Analysis of Personality and EEG Features in Emotion Recognition Using Machine Learning Techniques to Classify Arousal and Valence Labels

Martinez-Tejada, Laura Alejandra; Maruyama, Yasuhisa; Yoshimura, Natsue; Koike, Yasuharu

doi:10.3390/make2020007

Cited by 15 publications

(15 citation statements)

References 60 publications

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“…This result is in line with literature on the positive relationship between higher frequency oscillations and neuroticism [45][46][47] and locus of control and stress [48]. Again, such correlations can easily lead to unproven speculations about self-rated traits and EEG oscillatory activity which [49,50].…”

Section: Discussionsupporting

confidence: 89%

Physiological Measurements in Social Acceptance of Self Driving Technologies

Palatinus

Volosin

Csábi

et al. 2021

Preprint

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The goal of the present study is to examine the cognitive/physiological correlates of passenger travel experience in autonomously driven transportation systems. We investigated the social acceptance and cognitive aspects of self-driving technology by measuring physiological responses in real-world experimental settings using eye-tracking and EEG measures simultaneously on 17 volunteers. A typical test run included human-driven and autonomous conditions in the same vehicle, in a safe environment. In the spectrum analysis of the eye-tracking data we found significant differences in the complex patterns of eye movements: the structure of movements of different magnitudes were less variable in the autonomous drive condition. EEG data revealed less positive affectivity and slightly higher arousal in the autonomous condition compared to the human-driven condition. Correlates with personality traits are also discussed. These preliminary findings reinforced our initial hypothesis that passenger experience in human and machine navigated conditions entail different physiological and psychological correlates, and those differences are accessible using state of the art in-world measurements. These useful dimensions of passenger experience may serve as a source of information both for the improvement and design of self-navigating technology and for market-related concerns.

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Section: Discussionsupporting

confidence: 89%

Physiological Measurements in Social Acceptance of Self Driving Technologies

Palatinus

Volosin

Csábi

et al. 2021

Preprint

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“…One convolution kernel can convolve the images of multiple adjacent frames at a same time, and the values in the feature images are convolutions of the same position of each image in the previous layer. Except for this aspect, the principles of the 3D convolutional neural network are the same with those of the 2D convolutional neural network [31]. During English learning, the expressions captured by the machine are single-frame static expressions, which can be analyzed by the convolutional neural network.…”

Section: Human-computer Interaction Control Methods Based On Emotion Recognitionmentioning

confidence: 99%

Machine Learning-Based Student Emotion Recognition for Business English Class

Cui

Wang

Zhao

2021

Int. J. Emerg. Technol. Learn.

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Traditional English teaching model neglects student emotions, making many tired of learning. Machine learning supports end-to-end recognition of learning emotions, such that the recognition system can adaptively adjust the learning difficulty in English classroom. With the help of machine learning, this paper presents a method to extract the facial expression features of students in business English class, and establishes a student emotion recognition model, which consists of such modules as emotion mechanism, signal acquisition, analysis and recognition, emotion understanding, emotion expression, and wearable equipment. The results show that the proposed emotion recognition model monitors the real-time emotional states of each student during English learning; upon detecting frustration or boredom, machine learning will timely switch to the contents that interest the student or easier to learn, keeping the student active in learning. The research provides an end-to-end student emotion recognition system to assist with classroom teaching, and enhance the positive emotions of students in English learning.

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“…Research with neurophysiological sensors [ 12 , 13 ] has begun to aid in monitoring schizophrenia [ 14 , 15 ], Parkinson’s’ disease [ 16 ], traumatic brain injury [ 17 ], physiological signals [ 18 ], cognitive function [ 19 ], epileptic seizures [ 20 ], alcoholism [ 21 ], brain tumors [ 22 ], brain cancer [ 23 ], mental stability [ 24 ], personality [ 25 ], eye tracking [ 26 ], and many other phenomena. Moreover, studies conducted on EEG data processing have identified human emotions with exceptional accuracy [ 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 ].…”

Section: Literature Reviewmentioning

confidence: 99%

M1M2: Deep-Learning-Based Real-Time Emotion Recognition from Neural Activity

Akter

Prodhan

Pias

et al. 2022

Sensors

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Emotion recognition, or the ability of computers to interpret people’s emotional states, is a very active research area with vast applications to improve people’s lives. However, most image-based emotion recognition techniques are flawed, as humans can intentionally hide their emotions by changing facial expressions. Consequently, brain signals are being used to detect human emotions with improved accuracy, but most proposed systems demonstrate poor performance as EEG signals are difficult to classify using standard machine learning and deep learning techniques. This paper proposes two convolutional neural network (CNN) models (M1: heavily parameterized CNN model and M2: lightly parameterized CNN model) coupled with elegant feature extraction methods for effective recognition. In this study, the most popular EEG benchmark dataset, the DEAP, is utilized with two of its labels, valence, and arousal, for binary classification. We use Fast Fourier Transformation to extract the frequency domain features, convolutional layers for deep features, and complementary features to represent the dataset. The M1 and M2 CNN models achieve nearly perfect accuracy of 99.89% and 99.22%, respectively, which outperform every previous state-of-the-art model. We empirically demonstrate that the M2 model requires only 2 seconds of EEG signal for 99.22% accuracy, and it can achieve over 96% accuracy with only 125 milliseconds of EEG data for valence classification. Moreover, the proposed M2 model achieves 96.8% accuracy on valence using only 10% of the training dataset, demonstrating our proposed system’s effectiveness. Documented implementation codes for every experiment are published for reproducibility.

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Analysis of Personality and EEG Features in Emotion Recognition Using Machine Learning Techniques to Classify Arousal and Valence Labels

Cited by 15 publications

References 60 publications

Physiological Measurements in Social Acceptance of Self Driving Technologies

Physiological Measurements in Social Acceptance of Self Driving Technologies

Machine Learning-Based Student Emotion Recognition for Business English Class

M1M2: Deep-Learning-Based Real-Time Emotion Recognition from Neural Activity

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