Description
THE PRESENT DATASET CONTAINS THREE FOLDERS:
i. DEMOGRAPHIC AND ANNOTATION DATASET: this .xlsx spreadsheet contains the relevant demographic data for all 25 participants in our study. Also, it includes the participant evaluation for every paragraph read during our experiment for three factors: Interest, Attentiveness and Effort. Those values are important to correlate with the Flesch–Kincaid readability score. Please refer to the paper for more details.
ii. EXPERIMENT DATASET: the folder contains all the data collected during our experiments. There were 25 participants, each one with the data obtained during 16 readings in our tests, organised in consecutive folders. Each folder contains the data generated through the readings of two apparatus used in our study, an Eye-tracking and an EEG helmet. Video feed has not been included as the dataset is anonymised.
iii. OPENFACE DATASET: this folder includes, amongst other data, the HOG files obtained through the processing of the participant's videos, (not included) using OpenFace, a Python and Torch implementation of face recognition with deep neural networks (https://cmusatyalab.github.io/openface/#openface). The histogram of oriented gradients (HOG) is a feature descriptor used in computer vision and image processing for the purpose of object detection.
i. DEMOGRAPHIC AND ANNOTATION DATASET: this .xlsx spreadsheet contains the relevant demographic data for all 25 participants in our study. Also, it includes the participant evaluation for every paragraph read during our experiment for three factors: Interest, Attentiveness and Effort. Those values are important to correlate with the Flesch–Kincaid readability score. Please refer to the paper for more details.
ii. EXPERIMENT DATASET: the folder contains all the data collected during our experiments. There were 25 participants, each one with the data obtained during 16 readings in our tests, organised in consecutive folders. Each folder contains the data generated through the readings of two apparatus used in our study, an Eye-tracking and an EEG helmet. Video feed has not been included as the dataset is anonymised.
iii. OPENFACE DATASET: this folder includes, amongst other data, the HOG files obtained through the processing of the participant's videos, (not included) using OpenFace, a Python and Torch implementation of face recognition with deep neural networks (https://cmusatyalab.github.io/openface/#openface). The histogram of oriented gradients (HOG) is a feature descriptor used in computer vision and image processing for the purpose of object detection.
Date made available | 22 Apr 2020 |
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Publisher | University of St Andrews |
Temporal coverage | 22 Feb 2020 - 22 Feb 2024 |
Date of data production | 21 Aug 2019 - 12 Oct 2019 |
Geographical coverage | St Andrews, Scotland, UK |
Keywords
- Attention
- EEG
- eye tracking
- reading academically
- HOG
- OpenFace