Brain-Computer Interfaces | Department of Psychology

Brain-Computer Interfaces

Collaborative Filtering for Brain-Computer Interaction

In the CNS lab (UNT), brain-computer interfaces (BCIs; click here for example) allow for communication between a user's brain and a given simulation (e.g., virtual worlds; adaptive virtual environments; video games). The BCIs are being applied in rehabilitation/training, neuropsychological assessment, and social cognitive neuroscience projects. Existing research in BCI applications includes two primary areas:

  1. Assistive technology: BCI allows for persons with disabilities to regain functional capacities and improve their quality of life.
  2. Rehabilitiation\training: the BCI may be used as a therapeutic application, in which patients recover their neurocognitive function through implicit and explicit alteration of their electroencephalographic (EEG) signals.

We are currently working with neuropsychologists in the DFW Metroplex to apply these technologies to persons with spinal cord injuries.

General CNS Lab Readings on BCIs and AVEs:

·Salisbury, D.B., Parsons, T.D., Monden, K., Trost, Z., & Driver, S. (2016). Brain-computer interface for individuals after inpatient spinal cord injury. Rehabilitation Psychology, 61, 4, 435-441. (PDF)

·Salisbury, D. B., Dahdah, M., Driver, S., Parsons, T. D., & Richter, K. M. (2016). Virtual reality and brain computer interface in neurorehabilitation. Proceedings (Baylor University. Medical Center), 29(2), 124-127. (PDF)

·Parsons, T.D., Carlew, A.R., & Salisbury, D. (2015). Brain-Computer Interface Targeting Cognitive Functions after Spinal Cord Injury. Archives of Clinical Neuropsychology, 30, 8.

·McMahan, T., Parberry, I., & Parsons, T.D. (2015). Modality Specific Assessment of Video Game Player's Experience Using the Emotiv Entertainment Computing. Entertainment Computing, 7, 1-6. (PDF)

·Salisbury, D., Driver, S., & Parsons, T.D. (2015). Brain-computer interface targeting non-motor functions after spinal cord injury. Spinal Cord, 53, S25-S26. (PDF)

·*McMahan, T., Parberry, I., & Parsons, T.D. (2015). Evaluating Player Task Engagement and Arousal using Electroencephalography. Procedia Manufacturing, 3, 2303 - 2310. (PDF)

·McMahan, T., Parberry, I., & Parsons, T.D. (2015). Evaluating Electroencephalography Engagement Indices during Video Game Play. Proceedings of the Foundations of Digital Games Conference, June 22 - June 25, 2015.

·Wu, D., Lance, B., & Parsons, T.D. (2013). Collaborative Filtering for Brain-Computer Interaction Using Transfer Learning and Active Class Selection. PLOS ONE, 1-18. (PDF)

·Parsons, T.D.,Courtney, C., & Dawson, M. (2013). Virtual Reality Stroop Task for Assessment of Supervisory Attentional Processing. Journal of Clinical and Experimental Neuropsychology, 35, 812-826. (PDF)

·Parsons, T.D., & J. Reinebold. (2012). Adaptive Virtual Environments for Neuropsychological Assessment in Serious Games. IEEE Transactions on Consumer Electronics, 58, 197-204. (PDF)

·Parsons, T.D., Rizzo, A.A., Courtney, C., & Dawson, M. (2012). Psychophysiology to Assess Impact of Varying Levels of Simulation Fidelity in a Threat Environment. Advances in Human-Computer Interaction, 5, 1-9. (PDF)

·Parsons, T.D., & Courtney, C. (2011) Neurocognitive and Psychophysiological Interfaces for Adaptive Virtual Environments. In C. Röcker, T. & M. Ziefle (Eds.), Human Centered Design of E-Health Technologies (pp. 208 - 233). Hershey: IGI Global. (PDF)

·Parsons, T.D. (2011) Affect-sensitive Virtual Standardized Patient Interface System. In D. Surry, T. Stefurak, & R. Gray (Eds.), Technology Integration in Higher Education: Social and Organizational Aspects (pp. 201 - 221). Hershey: IGI Global. (PDF)

·Wu, D., & Parsons, T.D. (2011). Active Class Selection for Arousal Classification. Lecture Notes in Computer Science, 6975, 132-141. (PDF)

·Wu, D., & Parsons, T.D. (2011). Inductive Transfer Learning for Handling Individual Differences in Affective Computing. Lecture Notes in Computer Science, 6975, 142-151. (PDF)

·Wu, D., Courtney, C., Lance, B., Narayanan, S.S., Dawson, M., Oie, K., & Parsons, T.D. (2010). Optimal Arousal Identification and Classification for Affective Computing: Virtual Reality Stroop Task. IEEE Transactions on Affective Computing, 1, 109-118. (PDF)

Adaptive Virtual Environments: Assessment/Training


Psychophysiological Systems

The CNS Lab equipment now includes a number of different psychophysiological systems:

Traditional EEG: 32 channel EEG system

Emotiv EEG: 14 channels (plus CMS/DRL references)

EEGLAB toolboxes for advanced EEG signal processing:

EEGLAB was developed in Dr. Scott Makeig's lab at UCSD. It is an interactive Matlab toolbox for processing continuous and event-related EEG.

Biopac MP150 EEG:

Allows for the recording of ECG, EDA, EMG, Respiration, and up to 16 leads of EEG. The Network Data Transfer (NDT) is a real-time data transfer system that allows access to the data being acquired for integration with third party applications


Binocular ViewPoint PC-60 Scene Camera Version with EyeFrame hardware--mounted into the Head Mounted Display (HMD)

Invasive Brain-Computer Interfaces

  • My work with human-computer interfaces began with Medtronics's invasive brain-computer interfaces in the Neurology Department at UNC.
  • I conducted research on the frontostriatal system and the cognitive and emotional sequelae of deep brain stimulation (DBS; see Parsons, Rogers, Braaten, Woods, and Troster, 2006; see also Woods, Parsons, et al., 2005).
  • My current work focuses on the development of noninvasive brain-computer interfaces and psychophysiologically adaptive virtual environments.

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