Closed-Loop Stimulus Optimization to Increase Communication Efficiency in Brain-Computer Interfaces

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ABSTRACT This administrative supplement is in response to the Notice of Special Interest to improve the artificial intelligence and machine learning (AI/ML)-Readiness of NIH-supported sata (NOT-OD-21-094). Summary of Parent Award. Augmentative and alternative communication (AAC) systems are used by people with communication and motor disabilities, such as amyotrophic lateral sclerosis (ALS), to communicate and interact with their environment. There are conventional AAC devices that are controlled by access methods such as touch, switch, head tracking and eye gaze; however, these access methods become difficult or impossible to use when sustained muscle control or voluntary motor control is lost. There are brain-computer interface (BCI) communication systems, such as the P300 speller, that use sensory stimulation to elicit and then detect sensory neural responses in electroencephalography (EEG) data. However, communication with stimulus driven BCIs is suboptimal due to relying on inherently noisy EEG data and highly variable neural responses for BCI control. BCI communication rates can potentially be improved by leveraging information in EEG data in real-time to optimally tune the BCI system’s parameters to maximise BCI performance under conditions of uncertainty. This work investigates a novel closed-loop stimulus selection algorithm that optimises the stimulus presentation schedule of the P300 speller in real-time based on the measured EEG data and the BCI system’s belief about the user’s intent. Aim 1 develops and tests the novel algorithm in a cohort of abled- bodied individuals to evaluate the real-time feasibility and utility of closed-loop stimulus selection. Aim 2 will test the closed-loop stimulus selection algorithm in a cohort of individuals with ALS to assess the performance of the algorithm in target BCI end users. Goals of this Supplement. There is a current unmet need for large, diverse BCI datasets that include target BCI end users for BCI algorithm development, particularly with the popularity of data hungry deep learning models. Based on NIH-supported research for 10+ years, we have acquired a large amount of single- and multi-session data from P300 speller studies with abled-bodied participants and participants with ALS using different stimulus presentation paradigms. Guided by FAIR principles, in this supplement: (1) we will perform data curation, data cleaning and data engineering to develop a cross-platform readable P300 speller dataset with common data and metadata elements and make this dataset publicly available; and we will demonstrate the usability of this dataset in (2) an AI/ML application focused on developing robust data representations to mitigate the negative effect of variabilities in EEG data on AI/ML algorithms; and (3) in student research programs focused on skill development in data science and AI/ML. A large, inclusive and accessible BCI dataset will have significant impact in the BCI community and the broader AI/ML community, as it will support research to develop and compare novel data representations, stimulus paradigms and neural signal decoding algorithms towards establishing BCIs as viable AAC devices.