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The effect of target and non-target similarity on neural classification performance: a boost from confidence.

Marathe AR, Ries AJ, Lawhern VJ, Lance BJ, Touryan J, McDowell K, Cecotti H - Front Neurosci (2015)

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Translational Neuroscience Branch, US Army Research Laboratory, Human Research and Engineering Directorate Aberdeen Proving Grounds, MD, USA.

ABSTRACT
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.

No MeSH data available.


RSVP task and stimuli in the current experiment. Participants were required to detect target images while ignoring non-targets and background distractors.
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Figure 1: RSVP task and stimuli in the current experiment. Participants were required to detect target images while ignoring non-targets and background distractors.

Mentions: Participants were seated 75 cm from a monitor and viewed a series of images from a simulated desert metropolitan environment in a RSVP paradigm (Figure 1). Images (960 × 600 pixels, 96 dpi, subtending 36.3° × 22.5°) were presented using E-prime software for 500 ms (2 Hz) with no inter-stimulus interval.


The effect of target and non-target similarity on neural classification performance: a boost from confidence.

Marathe AR, Ries AJ, Lawhern VJ, Lance BJ, Touryan J, McDowell K, Cecotti H - Front Neurosci (2015)

RSVP task and stimuli in the current experiment. Participants were required to detect target images while ignoring non-targets and background distractors.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4544215&req=5

Figure 1: RSVP task and stimuli in the current experiment. Participants were required to detect target images while ignoring non-targets and background distractors.
Mentions: Participants were seated 75 cm from a monitor and viewed a series of images from a simulated desert metropolitan environment in a RSVP paradigm (Figure 1). Images (960 × 600 pixels, 96 dpi, subtending 36.3° × 22.5°) were presented using E-prime software for 500 ms (2 Hz) with no inter-stimulus interval.

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Translational Neuroscience Branch, US Army Research Laboratory, Human Research and Engineering Directorate Aberdeen Proving Grounds, MD, USA.

ABSTRACT
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.

No MeSH data available.