Predicting Academic Emotions Based on Brainwaves, Mouse Behaviour and Personality Profile

Azcarraga, Judith; Suarez, Merlin Teodosia

doi:10.1007/978-3-642-32695-0_64

Cited by 11 publications

(10 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In [35], researchers attempted to recognize an intimate human quality such as "affect" using a combination of facial images with prosodic and spectral features from the voice. In [3], they also tried to recognize emotions using other behavior measurements such as brainwaves or mouse movements.…”

Section: Related Workmentioning

confidence: 46%

Recognition of understanding level and language skill using measurements of reading behavior

Martínez-Gómez

Aizawa

2014

Proceedings of the 19th International Conference on Intelligent User Interfaces

View full text Add to dashboard Cite

The reading act is an intimate and elusive process that is important to understand. Psycholinguists have long studied the effects of task, personal or document characteristics on reading behavior. An essential factor in the success of those studies lies in the capability of analyzing eye-movements. These studies aim to recognize causal effects on patterns of eye-movements, by contriving variations in task, personal or document characteristics. In this work, we follow the opposite direction. We present a formal framework to recognize reader's level of understanding and language skill given measurements of reading behavior via eye-gaze data. We show significant error reductions to recognize these attributes and provide a detailed study of the most discriminative features.

show abstract

Section: Related Workmentioning

confidence: 46%

Recognition of understanding level and language skill using measurements of reading behavior

Martínez-Gómez

Aizawa

2014

Proceedings of the 19th International Conference on Intelligent User Interfaces

View full text Add to dashboard Cite

show abstract

“…As for the Motor Imagery, the performance (61.3% for the classification accuracy with a standard deviation of 4.3%) greatly suffered from the low classification accuracy that led to many errors and much slower interactions. Azcarraga & Suarez (2001) suggested that the Emotiv EPOC needs a small amount of time to normalize a user’s data. In their study they told the subjects to relax for a period of three minutes in order for the EEG to allow to create a baseline for EEG data.…”

Section: Introductionmentioning

confidence: 99%

Consumer grade EEG devices: are they usable for control tasks?

Maskeliūnas

Damaševičius

Martišius

et al. 2016

PeerJ

163

105

View full text Add to dashboard Cite

We present the evaluation of two well-known, low-cost consumer-grade EEG devices: the Emotiv EPOC and the Neurosky MindWave. Problems with using the consumer-grade EEG devices (BCI illiteracy, poor technical characteristics, and adverse EEG artefacts) are discussed. The experimental evaluation of the devices, performed with 10 subjects asked to perform concentration/relaxation and blinking recognition tasks, is given. The results of statistical analysis show that both devices exhibit high variability and non-normality of attention and meditation data, which makes each of them difficult to use as an input to control tasks. BCI illiteracy may be a significant problem, as well as setting up of the proper environment of the experiment. The results of blinking recognition show that using the Neurosky device means recognition accuracy is less than 50%, while the Emotiv device has achieved a recognition accuracy of more than 75%; for tasks that require concentration and relaxation of subjects, the Emotiv EPOC device has performed better (as measured by the recognition accuracy) by ∼9%. Therefore, the Emotiv EPOC device may be more suitable for control tasks using the attention/meditation level or eye blinking than the Neurosky MindWave device.

show abstract

“…Our results also highlight the importance of incorporating psychological variables, such as personality and motivational factors, for affective computing [21]. Because affect is displayed contextually, it is critical to integrate people's personal information as much as possible.…”

Section: Discussionmentioning

confidence: 45%

“…The mouse pressure was as discriminable as skin conductance measure for the detection of frustration. Azcarraga and Suarez [21] evaluated EEG signals and mouse activities (the number of mouse clicks, distance traveled, click duration) during algebra learning in an intelligent tutoring system (ITS) to predict subjects' emotions (N = 25) during ITS learning. Prediction rates based solely on EEG were 54 to 88%.…”

Section: B Related Work:mouse Movement Analysis For Affectivementioning

confidence: 46%

Mouse Trajectories and State Anxiety: Feature Selection with Random Forest

Yamauchi

2013

2013 Humaine Association Conference on Affective Computing and Intelligent Interaction

View full text Add to dashboard Cite

Do users' mouse activities reveal their affective states, as other bodily expressions such as postures and gestures signal emotions? When people are frustrated while trying to solve a puzzle or math problem, their frustration can be manifested in the way they use a computer mouse, such as pressing a button hard. But when a user is engaged in an innocuous and mundane task, what mouse activities provide a clue to detect affective states? To address these questions, we extracted 134 mouse trajectory variables in a choice-reaching experiment (N=234, female = 137, male = 97) and selected 3~8 key features by applying random forest regression. Using Spielberger's State Anxiety Inventory, we investigated the extent to which the selected trajectory features predict state anxiety of new subjects (N = 133; female = 75, male = 58). Results indicate that distributions of temporal features (e.g., velocity) as well as spatial characteristics (e.g., direction change) are indicative of users' state anxiety. A theoretical rationale, pros and cons of using mouse movement analysis and the role of other psychological variable for mouse-based affective computing are also discussed.

show abstract

Predicting Academic Emotions Based on Brainwaves, Mouse Behaviour and Personality Profile

Cited by 11 publications

References 10 publications

Recognition of understanding level and language skill using measurements of reading behavior

Recognition of understanding level and language skill using measurements of reading behavior

Consumer grade EEG devices: are they usable for control tasks?

Mouse Trajectories and State Anxiety: Feature Selection with Random Forest

Contact Info

Product

Resources

About