Brain-Computer Interface in Neuropsychological Rehabilitation
Keywords:
Brain Computer Interface, P300, Steady State Evoked Potential, Sensorimotor Rhythm, Slow Cortical Potentials, Long-Term PotentiationAbstract
Brain-computer interface (BCI) is a new frontier of neuropsychological rehabilitation. Neuroscientists have long visualized the possibility of using brain signals to control artificial devices. There are promises as well as challenges involved in it. The present article examines the concept of BCI through its function-based classification and its various operational paradigms such as P300, steady state evoked potentials, sensorimotor rhythms, and slow cortical potentials, which are used as means of BCI. In this context, Hebb’s theorem of long-term potentiation (LTP) was discussed to explain the mechanism of behaviour change. While describing the stages of signal acquisition, this article describes procedures for artifact reduction. It provides a kaleidoscopic view evidence-based practice of BCIs various clinical conditions, with hope that in coming years BCI will provide new avenues of applied research and insight for neuropsychological intervention.
References
Arvaneh, M., Robertson, I. H. & Ward, T. E. (2019). A P300-based brain-computer interface for improving attention, Frontiers of Human Neuroscience, 5: 524, doi: 10.3389/fnhum.2018.00524
Banaschewski T., Brandeis D. (2007). Annotation: what electrical brain activity tells us about brain function that other techniques cannot tell us—a child psychiatric perspective. Journal of Child Psychol. Psychiatry 48, 415–435.
Bauer H., Nirnberger G. (1981). Concept identification as a function of preceding negative or positive spontaneous shifts in slow brain potentials. Psychophysiology 18, 466–469.
Badakva, A. M., Miller, N. V., & Zobova, L. N. (2016). Artificial feedback for invasive brain-computer interfaces, Human Physiology, 42 (1), 111-118.
Bearden, T. S., Cassisi, J. E., & Pineda, M. (2003).Neurofeedback training of a patient with thalamic and cortical infarctions. Applied Psychophysiology and Biofeedback, 28: 241-252.
Berger, H., Ueber das Elektrenkephalogramm des Menschen, Archiv fur Psychitrie und Nervenkrankheiten, 87, 527-570.
Birbaumer N., Roberts L. E., Lutzenberger W., Rockstroh B., Elbert T. (1992). Area-specific self-regulation of slow cortical potentials on the sagittal midline and its effects on behavior. Electroencephalography and Clininical Neurophysiology. 84, 353–361.
Bliss, T. V. P. & Lomo, T. (1973). Long-lasting potentiation of synaptic transmission in the dentate area of the anesthetized rabbit following stimulation of the prefrontal path, The Journal of Physiology, 232 (2), 331-356.
Buttaro, T. M. (2012). Neurologic disorders. In T. M. Buttaro & K. A. Barba (eds.) Nursing Care of Hospitalized Older Patients. West Sussex: UK: John Wiley & Sons.
Calderone D. J., Lakatos P., Butler P. D., Castellanos F. X. (2014). Entrainment of neural oscillations as a modifiable substrate of attention. Trends in Cognitive Science, 18, 300–309.
Childers D., Principe, J. C., Arroyo, A., (1989) Rome Air Development Centre. Rep. F30602-88-D-0027.
Cincotti, F., Scripione, A., Timperi, A., Mattia, D., Marciasni, A. G., Millan, J., & Bablioni, F. (2003). Comparision of different feature classifiers for brain-computer interfaces. First International IEEEEMBS Conference on Neural Engineering Conference Proceedings, 645-647.
Coffey, R. J. (2001). Deep brain stimulation for chronic pain: Results of two multi-centre trials and a structured review . Pain Medicine, 2: 183-192 ,
Das, U. N. (2010). Is hypertension a disorder of brain? In Metabolic Syndrome Pathophysiology: The Role of Essential Fatty Acids: Oxford: UK. Wiley-Blackwell.
Downar, Z. & Daskalaakis, J. Z. (2013). New targets for rTMS in depression: A review of convergent evidence. Brain Stimulation, 6 (3): 231-240.
Eisen, M. D. (2003) Djourno, Eyries and the first implanted electrical neural stimulator to restore hearing. Otology and Neurotology, 24 (3): 500-506.
Farwell, L. A., & Donchin, E. (1988). Talking off the top of your head: A mental prosthesis utilizing event-related brain potentials. Electroencephalography and Clinical Neurophysiology, 70, 510-523.
Friedrich, E. V., Shivanthan, A., Lim, T., Suttie, N., Louchart, S., Pillen, S., & Pineda, J. A. ( 2015). An effective neurofeedback intervention to improve social interaction in children with autism spectrum disorder. Journal of Autism and Developmental Disorders, 45:4084-4100.
Georgopoulos, A. P., Schwartz, A. B., & Kettner, R. E. (1986). Neuronal population coding of movement direction Science (New York, N. Y.), 233 (4771): 1416-1419.
Golub, M. D., Chase, S. M., Batista, A. P., & Yu, B. M. (2016). Brain-computer interface for dissecting cognitive processes underlying sensorimotor control. Current Opinions in Neurobiology, 37:53-58.
Gomez-Rodriguez, M., Peters, J., Hill, N. J. & Scholkopf, Gharabaghi, A. & Drosse-Wentrup, M. (2010). Closing the sensorimotor loop: Haptic feedback facilitates decoding of arm movement, Conference: Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, Istanbul Turkey, 10-13 October 2010.
Graimann, B., Allison, B., & Pfurtscheller, G. (2010). Brain-computer interfaces: A gentle introduction. Brain-Computer Interfaces, The Frontiers Collection, Springer-Verlag, p. 1.
Grosse-Wentru, M., Mattia, D., & Qwiss, K. (2011). Using brain-computer interfacesto include neural plasticity and restore function. Journal of Neural Engineering, 8 (2)
Hammond, D. C., (2005). Neurofeedback treatment of depressionand anxiety, Journal of Adult Development, 12: 131-137.
Hebb, D. O. (1949). The organization of behavior: A neuropsychological theory. In M. E. Hebb (Ed.) Vol. 1. Lawrence Erlbaum Associated Inc.
Henkel, G. (2013). History of the cochlear implant. ENT Tpoday. Triological Society, The American Laryngological, Rhynological and Otological Society, Inc.
Hill, N. J. and B Scholkopf, B. (2012). An online brain–computer interface based on shifting attention to concurrent streams of auditory stimuli. Journal of Neural Engineering, (2), 26011 (13pp) doi:10.1088/1741-2560/9/2/026011
Hochberg, L. R. (2006). Neuronal ensemble control of prosthetic devices by human with tentraplegia, Nature, 442 (7099), 164-171.
Hwang, H. –J. Kim, S., Choi, S., & Im, C. –H. (2013). EEG-based brain computer interfaces: A thorough literature survey. International Journal of Human-Computer Interaction, 29 (12), 814-826.
Kaido, T., Otsuki, T., Kaneko, Y., Takahashi, A., Omori, M., & Okamoto, T. (2011). Deep brain stimulation for Tourette syndrome: A prospective pilot studyin Japan. Neuromodulation, 14: 123-129.
Kamiya, J. (1968). Conscious control of brain waves. Psychology Today, 1 (11), 56-60. Kaushik, P., & Jena, S. P. K. (2022). Effect of cognitive-behavioral intervention on electroencephalographic band powers of children with learning difficulty under eyes-closed and eyes-open conditions, Arquivos de Neuro-Psiquiatria, 80 (3), 224-232.
Konger, C., Principe, J. C., & Taner, M. (1990). Neural network classification of event-related potentials for design of a new computer interface. Proceedings of International Conference of Neural Networks, 1, 364-372.
Kramer, A. F., & Parasuraman, R. (2007). Neuroergonomics: Application of neuroscience of human factors. In Cassiopo, J. T., Tassinary, L. G. & Berntson (2007). Handbook of psychophysiology, Oxford University Press, 704-722.
Krusienski, McFarland, D. J. & Wolpaw, J. R. (2012). Value of amplitude, phase, and coherence, features for sensorimotor rhythm-based brain computerinterface. Brain Research Bulletin, 87, 130-134.
Lipsman, N., Woodside, D. B., Giacobbe, P., & Lozano, A. M. (2013). Neurosurgical treatment of anorexia nervosa: Review of the literature from leucotomy to deep brain stimulation. European Eating Disorders Review, 21:428-435.
Lubar, J. F. & Shouse, M. N. (1976). EEG and behavioural change in hyperkinetic child concurrent with training of the sensorimotor rhythm (SMR): A preliminary report. Biofeedback and Self-regulation, 1, 293-306.
Mantione, D. D. M. et al, (2010). Deep brain stimulation of the nucleus accumbens for treatment –refractory obsessive-compulsive disorder. Achieves of General Psychiatry, 67 (10): 1061-1068.
Mayberg, H. S. et al. (2005). Deep brain stimulation for treatment-resistant depression. Neuron (45) 5:651-660. Mayer K., Wyckoff S. N., Strehl U. (2013). One size fits all? Slow cortical potentials neurofeedback: a review. Journl of Attention Disorder. 17, 393–409. 10.1177/1087054712468053
McGee, J. R. (1965). F. Blair Simmons Press Release. Stanford Medical Centre News Bureau DA 1-5310.
Medical Dictionary @ (2009). Farlex and Partners.
Micouland-Franchi, J. –A., Mc. Gonigal, A., Lopez, R., Daudet, C. , Kotawas, I., & Bartolomei, F. (2015). Electroencephalographic neurofeedback: Level of evidence in mental and brain disorders and suggestions for good clinical practice. Neuropsychophysiologie Clinique, 45:423-433.
Nam, C. S., Choi, I., Wadeson, A., & Whang, M. (2018) Brain-computer interface: An emerging interaction technology, In C. S. Nam, , A. Nijholt & F. Lotte, (Eds.). Brain computer interfaces handbook: Technological and theoretical advances, CRC Press: Taylor & Francis Group.
Nam, C. S., Nijholt, A. & Lotte, F. (Eds.) (2018). Brain computer interfaces handbook: Technological and theoretical advances, CRC Press: Taylor & Francis Group.
Nam, C. S., Nijholt, A., & Lotte, F. (2018). Preface, In C. S. Nam, A. Nijholt, & F. Lotte (Eds.) Brain computer interfaces handbook: Technological and theoretical advances, CRC Press: Taylor & Francis Group.
Nicolelis, M. A. L. (2001). Action from thoughts, Nature, 18, 409, 403-407.
Pfurtscheller, G., Flotzinger, D., & Kalcher, J. (1993). Brain-computer interface-– A new communication device for handicapped persons. Journal of Microcomputer Application, 16, 293-299.
Principe, J. C. (2013). The cortical mouse: A piece of forgotten history in non-invasive brain-computer interfaces. IEE Pulse, 4 (4), 26-29. Schupp H. T., Lutzenberger W., Rau H., Birbaumer N. (1994). Positive shifts of event-related potentials: a state of cortical disfacilitation as reflected by the startle reflex probe. Electroencephalography and Clininical Neurophysiology. 90, 135–144.
Serruya, M. D., & Kahana, M. J. (2008). Techniques and devices to restore cognition. Behavioural Brain Research, 92 (2): 149-165.
Smith, J. D., Darvas, F., & Rao, M. M., Olson, J. D., Darvas, F., & Rao, R. P. N. (1018). Utilizing subdermal electrodes as noninvasive alternative for motor-based BCIs. In C. S. Nam, , A. Nijholt & F. Lotte, (Eds.). Brain computer interfaces handbook: Technological and theoretical advances, p. 129-277, CRC Press: Taylor & Francis Group.
Stefan, K. E., Kunesch, L. G., ,Cohen, R., , Beneke, and Classen, J. (2000). Induction of plasticity in human motor cortex by paired associative stimulation, Brain, 123, (3), 527-584.
Studer, P., Kratz, O., Gevenssleben, H., Rothenberger, A., Moll, G. H., Hauntzinger, M. & Heinrich, H. (2014). Slow cortical potential and theta/beta neurofeedback training in adults: Effects on attentional processes and motor system excitability. Frontiers in human neuroscience, 8 July, 555.
Tariq M, Trivailo PM, Simic M. (2018). Event-related changes detection in sensorimotor rhythm. International Robotics and Automation Journal. 4(2):119-120.
Trudeau, D. L. (2005). EEG biofeedback for addictive disorders—The state of art. Journal of Adult Development, 12: 139-146.
Vidal, J. J. (1973). Toward direct brain-computer communication. Annual Review of Biophysics and Bioengineering, 2, 157-180.
Wardell, K. et al., (2015). Deep brain stimulation of pallidum internum for Gilles de la Tourette syndrome: A patient specific model-based stimulation study of electric field. Neuromodulation Technology at the Neural Interface 18: 90-96. Wolpaw, J., Birbaumer, M., McFarnald, D. J., Pfurtscheller, G., Vaughan, T. (2002). Brain-computer interface for cursor control. Clinical Neurophysiology, 113, 767-791. Wolpaw, J., McFarnald, D. J., Neat, G. W., & Forneris, C. A. (2019). An EEG-based brain-computer interface for cursor control, Electroencephalography and Clinical Neurophysiology, 78, 252-259. Wu, W., He, H. & Tian, F. (2012). Study of recognition algorithm based on transient visual evoked potential. Transducer & Microsystem Technologies, 31(4), 47-49. Zander, T. O., Kothe, C., Welke, S., Rotting, M., (2008). Enhancing human-machine systems with secondary input from passive brain-computer interfaces. Proceedings of 4th International CBI Workshop & Training Course, Graz, Austria. Graz University of Technology Publishing House.
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