Grants and Contributions:

Grant or Award spanning more than one fiscal year. (2017-2018 to 2022-2023)

The long term research objective (vision) of this program is to significantly advance research in signal processing by introducing a unifying and powerful information processing framework. To achieve this vision the program introduces a model-based framework which is comprised of local, processing units, an inference/adaptation engine, and a control unit.
This brain-inspired, cognitive framework employs a measurement module to maximize information gain from the environment, uses memory-based attentional mechanisms to process information, deploys a reasoning/decision making engine to identify intelligent choices in an uncertain environment, and utilizes feedback control to interact with the environment in an efficient and cost effective manner.

The research program will demonstrate that the proposed framework decomposes the signal/information processing problem into a set of considerably reduced complexity processing tasks which are far easier to design and implement. Thus, it constitutes a natural setting for studying challenging problems in unsupervised learning, optimal estimation, decision and control. Moreover it will be shown that the framework can be applied to both well-studied linear problems as well as complex learning problems characterized by non-linear, non-Gaussian, non-stationary conditions.
In addition, to having significant theoretical implications, the results of the program will contribute to the advancement of the state-of-the-art in interdisciplinary areas such as neuroscience data processing, and advance technological solutions which promote well-being and enhance quality of life. To that end, two open research problems (challenges) from the field of EEG-based brain-machine interaction (BMI) will be used to demonstrate the framework’s utility. Namely, the framework will be used to: (i) reconcile and unify spatial filters used in cognitive electrophysiology, and (ii) show-case how these spatial filters can be used in the so-called zero-training BMI interfaces for rehabilitation systems.

The program’s intellectual merit is the development of an all-encompassing cognitive information processing framework. It will produce novel results of importance to the advancement of methodology, theoretical understanding of brain-inspired information processing, and useful solutions in its practical applications. Innovations in the areas of cognitive signal processing will have applications far beyond the engineering community with profound impact on quality of life and healthcare.

Lastly, the program will provide a platform for training and development experience for the students involved, and it will be a major inspiration for further studies in cognitive systems. It will help spring new communities of research contributors, and developers, around specific interests in cognitive information processing.