Classification of Error Related Potentials using Convolutional Neural Networks


A class of Electroencephalogram (EEG) generated when a person is present with a stimuli are Error-related potentials (ErrP). Classification of ErrP revolutionized the domain of Brain-Computer Interface (BCI), however, due to its poor classification accuracy, it is difficult to use it in practical applications. The use of deep learning techniques such as Convolutional neural networks (ConvNets) has been growing because of its end-to-end learning capabilities and classification performance in other fields such as computer vision, speech processing and text synthesis. Very little work has been published towards classification of EEG or its classes using deep learning approaches because of its unexplored benefits in BCI applications. In this paper we propose a novel deep ConvNet architecture to accurately classify Error-related potentials. Using the very recent advances from the field of machine learning, such as batch normalization and dropout layers, we designed a new deep ConvNet architecture. We evaluate and compare the performance of two proposed deep ConvNets by finding its classification accuracy for EEG recordings from same and cross sessions as well as cross subjects for different trials of individual subjects. We see that the net proposed later with newer layers performs very well by improving the mean performance by 4%.

2019 9th International Conference on Cloud Computing, Data Science Engineering (Confluence)
Sunny Arokia Swamy Bellary
Sunny Arokia Swamy Bellary
Engineer/Scientist II

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James M. Conrad
James M. Conrad
Professor, Electrical and Computer Engineering

James M. Conrad received his bachelor’s degree in computer science from the University of Illinois, Urbana, and his master’s and doctorate degrees in computer engineering from North Carolina State University. He is currently a professor at the University of North Carolina at Charlotte. He has served as an assistant professor at the University of Arkansas and as an instructor at North Carolina State University. He has also worked at IBM, Ericsson/Sony Ericsson, and BPM Technology. Dr. Conrad is a Professional Engineer, a Senior Member of the IEEE and a Certified Project Management Professional (PMP). He is also a member of Eta Kappa Nu and the Project Management Institute. He served on the IEEE Board of Directors as Region 3 director for 2016-2017, and again as a director in 2020 when he also served as IEEE-USA President. He is the author of numerous books, book chapters, journal articles, and conference papers in the areas of embedded systems, robotics, parallel processing, and engineering education.