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Proceedings Paper

Single-trial EEG RSVP classification using convolutional neural networks
Author(s): Jared Shamwell; Hyungtae Lee; Heesung Kwon; Amar R. Marathe; Vernon Lawhern; William Nothwang
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Paper Abstract

Traditionally, Brain-Computer Interfaces (BCI) have been explored as a means to return function to paralyzed or otherwise debilitated individuals. An emerging use for BCIs is in human-autonomy sensor fusion where physiological data from healthy subjects is combined with machine-generated information to enhance the capabilities of artificial systems. While human-autonomy fusion of physiological data and computer vision have been shown to improve classification during visual search tasks, to date these approaches have relied on separately trained classification models for each modality. We aim to improve human-autonomy classification performance by developing a single framework that builds codependent models of human electroencephalograph (EEG) and image data to generate fused target estimates. As a first step, we developed a novel convolutional neural network (CNN) architecture and applied it to EEG recordings of subjects classifying target and non-target image presentations during a rapid serial visual presentation (RSVP) image triage task. The low signal-to-noise ratio (SNR) of EEG inherently limits the accuracy of single-trial classification and when combined with the high dimensionality of EEG recordings, extremely large training sets are needed to prevent overfitting and achieve accurate classification from raw EEG data. This paper explores a new deep CNN architecture for generalized multi-class, single-trial EEG classification across subjects. We compare classification performance from the generalized CNN architecture trained across all subjects to the individualized XDAWN, HDCA, and CSP neural classifiers which are trained and tested on single subjects. Preliminary results show that our CNN meets and slightly exceeds the performance of the other classifiers despite being trained across subjects.

Paper Details

Date Published: 25 May 2016
PDF: 10 pages
Proc. SPIE 9836, Micro- and Nanotechnology Sensors, Systems, and Applications VIII, 983622 (25 May 2016); doi: 10.1117/12.2224172
Show Author Affiliations
Jared Shamwell, U.S. Army Research Lab. (United States)
Hyungtae Lee, U.S. Army Research Lab. (United States)
Heesung Kwon, U.S. Army Research Lab. (United States)
Amar R. Marathe, U.S. Army Research Lab. (United States)
Vernon Lawhern, U.S. Army Research Lab. (United States)
William Nothwang, U.S. Army Research Lab. (United States)


Published in SPIE Proceedings Vol. 9836:
Micro- and Nanotechnology Sensors, Systems, and Applications VIII
Thomas George; Achyut K. Dutta; M. Saif Islam, Editor(s)

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