EEG based personality prediction using genetic programming

Bhardwaj, Harshit; Tomar, Pradeep; Sakalle, Aditi; Bhardwaj, Arpit; Asthana, Rishi; Vidyarthi, Ankit

doi:10.1002/asjc.3019

Cited by 6 publications

(4 citation statements)

References 34 publications

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“…The findings suggest that individuals who exhibit low levels of extroversion tend to gravitate towards games that are relatively slower-paced and strategic, as opposed to those who are highly extroverted. An EEG-based personality recognition in response to video clips using genetic programming proposed in [65] reported the highest classification accuracy of 84.34%. A Deep Long Short-Term Memory model has been employed for personality trait classification in a study conducted in [66].…”

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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“…• brain research (Bhardwaj, Tomar, Sakalle, Bhardwaj, Asthana, & Vidyarthi, 2023;Venurkar, Srivastava, Shukla, Acharya, Saha, & Deshpande, 2022) • gaming (Bocci, Ferrari, & Sarini, 2023;Hernandez, Muratet, Pierotti, & Carron, 2022) .…”

Section: • Perceiving Informationmentioning

confidence: 99%

“…• language processing (López-Santillán, González, Montes-y-Gómez, & López-Monroy, 2023; Ryan, Katarina, & Suhartono, 2023) and • predictive modeling (Bhardwaj, Tomar, Sakalle, Bhardwaj, Asthana, & Vidyarthi, 2023;Cristescu, Ciupercă, & Cîrnu, 2022;Naseri & Momtazi, 2023;Pavan Kumar & Gavrilova, 2022;Sánchez-Ferná ndez, Baca Ruiz, & Pegalajar Jiménez, 2023;Wang, Ye, Lv, Gong, Lu, & Wang, 2023).…”

Section: • Perceiving Informationmentioning

confidence: 99%

The Association of Personality Characteristics with Learning Strategy Preferences

Conti,

McNeil

2024

JEL

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The purpose of this study was to describe the association between the learning strategy preference of the learners as identified by Assessing The Learning Strategies of AdultS (ATLAS) and the individual personality traits as defined by the Myers-Briggs Type Indicator (MBTI). The sample was 553 adults in Canada and the United States. Two types of analyses were used to investigate the association between learning strategy preferences and personality traits. First, discriminant analysis explored the interaction of personality traits with the learning strategy preference. Second, analysis of variance measured the association of each personality trait with the learning-strategy-preference groups separately. The findings provided several explicit personality traits associated with each learning-strategy-preference group. These findings support the conclusion that a strong association exists between personality traits and learning-strategy-preference characteristics. Learning strategy preferences and personality traits complement each other. Each clarifies and enriches the other. As a result, teachers have two indicators that can help them personalize the teaching-learning environment for each student. Teachers can use the learning-strategy-group descriptions as guides for organizing each learner's instructional activities and plans and as a cognitive framework for uncovering and monitoring student behaviors and alerting teachers to potential learning difficulties for some students. Students can apply the descriptions of the learning-strategy-preference groups to facilitate self-assessment and metacognition. Theory can be enhanced by considering the two concepts of learning strategy preferences and personality traits coupled and by conducting quantitative and qualitative research to test and expand the generalizability of the learning-strategy-group descriptions. (Permission is granted to use Assessing The Learning Strategies of AdultS and the Personality Identity Estimator in practice and research. Links to printable copies and online completion are appended.)

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“…Its core concept is respecting children and families, transmitting health information, respecting family members' choices, emphasizing collaboration among children, families, and caregivers, and giving them strength and support [14,15]. Family-centered nursing can make clinical nursing staff realize that only by involving family members in the nursing process can children receive good nursing, and a friendly relationship of mutual respect and support between the medical staff and children's families is established [16,18]. In recent years, the adoption of this nursing model in pediatric emergencies, newborns, asthma, diabetes, and children's health education has played a very substantial effect.…”

Section: Introductionmentioning

confidence: 99%

Nursing Intervention of Children’s Lower Limb Chronic Wound Healing Under Artificial Intelligence

Hao,

Sun

2023

IEEE Access

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This work aimed to compare the effect of family-centered nursing and routine nursing on the treatment of children with chronic lower limb wounds. A total of 112 children with chronic wounds of the lower limbs were divided into a test group (56 cases) and a control group (56 cases). The children in the test group were given a family-centered nursing intervention, while those in the control group were given a routine nursing intervention. Additionally, an image segmentation algorithm based on a deep convolutional neural network was proposed to segment chronic wound images. The results showed that there were no significant differences in sex, injury site (left or right limb), age, height, weight, and cause of disease (scald, blow, fall) between the test group and the control group (P > 0.05). The wound pain score of the test group was 3.38 ± 0.75 and that of the control group was 6.24 ± 1.48. Compared with the control group, the wound pain score of the test group was lower (P < 0.05). The total effective rate of the test group (92.86%) was significantly higher than that of the control group (78.57%) (P < 0.05). The accuracy of the DCNN model in both the training and test datasets was significantly higher than that of the FCM model (P < 0.05). The results showed that, compared with routine nursing, family-centered nursing intervention had a more significant effect on children with chronic wounds of the lower extremities and had clinical promotion value.INDEX TERMS Deep convolutional neural networks; family-centered nursing; chronic wounds in children's lower limbs; image segmentation; wound pain

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EEG based personality prediction using genetic programming

Cited by 6 publications

References 34 publications

PROPER: Personality Recognition based on Public Speaking using Electroencephalography Recordings

PROPER: Personality Recognition based on Public Speaking using Electroencephalography Recordings

The Association of Personality Characteristics with Learning Strategy Preferences

Nursing Intervention of Children’s Lower Limb Chronic Wound Healing Under Artificial Intelligence

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