CAREER: Signal Models, Channel Capacity, and Information Rate for Noninvasive Brain Interfaces

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The PI's ultimate research goal is to empower people with severe speech and physical impairments so they can live their lives to the fullest extent independently and productively. To this end, he will in this project exploit and advance emerging brain computer interface (BCI) technology by rigorously developing macro-level dynamic models for the visual evoked potentials (VEP) in the brain measured by electroencephalography (EEG) in the context of BCI design. The models will enable a communication channel interpretation of the BCI and will allow analysis and design breakthroughs stemming from the application of information theory and digital communication concepts. Cortical dynamics and background processes will be modeled using a probabilistic dynamic framework at a spatiotemporal scale appropriate for BCI analysis and design. Model-based performance limits on bandwidth and calibration accuracy will then be determined, in order to develop better information coding techniques for optimal communication bandwidth (speed) utilization and better subject training and model calibration procedures for best accuracy return on investment of effort. Prototype real-time applications that operate at optimal or near-optimal performance levels utilizing the developed theoretical advancements for communication and control will be implemented, to enable access by and support independence for the target user groups. Project outcomes will disrupt the trend of black-box BCI design by building dynamic system models for stimulus-to-EEG systems encountered in BCI applications, and treating them as stochastic communication channels in order to characterize signals accordingly and to employ information theoretic approaches to analysis and design. This novel theoretical framework will enable model-based quantitative characterization of BCI performance limits and will allow the design of optimal or near-optimal coding/decoding strategies as well as improved calibration procedures that will have immediate impact on increasing bandwidth and intent detection accuracy, as well as calibration duration reduction in BCI systems - primary barriers between laboratory prototypes and real-world-worthy BCI products.Broader Impacts: If successful this project will advance BCI technology to the next level, thereby revolutionizing human computer interaction and empowering persons with physical disabilities by enabling seamless control of computers and devices. The project will afford, through collaboration with the Center for Subsurface Sensing and Imaging Systems (CenSSIS) at Northeastern University as well as colleagues across departments and institutions, opportunities to both undergraduate engineering and non-engineering majors for enhanced learning and collaboration skills by immersing them in interdisciplinary cutting-edge research and design projects with societal impact. The PI will engage high school students and teachers in the research through his institution's Center for STEM Education. And he will inform the broader public of ongoing technological advances in the BCI field and raise disability awareness through collaboration with the Cahners ComputerPlace at the Boston Museum of Science.