Low-burden, high-throughput brain-computer interfaces

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CONTEXT A brain-computer interface (BCI) facilitates direct communication between the human brain and a computer and as such provides an attractive, potentially highly intuitive means of interacting with technology. Indeed, there has been a surge of interest in BCI applications in diverse industry sectors, including: home automation, military communications, gaming, geriatrics, assistive technology, and law enforcement, among others. However, current BCI technology requires arduous machine calibration and user training before a user's brain signals can be accurately recognized. Further, BCI-based communication remains slow compared to human face-to-face interaction. These challenges need to be overcome before the potential of BCIs can be broadly realized in society. OBJECTIVES The focus of my Discovery Grant is to tackle these challenges, specifically to: 1. Reduce the burden of machine calibration and user training, and 2. Increase the rate of BCI-based communication. PROPOSED RESEARCH To reduce the burden of calibration and training, we will investigate several technological approaches to accelerating machine and user learning. One method is to endow the BCI with a mathematical model developed for other users and to determine a way to efficiently adjust that model to a new user, while the user is also learning under the guidance of an expert. To increase the rate of BCI-based communication, we will deploy algorithms that understand the meaning of words spoken to a BCI user and subsequently present the BCI user with only messages that are relevant to the conversation at hand. We will develop a fast and intuitive way for the BCI user to select the desired message, simply by reading or imagining the words. ANTICIPATED OUTCOMES The proposed research will generate new ways for machines to learn to recognize changing brain patterns of a human user and new algorithms that help human users acquire the necessary skill to interact with technology via mental activity alone. This research will also yield a new and more efficient way to communicate with another human being via a BCI. Collectively, these outcomes will improve the practicality and usefulness of BCI technology. The proposed program will train 8 PhD students and up to 3 post-doctoral fellows. IMPORTANCE This research is of importance to Canadians living with severe disabilities. An easy-to-learn and fast BCI will open doors to educational attainment and eventual employment to those who are unable to communicate through speech or movements. This research will introduce to the BCI research field new theory and algorithms for concurrent machine and user learning, the notion of a BCI coach, evidence about the value of multisensory feedback and potentially new ways to detect user preference through brain signals. Finally, the program will assert a Canadian role in the burgeoning field of BCI by producing technologies that can be transferred to companies and BCI experts to lead the emerging sector.