A Brain-Computer Interface (BCI) for the Detection of Mine-Like Objects in Sidescan Sonar Imagery

• Published in: IEEE journal of oceanic engineering Vol. 41; no. 1; pp. 123 - 138
• Main Authors: Barngrover, Christopher, Althoff, Alric, DeGuzman, Paul, Kastner, Ryan
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.01.2016; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Boosting; brain-computer interface (BCI); Calibration; Computer vision; Electroencephalography; Human; Human-computer interface; Image detection; Marine; mine-like object (MLO); object detection; Object recognition; Processing speed; rapid serial visual presentation (RSVP); sidescan sonar; Sonar; Sonar detection; Support vector machines; Training; Visualization
• ISSN: 0364-9059; 1558-1691
• DOI: 10.1109/JOE.2015.2408471

Detection of mine-like objects (MLOs) in sidescan sonar imagery is a problem that affects our military in terms of safety and cost. The current process involves large amounts of time for subject matter experts to analyze sonar images searching for MLOs. The automation of the detection process has been heavily researched over the years and some of these computer vision approaches have improved dramatically, providing substantial processing speed benefits. However, the human visual system has an unmatched ability to recognize objects of interest. This paper posits a brain-computer interface (BCI) approach, that combines the complementary benefits of computer vision and human vision. The first stage of the BCI, a Haar-like feature classifier, is cascaded in to the second stage, rapid serial visual presentation (RSVP) of images chips. The RSVP paradigm maximizes throughput while allowing an electroencephalography (EEG) interest classifier to determine the human subjects' recognition of objects. In an additional proposed BCI system we add a third stage that uses a trained support vector machine (SVM) based on the Haar-like features of stage one and the EEG interest scores of stage two. We characterize and show performance improvements for subsets of these BCI systems over the computer vision and human vision capabilities alone.