Publikation
Implicit Search Intent Recognition using EEG and Eye Tracking: Novel Dataset and Cross-User Prediction
Mansi Sharma; Shuang Chen; Philipp Müller; Maurice Rekrut; Antonio Krüger
In: Proceedings of the 25th International Conference on Multimodal Interaction. ACM International Conference on Multimodal Interaction (ICMI), Pages 345-354, IEEE, 2023.
Zusammenfassung
For machines to efectively assist humans in challenging visual
search tasks, they must diferentiate whether a human is simply
glancing into a scene (navigational intent) or searching for a target
object (informational intent). Previousresearch proposed combining
electroencephalography (EEG) and eye-tracking measurements to
recognize such search intents implicitly, i.e., without explicit user
input. However, the applicability of these approaches to real-world
scenarios sufers from two key limitations. First, previous work
used fxed search times in the informational intent condition - a
stark contrast to visualsearch, which naturally terminates when the
target is found. Second, methods incorporating EEG measurements
addressed prediction scenarios that require ground truth training
data from the target user, which isimpractical in many use cases. We
address these limitations by making the frst publicly available EEG
and eye-tracking dataset for navigational vs. informational intent
recognition, where the user determines search times. We present
the frst method for cross-user prediction of search intents from
EEG and eye-tracking recordings and reach 84.5% accuracy in leaveone-user-out evaluations - comparable to within-user prediction
accuracy (85.5%) but ofering much greater fexibility