S
Sumit Soman
Researcher at Indian Institute of Technology Delhi
Publications - 37
Citations - 415
Sumit Soman is an academic researcher from Indian Institute of Technology Delhi. The author has contributed to research in topics: Support vector machine & Global optimization. The author has an hindex of 10, co-authored 36 publications receiving 325 citations. Previous affiliations of Sumit Soman include Centre for Development of Advanced Computing & Indian Institutes of Technology.
Papers
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Journal ArticleDOI
High performance EEG signal classification using classifiability and the Twin SVM
Sumit Soman,Jayadeva +1 more
TL;DR: It is shown that the combination of 'classifiability' for selecting the optimal frequency band and the use of the Twin Support Vector Machine (Twin SVM) for classification, yields significantly improved generalization on benchmark BCI Competition datasets.
Journal ArticleDOI
Recent trends in neuromorphic engineering
Sumit Soman,Jayadeva,Manan Suri +2 more
TL;DR: A review of recent trends in neuromorphic engineering and its sub-domains is looked at, with an attempt to identify key research directions that would assume significance in the future.
Proceedings ArticleDOI
Deep learning for health informatics: Recent trends and future directions
TL;DR: Recent trends and applications of deep learning applied to the healthcare domain are reviewed and possible future directions that could be pursued further in this domain are suggested.
Proceedings ArticleDOI
Controlling an arduino robot using Brain Computer Interface
TL;DR: This paper establishes an application to control a robot on the Arduino platform by the use of a BCI system, which does not require training for individual users and achieves around 96% accuracy using computationally inexpensive feature extraction and classification techniques.
Journal ArticleDOI
Using Brain Computer Interface for Synthesized Speech Communication for the Physically Disabled
Sumit Soman,B.K. Murthy +1 more
TL;DR: A BCI-based system for generation of synthesized speech, which works on eye-blinks detected from the Electroencephalogram (EEG) signals of the user, which is particularly useful for patients suffering from locomotive disorders such as locked-in syndrome.