Implicit User-centric Personality Recognition Based on Physiological Responses to Emotional Videos

Wache, Julia; Subramanian, Ramanathan; Abadi, Mojtaba Khomami; Vieriu, Radu-Laurentiu; Sebe, Nicu

doi:10.1145/2818346.2820736

Cited by 32 publications

(3 citation statements)

References 25 publications

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“…Behavioral measures include observing the behavior of an individual while performing different types of activities like analysis of Facebook usage trends [27] and tweets [28], [29]. On the other hand, physiological signal-based personality trait recognition involves bio-markers like electroencephalography (EEG) [30], electrocardiography (ECG) [31], heart rate (HR) [32], and galvanic skin response (GSR) [18]. Compared to the explicit behavioral indicators, the inner emotions of a person measured by the use of biological and physiological markers can be deemed to be more effective in avoiding deceptiveness [33].…”

Section: Literature Reviewmentioning

confidence: 99%

PROPER: Personality Recognition based on Public Speaking using Electroencephalography Recordings

Majid,

Butt,

Nizami

et al. 2024

IEEE Access

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Personality trait recognition is an important psychological paradigm to understand the differences in people's behavior. This paper presents a new dataset, which we dubbed as PROPER (Personality Recognition based On Public Speaking using Electroencephalography Recordings) that connects the personality traits of an individual with public speaking activity via electroencephalography (EEG) signals. EEG data of 40 healthy individuals is recorded before, during, and after public speaking activity using Muse headband. A score from the Big Five Personality Trait questionnaire is used to label the participant's EEG data. A statistical analysis of EEG signals for each personality trait during different phases of the experiment is performed. The personality recognition process involves data acquisition, pre-processing, feature extraction and selection, and classification. Five feature groups are extracted from the frequency bands of EEG data of each channel. Feature selection is applied to the extracted features via the wrapper method. Support vector machine, the Naive Bayes, and multilayer perceptron (MLP) are used to classify the personality traits. An average F1-score of 0.95 for extroversion, 0.94 for openness to experience, 0.90 for conscientiousness, 0.84 for neuroticism, and 0.85 for agreeableness is achieved using the MLP classifier using pre-stimulus, during activity, and post-stimulus EEG data respectively.

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Section: Literature Reviewmentioning

confidence: 99%

PROPER: Personality Recognition based on Public Speaking using Electroencephalography Recordings

Majid,

Butt,

Nizami

et al. 2024

IEEE Access

View full text Add to dashboard Cite

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“…Implicit tagging has been defined as using non-verbal spontaneous behavioral responses to find relevant tags for multimedia content [40]. These responses include Electroencephalography (EEG) and Magnetoencephalography (MEG) along with physiological responses such as heart rate and body temperature [2,31,48,49,54] and gazing behavior [13,41,44,50]. Determining the affective dimensions of valence and arousal using these methods has been shown to be very effective.…”

Section: Human Centered Affect Recognitionmentioning

confidence: 99%

Looking Beyond a Clever Narrative

Shukla

Katti

Kankanhalli

et al. 2018

Proceedings of the 20th ACM International Conference on Multimodal Interaction

Self Cite

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Emotion evoked by an advertisement plays a key role in influencing brand recall and eventual consumer choices. Automatic ad affect recognition has several useful applications. However, the use of content-based feature representations does not give insights into how affect is modulated by aspects such as the ad scene setting, salient object attributes and their interactions. Neither do such approaches inform us on how humans prioritize visual information for ad understanding. Our work addresses these lacunae by decomposing video content into detected objects, coarse scene structure, object statistics and actively attended objects identified via eyegaze. We measure the importance of each of these information channels by systematically incorporating related information into ad affect prediction models. Contrary to the popular notion that ad affect hinges on the narrative and the clever use of linguistic and social cues, we find that actively attended objects and the coarse scene structure better encode affective information as compared to individual scene objects or conspicuous background elements.

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“…Several studies have appeared in this direction recently. For example, J. Wache et al [26] conducted an experiment based on physiological responses to automatically detect personality using the Big-Five model. Emotional clips were presented to participants who rated them in terms of valence and arousal.…”

Section: Introductionmentioning

confidence: 99%

Automatic Recognition of Personality Profiles Using EEG Functional Connectivity during Emotional Processing

et al. 2020

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Personality is the characteristic set of an individual’s behavioral and emotional patterns that evolve from biological and environmental factors. The recognition of personality profiles is crucial in making human–computer interaction (HCI) applications realistic, more focused, and user friendly. The ability to recognize personality using neuroscientific data underpins the neurobiological basis of personality. This paper aims to automatically recognize personality, combining scalp electroencephalogram (EEG) and machine learning techniques. As the resting state EEG has not so far been proven efficient for predicting personality, we used EEG recordings elicited during emotion processing. This study was based on data from the AMIGOS dataset reflecting the response of 37 healthy participants. Brain networks and graph theoretical parameters were extracted from cleaned EEG signals, while each trait score was dichotomized into low- and high-level using the k-means algorithm. A feature selection algorithm was used afterwards to reduce the feature-set size to the best 10 features to describe each trait separately. Support vector machines (SVM) were finally employed to classify each instance. Our method achieved a classification accuracy of 83.8% for extraversion, 86.5% for agreeableness, 83.8% for conscientiousness, 83.8% for neuroticism, and 73% for openness.

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Implicit User-centric Personality Recognition Based on Physiological Responses to Emotional Videos

Cited by 32 publications

References 25 publications

PROPER: Personality Recognition based on Public Speaking using Electroencephalography Recordings

PROPER: Personality Recognition based on Public Speaking using Electroencephalography Recordings

Looking Beyond a Clever Narrative

Automatic Recognition of Personality Profiles Using EEG Functional Connectivity during Emotional Processing

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