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Brain computer interfaces: The brain is remarkable for deftly and exquisitely controlling movements in space. Damage to neural circuits due to brain injury such as stroke or ALS etc. results in severe motor impairments affecting the quality of life. An intriguing technology to augment functional outcomes is brain computer interfaces (BCIs), that neurally decode intended movements directly and bypass intrinsic corticospinal pathways. The focus of my research is towards the development of stable and robust decoding algorithms for real-world, plug-and-play use of BCIs for reaching, grasping and manipulating objects in the environment (Silversmith*, Abiri*, Hardy*, Natraj* et. al, 2021, Natraj, 2023). More information is detailed here.

Neural representation of movement: An important first step in the development of BCI algorithms is an in depth understanding of how the brain represents and encodes intended movements. This involves simultaneous measurements of high-dimensional neural, kinematic and behavioral activity, along with the appropriate statistical signal processing methods to uncover hidden relationships between neural and kinematic data in high-dimensional signals. Such experiments can be carried out in healthy human participants, with clinical patients and also longitudinally with motor skill learning using simple table-top experimental setups and with multi-modal measurements (e.g., Natraj, 2013, Natraj 2015, Tu-Chan, Natraj et. al., 2017, Natraj 2018, Natraj, 2022)

Sleep dependent information processing: A very closely related goal is to understand the neural processing of motor information (such as learning to use a BCI) during the times away from the task. A particularly relevant 'offline' time-period is during 'sleep' when the brain processes, prunes, and consolidates information from daytime awake periods. Understanding sleep-dependent information processing also has important translational benefit towards psychiatric conditions such as posttraumatic disorder (PTSD) that is characterized by highly disturbed sleep. The major focus of my research in sleep neuroscience is towards multivariate sleep-signal analyses pipelines and their applications towards skill consolidation and neural injury (Natraj 2023, 2022)

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