The effect of target and non-target similarity on neural classification performance: a boost from confidence.
Brain computer interaction (BCI) technologies have proven effective in utilizing single-trial classification algorithms to detect target images in rapid serial visualization presentation tasks. While many factors contribute to the accuracy of these algorithms, a critical aspect that is often overlooked concerns the feature similarity between target and non-target images. In most real-world environments there are likely to be many shared features between targets and non-targets resulting in similar neural activity between the two classes. It is unknown how current neural-based target classification algorithms perform when qualitatively similar target and non-target images are presented. This study address this question by comparing behavioral and neural classification performance across two conditions: first, when targets were the only infrequent stimulus presented amongst frequent background distracters; and second when targets were presented together with infrequent non-targets containing similar visual features to the targets. The resulting findings show that behavior is slower and less accurate when targets are presented together with similar non-targets; moreover, single-trial classification yielded high levels of misclassification when infrequent non-targets are included. Furthermore, we present an approach to mitigate the image misclassification. We use confidence measures to assess the quality of single-trial classification, and demonstrate that a system in which low confidence trials are reclassified through a secondary process can result in improved performance.
Translational Neuroscience Branch, US Army Research Laboratory, Human Research and Engineering Directorate Aberdeen Proving Grounds, MD, USA.
Amar R
AR
Marathe
Translational Neuroscience Branch, US Army Research Laboratory, Human Research and Engineering Directorate Aberdeen Proving Grounds, MD, USA.
Anthony J
AJ
Ries
Translational Neuroscience Branch, US Army Research Laboratory, Human Research and Engineering Directorate Aberdeen Proving Grounds, MD, USA ; Department of Computer Science, University of Texas at San Antonio San Antonio, TX, USA.
Vernon J
VJ
Lawhern
Translational Neuroscience Branch, US Army Research Laboratory, Human Research and Engineering Directorate Aberdeen Proving Grounds, MD, USA.
Brent J
BJ
Lance
Translational Neuroscience Branch, US Army Research Laboratory, Human Research and Engineering Directorate Aberdeen Proving Grounds, MD, USA.
Jonathan
J
Touryan
Translational Neuroscience Branch, US Army Research Laboratory, Human Research and Engineering Directorate Aberdeen Proving Grounds, MD, USA.
Kaleb
K
McDowell
Intelligent Systems Research Centre, School of Computing and Intelligent Systems, University of Ulster Londonderry, UK.
Hubert
H
Cecotti
EEG; brain-computer interface; classification; confidence; rapid serial visual presentation; single-trial analysis