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:
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:
·Parsons, T.D., McMahan, T., & Parberry, I. (in press). Neurogaming-based Classification of Player Experience Using Consumer-Grade Electroencephalography. IEEE Transactions on Affective Computing. (PDF)
·Parsons, T.D., & Courtney, C. (in press). Interactions between Threat and Executive Control in a Virtual Reality Stroop Task. IEEE Transactions on Affective Computing. (PDF)
·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
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