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VERSION:2.0
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TZID:America/Chicago
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DTSTART:19700308T020000
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DTSTART:19701101T020000
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BEGIN:VEVENT
DTSTAMP:20251016T135143Z
LOCATION:Riverside East
DTSTART;TZID=America/Chicago:20251015T173000
DTEND;TZID=America/Chicago:20251015T183000
UID:HFESAM_ASPIRE 2025_sess138_POST367@linklings.com
SUMMARY:Decoding Changes in Tactile Stimuli Using Multivariate Pattern Ana
 lysis of EEG Data
DESCRIPTION:Lauren Horde and Sara Riggs (University of Virginia)\n\nVibrot
 actile and multimodal displays are often designed to rely on the human abi
 lity to accurately detect when a change occurs within a display. This work
  investigates whether there is specific, objective information about the c
 haracteristics of tactile stimuli, that is, stimuli containing a change ve
 rsus stimuli without a change, within event-related potential data from an
  EEG that can be extracted via multivariate decoding methods. A supervised
  machine learning classifier (i.e., support vector machine) was trained to
  classify change and no-change tactile stimuli based on the scalp voltage 
 at electrode sites overlaying the somatosensory cortex. The classifier cou
 ld predict with 63% accuracy whether or not a change trial had been presen
 ted to participants between 390-410 ms post-stimulus, demonstrating the fe
 asibility of applying ERP-based multivariate decoding techniques to the st
 udy of tactile information processing. Therefore, the 390-410 ms post-stim
 ulus time frame contains distinctive information that can be indicative of
  whether a change has occurred in tactile stimuli. The outcomes of this st
 udy can be broadly applied to advance brain-machine interfaces that seek t
 o predict when specific tactile information is perceived and received, e.g
 ., for the development of adaptive displays in which information is select
 ively presented based on environmental or task conditions.\n\n
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