curran, Eleanor A., and Maria J. Stokes. "Learning to control brain activity: a review of the production and control of EEG components for driving brain–computer interface (BCI) systems." Brain and cognition 51.3 (2003): 326-336.

      [3] Wolpaw, Jonathan R., et al. "Brain–computer interfaces for communication and control." Clinical neurophysiology 113.6 (2002): 767-791.

      [4] Lalor, Edmund C., et al. "Steady-state VEP-based brain-computer interface control in an immersive 3D gaming environment." EURASIP Journal on Advances in Signal Processing 2005.19 (2005): 706906.

      [5] Gouy-Pailler, Cedric, et al. "Topographical dynamics of brain connections for the design of asynchronous brain-computer interfaces." Engineering in Medicine and Biology Society, 2007.EMBS 2007.29th Annual International Conference of the IEEE.IEEE, 2007.

      [6] Muller-Putz, Gernot R., et al. "Steady-state somatosensory evoked potentials: suitable brain signals for brain-computer interfaces?." IEEE transactions on neural systems and rehabilitation engineering 14.1 (2006): 30-37.

      [7] McMillan, Grant R., et al. "Direct brain interface utilizing self-regulation of steady-state visual evoked response (SSVER)." Proc. RESNA’95 Annu. Conf. Vol. 15. 1995.

      [8] Touyama, Hideaki, and Michitaka Hirose. "Steady-state VEPs in CAVE for walking around the virtual world." International Conference on Universal Access in Human-Computer Interaction.Springer, Berlin, Heidelberg, 2007.

      [9] Solis-Escalante, Teodoro, Gerardo Gabriel Gentiletti, and Oscar Yanez-Suarez. "Detection of steady-state visual evoked potentials based on the multisignal classification algorithm." 3rd International IEEE/EMBS Conference on Neural Engineering, 2007.CNE’07. 2007.

      [10] Nicolas-Alonso, Luis Fernando, and Jaime Gomez-Gil. "Brain computer interfaces, a review." Sensors 12.2 (2012): 1211-1279.

      [11] Silberstein, Richard B., and A. Pipingas. "Steady-state visually evoked potential topography during the Wisconsin card sorting test." Electroencephalography and Clinical Neurophysiology/Evoked Potentials Section 96.1 (1995): 24-35.

      [12] Zhu, Danhua, et al. "A survey of stimulation methods used in SSVEP-based BCIs." Computational intelligence and neuroscience 2010 (2010): 1.

      [13] Regan, David. "Human brain electrophysiology: evoked potentials and evoked magnetic fields in science and medicine." (1989).

      [14] Vialatte, François-Benoît, et al. "Steady-state visually evoked potentials: focus on essential paradigms and future perspectives." Progress in neurobiology 90.4 (2010): 418-438.

      [15] Perlstein, William M., et al. "Steady-state visual evoked potentials reveal frontally-mediated working memory activity in humans." Neuroscience letters 342.3 (2003): 191-195.

      [16] Gray, M., et al. "Cortical neurophysiology of anticipatory anxiety: an investigation utilizing steady state probe topography (SSPT)." Neuroimage 20.2 (2003): 975-986.

      [17] Giabbiconi, Claire Marie, et al. "Selective spatial attention to left or right hand flutter sensation modulates the steady-state somatosensory evoked potential." Cognitive brain research20.1 (2004): 58-66.

      [18] Müller-Putz, Gernot. "New Concepts in Brain-Computer Communication: Use of Steady-State Somatosensory Evoked Potentials, User Training by Telesupport and Control of Functional Electrical Stimulation." (2004).

      [19] Nangini, C., et al. "Magnetoencephalographic study of vibrotactile evoked transient and steady-state responses in human somatosensory cortex." Neuroimage 33.1 (2006): 252-262.

      [20] Snyder, Abraham Z. "Steady-state vibration evoked potentials: description of technique and characterization of responses." Electroencephalography and Clinical Neurophysiology/Evoked potentials Section 84.3 (1992): 257-268.

      [21] Muller, G. R., ChNeuper, and GertPfurtscheller. "„Resonance-like “frequencies of sensorimotor areas evoked by repetitive tactile stimulation-resonan zeffekte insensomotorischenarealen,evoziertdurchrhythmischetaktile stimulation." BiomedizinischeTechnik/Biomedical Engineering46.7-8 (2001): 186-190.

      [22] Tobimatsu, Shozo, You Min Zhang, and Motohiro Kato. "Steady-state vibration somatosensory evoked potentials: physiological characteristics and tuning function." Clinical neurophysiology 110.11 (1999): 1953-1958.

      [23] Roß, Bernhard, et al. "A high-precision magnetoencephalographic study of human auditory steady-state responses to amplitude-modulated tones." The Journal of the Acoustical Society of America 108.2 (2000): 679-691.

      [24] Ross, B., et al. "The effect of attention on the auditory steady-state response." Neurology & clinical neurophysiology: NCN2004 (2004): 22-22.

      [25] Mäkelä, J. P., and R. Hari. "Evidence for cortical origin of the 40 Hz auditory evoked response in man." Electroencephalography and clinical neurophysiology 66.6 (1987): 539-546.

      [26] Steriade, M., et al. "Fast oscillations (20-40 Hz) in thalamocortical systems and their potentiation by mesopontine cholinergic nuclei in the cat." Proceedings of the National Academy of Sciences 88.10 (1991): 4396-4400.

      [27] Engelien, Almut, et al. "A combined functional in vivo measure for primary and secondary auditory cortices." Hearing research 148.1-2 (2000): 153-160.

      [28] Galambos, Robert. "Tactile and auditory stimuli repeated at high rates (30–50 per sec) produce similar event related potentials." Annals of the New York Academy of Sciences338.1 (1980): 722-726.

      [29] Pastor, Maria A., et al. "Activation of human cerebral and cerebellar cortex by auditory stimulation at 40 Hz." Journal of Neuroscience 22.23 (2002): 10501-10506.

      [30] Picton, Terence W., et al. "Potentials evoked by the sinusoidal modulation of the amplitude or frequency of a tone." The Journal of the Acoustical Society of America 82.1 (1987): 165-178.

      [31] Stapells, David R., Scott Makeig, and Robert Galambos. "Auditory steady-state responses: threshold prediction using phase coherence." Clinical Neurophysiology 67.3 (1987): 260-270.

      [32] Roß, Bernhard, Terence W. Picton, and Christo Pantev. "Temporal integration in the human auditory cortex as represented by the development of the steady-state magnetic field." Hearing research 165.1-2 (2002): 68-84.

      [33] Farwell, Lawrence Ashley, and Emanuel Donchin. "Talking off the top of your head: toward a mental prosthesis utilizing event-related brain potentials." Electroencephalography and clinical Neurophysiology 70.6 (1988): 510-523.

      [34] Polich, John, Patricia Crane Ellerson, and Jill Cohen. "P300, stimulus intensity, modality, and probability." International Journal of Psychophysiology 23.1-2 (1996): 55-62.

      [35] Ravden, Daran, and John Polich. "On P300 measurement stability: habituation, intra-trial block variation, and ultradian rhythms." Biological psychology 51.1 (1999): 59-76.

      [36] Pfurtscheller, Gert, and Christa Neuper. "Motor imagery and direct brain-computer communication." Proceedings of the IEEE 89.7 (2001): 1123-1134.

      [37] Blankertz, Benjamin, et al. "Neurophysiological predictor of SMR-based BCI performance." Neuroimage 51.4 (2010): 1303-1309.

      [38] Jeannerod, Marc. "Mental imagery in the motor context." Neuropsychologia 33.11 (1995): 1419-1432.

      [39] Pfurtscheller, Gert, et al. "EEG-based discrimination between imagination of right and left hand movement." Electroencephalography and clinical Neurophysiology 103.6 (1997): 642-651.

      [40] Birbaumer, Niels, et al. "Slow potentials of the cerebral cortex and behavior." Physiological reviews 70.1 (1990): 1-41.

      [41] Birbaumer, Niels, et al. "The thought translation device (TTD) for completely paralyzed patients." IEEE Transactions on rehabilitation Engineering 8.2 (2000): 190-193.

      [42] Kleber, Boris, and NielsBirbaumer. "Direct brain communication: neuroelectric and metabolic approaches at Tübingen." Cognitive Processing 6.1 (2005): 65-74.

      [43] Keirn, Zachary A., and Jorge I. Aunon. "A new mode of communication between man and his surroundings." IEEE transactions on biomedical engineering 37.12 (1990): 1209-1214.

      [44] Müller-Putz, Gernot R., et al. "Comparison of DFT and lock-in amplifier features and search for optimal electrode positions in SSVEP-based BCI." Journal of neuroscience methods 168.1 (2008): 174-181.

      [45] Middendorf, Matthew, et al. "Brain-computer interfaces based on the steady-state visual-evoked response." IEEE transactions on rehabilitation engineering 8.2 (2000): 211-214.

      [46] Müller-Putz, Gernot R., et al. "Steady-state visual evoked potential (SSVEP)-based communication: impact of harmonic frequency components." Journal of neural engineering 2.4 (2005): 123.

      [47] Kelly, Simon P., et al. "Visual spatial attention control in an independent brain-computer interface." IEEE Transactions on Biomedical Engineering 52.9 (2005): 1588-1596.

      [48] Kelly, Simon P., et al. "Visual spatial attention tracking using high-density SSVEP data for independent brain-computer communication." IEEE Transactions on Neural Systems and Rehabilitation Engineering 13.2 (2005): 172-178.

      [49] Lalor, E., et al. "Brain computer interface based on the steady-state VEP for immersive gaming control." BiomedizinscheTecknik 49.1 (2004): 63-64.

      [50] Sutter, Erich E. "The brain response interface: communication through visually-induced electrical brain responses." Journal of Microcomputer Applications 15.1 (1992): 31-45.

      [51] Cheng, Ming, et al. "Design and implementation of a brain-computer interface with high transfer rates." IEEE transactions on biomedical engineering 49.10 (2002): 1181-1186.

      [52] Nielsen, Kim Dremstrup, Alvaro Fuentes Cabrera, and Omar Feix do Nascimento. "EEG based BCI-towards a better control. Brain-computer interface research at Aalborg University." IEEE Transactions on Neural Systems and Rehabilitation Engineering 14.2 (2006): 202-204.

      [53] Hill, N. Jeremy, et al. "An auditory paradigm for brain-computer interfaces." Advances in neural information processing systems. 2005.

      [54] Donchin, Emanuel, Kevin M. Spencer, and RanjithWijesinghe. "The mental prosthesis: assessing the speed of a P300-based brain-computer interface." IEEE transactions on rehabilitation engineering 8.2 (2000): 174-179.

      [55] Glover, Andrew A., et al. "P300-like potentials in the normal monkey using classical conditioning and an auditory ‘oddball’paradigm." Electroencephalography and Clinical Neurophysiology/Evoked Potentials Section 65.3 (1986): 231-235.

      [56] Röder, Brigitte, et al. "Event-related potentials during auditory and somatosensory discrimination in sighted and blind human subjects." Cognitive Brain Research 4.2 (1996): 77-93.

      [57] Miltner, Wolfgang, Wolfgang Larbig, and Christoph Braun. "Biofeedback of somatosensory event-related potentials: can individual pain sensations be modified by biofeedback-induced self-control of event-related potentials?." Pain 35.2 (1988): 205-213.

      [58] Sommer, Werner, and Stefan Schweinberger. "Operant conditioning of P300." Biological psychology 33.1 (1992): 37-49.

      [59] Sugiarto, Indar. "Robust visual stimulator for P300-BCI." Instrumentation, Communications, Information Technology, and Biomedical Engineering (ICICI-BME), 2009 International Conference on.IEEE, 2009.

      [60] Hwang, Han-Jeong, Kiwoon Kwon, and Chang-Hwang Im. "Neurofeedback-based motor imagery training for brain–computer interface (BCI)." Journal of neuroscience methods179.1 (2009): 150-156.

      [61] Wolpaw, Jonathan R., Dennis J. McFarland, and Theresa M. Vaughan. "Brain-computer interface research at the Wadsworth Center." IEEE Transactions on Rehabilitation Engineering 8.2 (2000): 222-226.

      [62] Blankertz, Benjamin, et al. "The Berlin Brain-Computer Interface: Accurate performance from first-session in BCI-naive subjects." IEEE Trans Biomed Eng 55.10 (2008): 2452-2462.

      [63] Pfurtscheller, Gert, et al. "Graz-BCI: state of the art and clinical applications." IEEE Transactions on neural systems and rehabilitation engineering 11.2 (2003): 1-4.

      [64] McFarland, D. J., et al. "EEG-based brain–computer interface communication effect of target number and trial length on information transfer rate." SocNeurosciAbstr 2000b. Vol. 26. 2000.

      [65] Hinterberger, Thilo, et al. "Brain-computer communication and slow cortical potentials." IEEE Transactions on Biomedical Engineering 51.6 (2004): 1011-1018.

      [66] Kaiser, Jochen, et al. "Self-initiation of EEG-based communication in paralyzed patients." Clinical Neurophysiology 112.3 (2001): 551-554.

      [67] Neumann, Nicola, and NielsBirbaumer. "Predictors of successful self control during brain-computer communication." Journal of Neurology, Neurosurgery & Psychiatry 74.8 (2003): 1117-1121.

      [68] Millan, Jose del R., et al. "Local neural classifier for EEG-based recognition of mental tasks." ijcnn. IEEE, 2000.

      [69] Besserve, Michel, Line Garnero, and Jacques Martinerie. "De l'estimation à la classification des activitéscorticales.Uneapproche par sélection de variables pour les Interfaces Cerveau Machine." 21° Colloque GRETSI, Troyes, FRA, 11-14 septembre 2007. GRETSI, Grouped’Etudes du Traitement du Signal et des Images, 2007.

      [70] Chiappa, Silvia, and SamyBengio. HMM and IOHMM modeling of EEG rhythms for asynchronous BCI systems. No. EPFL-REPORT-82978.IDIAP, 2003.

      [71] Anderson, Charles W., Erik A. Stolz, and SanyogitaShamsunder. "Multivariate autoregressive models for classification of spontaneous electroencephalographic signals during mental tasks." IEEE Transactions on Biomedical Engineering 45.3 (1998): 277-286.

      [72] Millan, Jdel R., and JosepMouriño. "Asynchronous BCI and local neural classifiers: an overview of the adaptive brain interface project." IEEE Transactions on Neural Systems and Rehabilitation Engineering 11.2 (2003): 159-161.

      [73] del R Millan, Jose, et al. "A local neural classifier for the recognition of EEG patterns associated to mental tasks." IEEE transactions on neural networks 13.3 (2002): 678-686.

      [74] Penny, William D., and Stephen J. Roberts. "EEG-based communication via dynamic neural network models." Proc. Int. Joint Conf. on Neural Networks. 1999.

      [75] Penny, William D., et al. "EEG-based communication: a pattern recognition approach." IEEE transactions on Rehabilitation Engineering 8.2 (2000): 214-215.

      [76] Curran, Eleanor, et al. "Cognitive tasks for driving a brain-computer interfacing system: a pilot study." IEEE Transactions on Neural Systems and Rehabilitation Engineering 12.1 (2004): 48-54.

      [77] Birbaumer, Niels, et al. "EEG and slow cortical potentials in anticipation of mental tasks with different hemispheric involvement." Biological Psychology 13 (1981): 251-260.

  • Downloads

  • How to Cite

    Sen Purkayastha, S., K Jain, V., & K Sardana, H. (2018). Optimal Control Signal for an EEG Based Casual BCI. International Journal of Engineering & Technology, 7(3.12), 1257-1264. https://doi.org/10.14419/ijet.v7i3.12.17867

    Received date: 2018-08-19

    Accepted date: 2018-08-19

    Published date: 2018-07-20