Recognition of Visual Memory Recall Processes Using Eye Movement Analysis

Abstract

This dataset was recorded to investigate the feasibility of recognising visual memory recall from eye movements. Eye movement data was recorded of participants looking at familiar and unfamiliar pictures from four picture categories: abstract, landscapes, faces, and buildings. The study was designed with two objectives in mind: (1) to elicit distinct eye movements by using a large screen and well-defined visual stimuli, and (2) to record natural visual behaviour without any active visual search or memory task by not asking participants for real-time feedback.

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Contact: Andreas Bulling,

The data is only to be used for non-commercial scientific purposes. If you use this dataset in a scientific publication, please cite the following paper:

1. 

   Recognition of Visual Memory Recall Processes Using Eye Movement Analysis

   Andreas Bulling, Daniel Roggen

   Proc. ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp), pp. 455-464, 2011.

   Abstract Links BibTeX Project

   Physical activity, location, as well as a person’s psychophysiological and affective state are common dimensions for developing context-aware systems in ubiquitous computing. An important yet missing contextual dimension is the cognitive context that comprises all aspects related to mental information processing, such as perception, memory, knowledge, or learning. In this work we investigate the feasibility of recognising visual memory recall. We use a recognition methodology that combines minimum redundancy maximum relevance feature selection (mRMR) with a support vector machine (SVM) classifier. We validate the methodology in a dual user study with a total of fourteen participants looking at familiar and unfamiliar pictures from four picture categories: abstract, landscapes, faces, and buildings. Using person-independent training, we are able to discriminate between familiar and unfamiliar abstract pictures with a top recognition rate of 84.3% (89.3% recall, 21.0% false positive rate) over all participants. We show that eye movement analysis is a promising approach to infer the cognitive context of a person and discuss the key challenges for the real-world implementation of eye-based cognition-aware systems.

   doi: 10.1145/2030112.2030172

   Paper: bulling11_ubicomp.pdf

   @inproceedings{bulling11_ubicomp, author = {Bulling, Andreas and Roggen, Daniel}, keywords = {Cognition- Awareness, Cognitive Context, Electrooculography (EOG), Eye Movement Analysis, Visual Memory Recall}, title = {Recognition of Visual Memory Recall Processes Using Eye Movement Analysis}, booktitle = {Proc. ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp)}, year = {2011}, pages = {455-464}, doi = {10.1145/2030112.2030172} }

The dataset has the following characteristics: