Measuring Academic Affective States of Students via Brainwave Signals

Mampusti, Ella T.; Ng, Jose S.; Quinto, Jarren James I.; Teng, Grizelda L.; Suarez, Merlin Teodosia; Trogo, Rhia

doi:10.1109/kse.2011.43

Cited by 28 publications

(20 citation statements)

References 10 publications

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“…This includes questionaires or tasks prior to and/or after the experiment concerning the emotion or the cognitive feature examined [2,7,14,17]. More specifically, certain well-known tasks are given to the subject in order to evaluate the results.…”

Section: Annotationmentioning

confidence: 99%

“…Furthermore, examples of interactive Brain Computer Interfaces (BCI) used to induce different emotions are encountered in [4,8,11]. Finally, learning activities and tasks such as the Stroop Test, Hanoi Towers, Berg's Card Sorting Task, and seeking information on the web can also be used as stimuli [2,7,17,22].…”

Section: Stimuli/inductionmentioning

confidence: 99%

“…EEG information is combined with information taken from the subject's face using visual input or observations in [2,5,8,11,17]. For the latter, "MindReader" -a system developed at MIT Media Lab -infers emotions from facial expressions and head movements in real-time.…”

Section: Multimodalitymentioning

confidence: 99%

“…K-nearest neighbor algorithm and Support Vector Machines (SVM) are incorporated in [11] in order to create predictive models of frustration and excitement, and in [17] to classify numerous features of raw EEG in order to create a model of human academic emotion (boredom, confusion, engagement and frustration).…”

Section: Classificationmentioning

confidence: 99%

“…Mu waves (8)(9)(10)(11)(12)(13) Hz) are diminished with movement or an intent to move, or when others are observed performing actions. Beta waves (12)(13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30) are associated with focus, concentration, high alertness, agitation and anxiety. Gamma waves (30-100 Hz) are associated with very high states of consciousness, focus and intellectual acuity, and have a strong presence during meditation.…”

Section: Introductionmentioning

confidence: 99%

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Affective, Natural Interaction Using EEG: Sensors, Application and Future Directions

Hondrou¹

2012

Lecture Notes in Computer Science

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Abstract. ElectroEncephaloGraphy signals have been studied in relation to emotion even prior to the establishment of Affective Computing as a research area. Technological advancements in the sensor and network communication technology allowed EEG collection during interaction with low obtrusiveness levels as opposed to earlier work which classified physiological signals as the most obtrusive modality in affective analysis. The current article provides a critical survey of research work dealing with broadly affective analysis of EEG signals collected during natural or naturalistic interaction. It focuses on sensors that allow such natural interaction (namely NeuroSky and Emotiv), related technological features and affective aspects of applications in several application domains. These aspects include emotion representation approach, induction method and stimuli and annotation chosen for the application. Additionally, machine learning issues related to affective analysis (such as incorporation of multiple modalities and related issues, feature selection for dimensionality reduction and classification architectures) are revised. Finally, future directions of EEG incorporation in affective and natural interaction are discussed.

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Section: Annotationmentioning

confidence: 99%