A Comparative Study on Generating Training-Data for Self-Paced Brain Interfaces

TitleA Comparative Study on Generating Training-Data for Self-Paced Brain Interfaces
Publication TypeJournal Article
Year of Publication2007
AuthorsBashashati, A., S. G. Mason, J. F. Borisoff, R. K. Ward, and G. E. Birch
JournalNeural Systems and Rehabilitation Engineering, IEEE Transactions on
Pagination59 -66
Date Publishedmar.
KeywordsAdult, Artificial Intelligence, Automated, BCI, brain, Brain computer interface, Cognition, computer simulation, direct brain interface, EEG, electroencephalogram, electroencephalography, Evoked Potentials, Female, handicapped aids, Humans, Imagination, Male, medical control systems, Middle Aged, Models, Neurological, Pattern Recognition, self-paced brain interfaces, severe motor disabilities, Spinal Cord Injuries, Task Performance and Analysis, training data generation, User-Computer Interface

Direct brain interface (BI) systems provide an alternative communication and control solution for individuals with severe motor disabilities, bypassing impaired interface pathways. Most BI systems are aimed to be operated by individuals with severe disabilities. With these individuals, there is no observable indicator of their intent to control or communicate with the BI system. In contrast, able-bodied subjects can perform the desired physical movements such as finger flexion and one can observe the movement as the indicator of intent. Since no external knowledge of intention is available for individuals with severe motor disabilities, generating the data for system training is problematic. This paper introduces three methods for generating training-data for self-paced BI systems and compares their performances with four alternative methods of training-data generation. Results of the offline analysis on the electroencephalogram data of eight subjects during self-paced BI experiments show that two of the proposed methods increase true positive rates (at fixed false positive rate of 2%) over that of the four alternative methods from 50.8%-58.4% to about 62% which corresponds to 3.6%-11.2% improvement


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