Brain-computer interfaces (BCIs) allow a user to interact with the world directly through brain activity. These systems are being developed to provide a communication method for users with severe motor impairments who are not able to control the movements of their arms, tongue, and even eyes well enough to communicate in the usual ways. While the cognitive abilities of these individuals are thought to be largely preserved, they are often described as being "locked in" to their bodies, unable to interact with the outside world through the usual means of typing, talking, etc. Electroencephalogram (EEG) based motor imagery BCIs attempt to distinguish brain activity by measuring electrical activity on the scalp caused by the user imagining moving different body parts. Commonly, such systems try to distinguish when the user is imagining moving their right or their left hand. Imagining different body parts can then be mapped to different tasks to allow a user to interact with the world (e.g., to turn a light on or off, or to move a robot arm to one object or another). The goal of this research is to make these types of systems easier for users to learn and more reliable, by improving the feedback that is given to the user and improving the classification of the brain signals. The work has the potential to open up this method of communication for more people, and project outcomes may have even broader impact by enabling us to learn more about brain signals that can be used for communication in BCIs. In addition, diverse graduate students will be trained in interdisciplinary research, and undergraduate students in the BCI class will work on small related projects, some of which will be presented to high school students to encourage and stimulate their interest in science.
The ability of users to generate discriminable control signals is very variable. Moreover, environmental effects such as other brain processes, emotion and fatigue affect current BCI systems. The goal of this project is to improve the usability of EEG-based motor-imagery brain-computer interfaces. To this end, a multi-pronged approach will be used. First, richer feedback will give users a better visualization of the effects of their imagery and provide them with a better chance to learn how to discriminate the motor imagery of different body parts. Second, the machine classification of the EEG signal during motor imagery will be improved. This will include looking for other signals that may provide additional insight into the top-level state and goals of the user as well as developing new deep learning algorithms that can benefit from multi-task learning and transfer learning between individuals. Third, different closed-loop control methods will be explored to improve the total information transfer rate of the BCI as well as to reduce the number of training trials needed. The team's prior work has shown that interactive signals that respond to the feedback provided by the system are more robust to system estimation errors and non-stationarities. These signals can arise passively but also can be actively used by exploiting interactive commands that vary with the received feedback. Whether active control of interactive commands, or active control of standard commands with passive interactive recognition, performs better will be tested.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
