The following paper of Harish (my Ph.D. student) is accepted for publication in IEEE SMC 2017. SMC 2017 is the flagship conference of the IEEE Systems, Man, and Cybernetics Society. It provides an international forum for researchers and practitioners to report up-to-the-minute innovations and developments, summarize state-of-the-art, and exchange ideas and advances in all aspects of systems science and engineering, human-machine systems, and cybernetics. As a leading author, Harish has developed a new algorithm for Epilepsy patients to localize their brain activities.
Title: Automatic Response Assessment in Regions of Language Cortex in Epilepsy Patients Using ECoG-based Functional Mapping and Machine Learning
Accurate localization of brain regions responsible for language and cognitive functions in Epilepsy patients should be carefully determined prior to surgery. In this study, we address the limitation of the current RTFM (real-time functional mapping) signal estimation methods by analyzing the full frequency spectrum of the signal and applying machine learning algorithms. Experimental results obtained from RTFM of six adult patients in a strictly controlled experimental setup reveal the state of the art detection accuracy of ≈ 78% for the language comprehension task, an improvement of 23% over the conventional RTFM estimation method. To the best of our knowledge, this is the first study exploring the use of machine learning approaches for determining RTFM signal characteristics and using the whole frequency band for better region localization. Our results demonstrate the feasibility of machine learning based RTFM signal analysis method over the full spectrum to be a clinical routine in the near future.
We are currently improving our achieved results with deep-learning methodologies.