ABE: An Agent-Based Software Architecture for a Multimodal Emotion Recognition Framework

Gonzalez-Sanchez, Javier; Chavez-Echeagaray, Maria Elena; Atkinson, Robert K.; Burleson, Winslow

doi:10.1109/wicsa.2011.32

Cited by 37 publications

(33 citation statements)

References 12 publications

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“…Emotiv provides as direct output frustration and excitement in [11], long-term excitement, instantaneous excitement and engagement in [4,13] and excitement, engagement, boredom, meditation and frustration in [5] through its simple interface. In some cases information from the Emotiv sensor about the raw EEG and wave variations are combined with the information provided by other biological or non biological sensors, resulting in different types of emotion representation: anger, disgust, fear, happiness, sadness, joy, despisal, stress, concentration and excitement in [8], positive, negative and neutral state of mind in [3,20].…”

Section: Emotion Representationmentioning

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%

“…This is of interest because the sweat glands are controlled by the sympathetic nervous system, so skin conductance is used as an indication of psychological or physiological arousal. Examples can be found in [5,7,13,14].…”

Section: Multimodalitymentioning

confidence: 99%

“…In [5] "Tobii Eye Tracking System" was incorporated in the system, providing data about the user's attention and focus time while performing a task on the computer. This information is combined with the information provided by the EEG sensor.…”

Section: Multimodalitymentioning

confidence: 99%

“…Other inputs used to determine mental states can be mouse clicks, keyboard strokes, screen cam recordings [7], body posture, finger pressure (usually on the mouse) [5] and acoustic features [8].…”

Section: Multimodalitymentioning

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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