Lecture 15: Brain-Computer Interfaces | COGSCI 1 | UC Berkeley
Introduction to Cognitive Science (COGSCI 1B)
Lecture 15: Brain-Computer Interfaces
Introduction (0:00)
Types of signals used in BCIs (1:20)
An example of using EEG during an auditory entrainment task (2:18)
The axial-spatial hierarchy (4:10)
Auditory brainstem responses, middle latency responses, and late event-related potentials (6:12)
Amplitude, frequency, latency, period, and polarity (10:25)
Overview of the frequency signal bands in the EEG signal (11:55)
The delta band (16:00)
The theta band (16:36)
The alpha band (17:14)
The beta band (17:35)
The gamma band (18:23)
Event-related synchronization (ERS) and event-related desynchronization (ERD) (20:47)
An example of an event-related potential (ERP): The P300 (23:28)
Slow cortical potential (SCP) (29:08)
Tradeoffs regarding invasive and non-invasive BCI techniques (30:26)
Invasive techniques for BCIs (33:33)
Non-invasive techniques for BCIs (38:12)
Results from research using BCIs (40:53)
Processing steps involved in converting raw signals into device actions (42:34)
The importance of training for proper functioning of BCIs (44:07)
Results showing brain plasticity as a result of BCI training (57:20)
Factors that impact training with BCIs (1:03:28)
Conclusion (1:09:03)
References
Daly, J. J. & Wolpaw, J. R. (2008). Brain-computer interfaces in neurological rehabilitation. The Lancet: Neurology, 7, 1032-1043. https://doi.org/10.1016/S1474-4422(08)70223-0
Levine, S. P., Huggins, J. E., BeMent, S. L., Kushwaha, R. K., Schuh, L. A., Rohde, M. M., Passaro, E. A., Ross, D. A., Elisevich, K. V. & Smith, B. J. (2000). A direct brain interface based on event-related potentials. IEEE Transactions on Rehabilitation Engineering, 8, 180-185. https://doi.org/10.1109/86.847809
Miranda, R. A., Casebeer, W. D., Hein, A. M., Judy, J. W., Krotkov, E. P., Laabs, T. L., Manzo, J. E., Pankratz, K. G., Pratt, G. A., Sanchez, J. C., Weber, D. J., Wheeler, T. L. & Ling, G. S. F. (2015). DARPA-funded efforts in the development of novel brain-computer interface technologies. Journal of Neuroscience Methods, 244, 52-67. https://doi.org/10.1016/j.jneumeth.2014.07.019
Nierhaus, T., Vidaurre, C., Sannelli, C., Mueller, K. R. & Villringer, A. (2021). Immediate brain plasticity after one hour of brain-computer interface (BCI). The Journal of Physiology, 599, 2435-2451. https://doi.org/10.1113/JP278118
Vansteensel, M. J., Pels, E. G. M., Bleichner, M. G., Branco, M. P., Denison, T., Freudenburg, Z. V., Gosselaar, P., Leinders, S., Ottens, T. H., Van Den Boom, M. A., Van Rijen, P. C., Aarnoutse, E. J. & Ramsey, N. F. (2016). Fully implanted brain-computer interface in a locked-in patient with ALS. The New England Journal of Medicine, 375, 2060-2066. https://doi.org/10.1056/NEJMoa1608085
Much thanks to Alecia Barnes and Michelle Wolf at DSP Berkeley for providing professionally captioned media for the lectures in this course.
Introduction to Cognitive Science (COGSCI 1B)
Lecture 15: Brain-Computer Interfaces
Introduction (0:00)
Types of signals used in BCIs (1:20)
An example of using EEG during an auditory entrainment task (2:18)
The axial-spatial hierarchy (4:10)
Auditory brainstem responses, middle latency responses, and late event-related potentials (6:12)
Amplitude, frequency, latency, period, and polarity (10:25)
Overview of the frequency signal bands in the EEG signal (11:55)
The delta band (16:00)
The theta band (16:36)
The alpha band (17:14)
The beta band (17:35)
The gamma band (18:23)
Event-related synchronization (ERS) and event-related desynchronization (ERD) (20:47)
An example of an event-related potential (ERP): The P300 (23:28)
Slow cortical potential (SCP) (29:08)
Tradeoffs regarding invasive and non-invasive BCI techniques (30:26)
Invasive techniques for BCIs (33:33)
Non-invasive techniques for BCIs (38:12)
Results from research using BCIs (40:53)
Processing steps involved in converting raw signals into device actions (42:34)
The importance of training for proper functioning of BCIs (44:07)
Results showing brain plasticity as a result of BCI training (57:20)
Factors that impact training with BCIs (1:03:28)
Conclusion (1:09:03)
References
Daly, J. J. & Wolpaw, J. R. (2008). Brain-computer interfaces in neurological rehabilitation. The Lancet: Neurology, 7, 1032-1043. https://doi.org/10.1016/S1474-4422(08)70223-0
Levine, S. P., Huggins, J. E., BeMent, S. L., Kushwaha, R. K., Schuh, L. A., Rohde, M. M., Passaro, E. A., Ross, D. A., Elisevich, K. V. & Smith, B. J. (2000). A direct brain interface based on event-related potentials. IEEE Transactions on Rehabilitation Engineering, 8, 180-185. https://doi.org/10.1109/86.847809
Miranda, R. A., Casebeer, W. D., Hein, A. M., Judy, J. W., Krotkov, E. P., Laabs, T. L., Manzo, J. E., Pankratz, K. G., Pratt, G. A., Sanchez, J. C., Weber, D. J., Wheeler, T. L. & Ling, G. S. F. (2015). DARPA-funded efforts in the development of novel brain-computer interface technologies. Journal of Neuroscience Methods, 244, 52-67. https://doi.org/10.1016/j.jneumeth.2014.07.019
Nierhaus, T., Vidaurre, C., Sannelli, C., Mueller, K. R. & Villringer, A. (2021). Immediate brain plasticity after one hour of brain-computer interface (BCI). The Journal of Physiology, 599, 2435-2451. https://doi.org/10.1113/JP278118
Vansteensel, M. J., Pels, E. G. M., Bleichner, M. G., Branco, M. P., Denison, T., Freudenburg, Z. V., Gosselaar, P., Leinders, S., Ottens, T. H., Van Den Boom, M. A., Van Rijen, P. C., Aarnoutse, E. J. & Ramsey, N. F. (2016). Fully implanted brain-computer interface in a locked-in patient with ALS. The New England Journal of Medicine, 375, 2060-2066. https://doi.org/10.1056/NEJMoa1608085
Much thanks to Alecia Barnes and Michelle Wolf at DSP Berkeley for providing professionally captioned media for the lectures in this course.
