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

Abstract

Abstract-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.Index Terms-Boosting, brain-computer interface (BCI), minelike object (MLO), object detection, rapid serial visual presentation (RSVP), sidescan sonar.

Deep Gabor Neural Network for Automatic Detection of Mine-Like Objects in Sonar Imagery

“…In [19], Sawas and Petillot applied the Haar-like features and a cascade of boosted classifiers, which were first introduced by Viola and Jones [31]. In [21], Barngrover et al also utilized the Haar-like feature classifier to generate image patches (around regions of interest), which are then processed by subjects using the rapid serial visual presentation paradigm. Other feature-based methods used the geometric visual descriptors, such as scale-invariant feature transform (SIFT) [18], [32], [33] and local binary pattern (LBP) [20], [34].…”

A Decision Support System to Ease Operator Overload in Multibeam Passive Sonar

“…2. The signal present in beam 25 is clearly highlighted by the dark gray point, Y 25 given by (11), through its location in the low-probability cyan region. The linear structure implies a linear latent relationship in the data, which is expected as the array used for this recording was a uniformly spaced line array with standard beamforming, resulting in a single latent parameter here, namely the proximity to the speedboat contact.…”

“…A data fusion approach was developed in [10] to present signals from different sources, but this did not highlight anomalies or offer a reduced-signal representation to highlight sonar beams of interest. Barngrover et al [11] presented a novel interface between sonar operator and sensor information, utilizing the experience of the human in the analysis process. Despite generating a summary of the information in a more visually appealing method than standard sonar imagery, this approach failed to generate an aesthetically intuitive visualization of the observed information, which would reduce the information burden on the human user.…”

Filter Bank-Driven Multivariate Synchronization Index for Training-Free SSVEP BCI

“…In recent years, an increasing number of research efforts have been dedicated to the development of BCI systems [5,6], with applications extended from the realization of wheelchair operation [7], prosthetic control [8], neurological rehabilitation This work was supported in part by the Natural Science Foundation of China 61803255 and the Natural Science Foundation of Shanghai 18ZR1416700). (Corresponding author: Raofen Wang) [9] for physically challenged patients to a wider range of practical scenarios, such as virtual reality games [10], military detection [11] and operator fatigue detection [12,13]. Depending on the specific activity patterns of the brain, EEG signals applied to BCI development mainly include: slow cortical potential (SCP) [14], P300 evoked potential [15,16], steady-state visual evoked potential (SSVEP) [17,18], eventrelated desynchronization (ERD) and synchronization (ERS) [19,20].…”