Non-invasive Brain Computer Interfaces for Assistive Technologies

Brain-computer interfaces (BCIs) promise to provide a novel access channel for assistive technologies, including augmentative and alternative communication systems, for people with severe speech and physical impairments (SSPI). Among various options, non-invasive electroencephalogram (EEG)-based BCIs are considered as safe and more portable solutions which are potentially suitable for home use. The applications of these BCIs can include wheelchair navigation and typing. Research on the subject has been accelerating significantly in the last decade and the research community took great strides toward making non-invasive BCI a practical reality for individuals with SSPI. Nevertheless, the end goal has still not been reached and there is much work to be done to produce real-world-worthy systems that can be comfortably, conveniently, and reliably used by individuals with SSPI with help from their families and care givers who will need to setup and maintain these systems at home.

In the Cognitive Systems Lab, we develop solutions to improve BCIs. Different stimulation strategies can induce unique detectable signatures in the EEG, such as steady state evoked potentials and event related potentials. Brain waves in response to these stimuli can be processed using machine learning and signal processing techniques. Being non-invasive, EEG signals have very low signal to noise ratios. One of the methods to increase the classification performance is to consider the context information in applications. For example, in a typing task, one can use a language model to predict the most probable next letter at each point in a sentence. In a navigation task, such as controlling a wheelchair, historical data can provide context that provides information about probabilities for feasible directions.

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