Geometric Pattern Analysis and Mental Task Design for a Brain-Computer Interface

Colorado State University, Fort Collins CO

Investigators

Abstract

The goal of this project is to develop novel electroencephalogram (EEG) classification methods that result in a practical, real-time brain-computer interfaces (BCI) system. BCIs are hardware and software systems that sample EEG signals from electrodes placed on the scalp and extract patterns from EEG that indicate the mental activity being performed by the person. The long-term goal of this line of research is a new mode of communication for victims of diseases and injuries resulting in the loss of voluntary muscle control, such as amyotrophic lateral sclerosis (ALS), high-level spinal cord injuries or severe cerebral palsy. The autonomic and intellectual functions of such subjects continue to be active. This can result in a "locked-in" syndrome in which a person is unable to communicate to the outside world. The interpretation of information contained in EEG may lead to a new mode of communication with which subjects can communicate with their care givers or directly control devices such as televisions, wheel chairs, speech synthesizers and computers. The objectives of this project are the design and testing of an EEG system for experimentation in real-time EEG pattern analysis constructed of off-the-shelf components for under $5,000, development of new techniques for a novel approach to studying the cognitive components of mental tasks and how they vary in time and across subjects, demonstration that real-time feedback to the subject will produce a biofeedback situation in which the subject can learn to modify their EEG to increase classification accuracy, proof by demonstration that accuracy and classification time will be sufficient for two persons to interact over the net in a simple game controlled by two BCI systems. The evaluation of the results of this project in light of these objectives will be based on the accuracy of EEG classification, the speed with which the classification can be performed, and the expense of the EEG system and of its maintenance and extendibility. The most significant impact of this project to the disabled community will be an easier to use, affordable BCI system. The inclusion of a wide range of mental tasks will result in a better understanding of which mental tasks are easiest for subjects to consistently perform and for detection algorithms to reliably identify. Better BCI systems will also be significant for other classes of users who can benefit from augmented communication interfaces in applications that require extremely fast commands. The significance of this project to the BCI research community is the specification and testing of the inexpensive system for experimentation with EEG signal analysis. The system based on off-the-shelf components and software to be developed and made publicly available is expected to allow a number of additional research groups to enter the BCI field. Also, this project's results on the analysis of cognitive components in EEG measured during a wide range of mental tasks will broaden the set of mental activities available to users of BCI systems.