We examine if EEG-based cognitive load (CL) estimation is generalizable across the character, spatial pattern, bar graph and pie chart-based visualizations for the n-back task. CL is estimated via two recent approaches: (a) Deep convolutional neural network , and (b) Proximal support vector machines . Experiments reveal that CL estimation suffers across visualizations calling for for effective machine learning techniques to benchmark visual interface usability for a given analytic task.
|Title of host publication||ICMI '18: Proceedings of the 20th International Conference on Multimodal Interaction|
|Number of pages||5|
|Publication status||Published - 16 Oct 2018|
|Event||20th International Conference on Multimodal Interaction, ICMI 2018 - Boulder, United States|
Duration: 16 Oct 2018 → 20 Oct 2018
|Conference||20th International Conference on Multimodal Interaction, ICMI 2018|
|Period||16/10/18 → 20/10/18|