Mapping the cortical network involved in auditory attention

Sound arriving at our ears is a sum of acoustical energy from all the auditory sources in the environment. In order to dynamically follow different conversations in a crowded room, our brain must constantly direct attention to the auditory signal of interest and segregate sound that originated from other uninteresting sources. The fundamental neural circuitry and dynamics involved in this cognitive process, known as auditory scene analysis (ASA), is not well understood. We are currently studying the underlying neural bases of auditory attention in normal-hearing young adults and map its temporal characteristics across different cortical regions in ASA. [Funding agency: NIH-NIDCD]

Using neuroscience to design a next-generation Brain-Computer Interface

Harnessing the capability of reading and classifying brainwaves into the myriad of possible human cognitive states (referred to as brain-states) has been a long-standing engineering challenge. We are currently investigating how to best leverage the latest neuroscience knowledge to transform the current engineering approach in brain-state classification. By systematically assessing how we can capitalize on the similarity in brain function across subjects (a traditional neuroscience approach) and optimally incorporate a priori information to maximize classification algorithm performance at an individual level (a traditional engineering goal) we will be able to elucidate the benefit of an innovative, integrated neuroengineering approach in improving our abiity to classify human brain-states. [Funding agency: DOD-AFOSR, NSF-CSNE@UW]

Applying modern high-bandwidth communications theory to characterize cortical rhythms

The success of modern high-bandwidth communications hinges on a radio-frequency ecosystem that builds on concepts far advanced from the amplitude and frequency modulation developed for radios in the 1920’s and 1930’s. Within the human brain, the importance of long-range communications between different regions and between the brain and its periphery is well known. Many neuroscientists have borrowed theories from radio in an attempt to describe the effective communication pathways in our brains. However, in the past decade, there has been a growing understanding that cross-frequency rhythmic coupling (e.g., amplitude-amplitude, amplitude-phase coupling) is ubiquitous in the brain, and antiquated 1930’s radio-signal analysis techniques cannot adequately describe this complex cross-frequency brain coupling. The recent development of time-varying linear systems mathematics, critical to the world-wide growth in multi-user internet and cloud computing communications, has not yet found use in the brain science community. We propose to utilize these new engineering insights, known as widely linear signal processing and system identification, to provide new mathematical tools that will shed light on communications and the role of neuronal oscillations within the human brain. [Funding agency: NSF-CSNE@UW]

Audiovisual binding

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Cognitive load assessment in complex environment

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Characterizing different mental health illnesses using functional connectivity of brain networks

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