A Feature Extraction Method Based on Differential Entropy and Linear Discriminant Analysis for Emotion Recognition

Chen, Dong Wei; Miao, Rui; Yang, Wei; Liang, Yong; Chen, Hao Heng; Huang, Lan; Deng, Chun Jian; Han, Na

doi:10.3390/s19071631

Cited by 70 publications

(48 citation statements)

References 44 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We compute FD using the Higuchi algorithm for each of the 14 EEG signals (14 features). • DE can be defined as the entropy of continuous random variables and is used to measure its complexity [61]. DE is equivalent to the logarithm of the energy spectrum (ES) in a certain frequency band for a fixed length EEG sequence [62].…”

mentioning

confidence: 99%

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

Martinez-Tejada

Maruyama

Yoshimura

et al. 2020

MAKE

View full text Add to dashboard Cite

We analyzed the contribution of electroencephalogram (EEG) data, age, sex, and personality traits to emotion recognition processes—through the classification of arousal, valence, and discrete emotions labels—using feature selection techniques and machine learning classifiers. EEG traits and age, sex, and personality traits were retrieved from a well-known dataset—AMIGOS—and two sets of traits were built to analyze the classification performance. We found that age, sex, and personality traits were not significantly associated with the classification of arousal, valence and discrete emotions using machine learning. The added EEG features increased the classification accuracies (compared with the original report), for arousal and valence labels. Classification of arousal and valence labels achieved higher than chance levels; however, they did not exceed 70% accuracy in the different tested scenarios. For discrete emotions, the mean accuracies and the mean area under the curve scores were higher than chance; however, F1 scores were low, implying that several false positives and false negatives were present. This study highlights the performance of EEG traits, age, sex, and personality traits using emotion classifiers. These findings could help to understand the traits relationship in a technological and data level for personalized human-computer interactions systems.

show abstract

mentioning

confidence: 99%

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

Martinez-Tejada

Maruyama

Yoshimura

et al. 2020

MAKE

View full text Add to dashboard Cite

show abstract

“…Moreover, effect of culture on emotion recognition was investigated using EEG signals by presenting video clips in two different languages [ 72 ]. A feature extraction method was proposed to improve emotion recognition accuracy using EEG signals [ 73 ]. A quadratic time-frequency feature extraction scheme was proposed to recognize emotions using EEG signals [ 74 ].…”

Section: Related Workmentioning

confidence: 99%

Physiological Sensors Based Emotion Recognition While Experiencing Tactile Enhanced Multimedia

Raheel

Majid

Alnowami

et al. 2020

Sensors

View full text Add to dashboard Cite

Emotion recognition has increased the potential of affective computing by getting an instant feedback from users and thereby, have a better understanding of their behavior. Physiological sensors have been used to recognize human emotions in response to audio and video content that engages single (auditory) and multiple (two: auditory and vision) human senses, respectively. In this study, human emotions were recognized using physiological signals observed in response to tactile enhanced multimedia content that engages three (tactile, vision, and auditory) human senses. The aim was to give users an enhanced real-world sensation while engaging with multimedia content. To this end, four videos were selected and synchronized with an electric fan and a heater, based on timestamps within the scenes, to generate tactile enhanced content with cold and hot air effect respectively. Physiological signals, i.e., electroencephalography (EEG), photoplethysmography (PPG), and galvanic skin response (GSR) were recorded using commercially available sensors, while experiencing these tactile enhanced videos. The precision of the acquired physiological signals (including EEG, PPG, and GSR) is enhanced using pre-processing with a Savitzky-Golay smoothing filter. Frequency domain features (rational asymmetry, differential asymmetry, and correlation) from EEG, time domain features (variance, entropy, kurtosis, and skewness) from GSR, heart rate and heart rate variability from PPG data are extracted. The K nearest neighbor classifier is applied to the extracted features to classify four (happy, relaxed, angry, and sad) emotions. Our experimental results show that among individual modalities, PPG-based features gives the highest accuracy of 78.57 % as compared to EEG- and GSR-based features. The fusion of EEG, GSR, and PPG features further improved the classification accuracy to 79.76 % (for four emotions) when interacting with tactile enhanced multimedia.

show abstract

“…LDA is used for classification and for feature-extraction purposes. LDA has had many recent applications, such as extracting EEG and EMG signals [41], emotion recognition [42], and face recognition [43]. According to Idakwo et al [26], it is common for LDA to be ineffective when dealing with complex data structures.…”

Section: Literature Reviewmentioning

confidence: 99%

Feature Extraction Methods in Quantitative Structure–Activity Relationship Modeling: A Comparative Study

2020

View full text Add to dashboard Cite

Computational approaches for synthesizing new chemical compounds have resulted in a major explosion of chemical data in the field of drug discovery. The quantitative structure-activity relationship (QSAR) is a widely used classification and regression method used to represent the relationship between a chemical structure and its activities. This research focuses on the effect of dimensionality-reduction techniques on a high-dimensional QSAR dataset. Because of the multi-dimensional nature of QSAR, dimensionality-reduction techniques have become an integral part of its modeling process. Principal component analysis (PCA) is a feature extraction technique with several applications in exploratory data analysis, visualization and dimensionality reduction. However, linear PCA is inadequate to handle the complex structure of QSAR data. In light of the wide array of current feature-extraction techniques, we perform a comparative empirical study to investigate five feature-extraction techniques: PCA, kernel PCA, deep generalized autoencoder (dGAE), Gaussian random projection (GRP), and sparse random projection (SRP). The experiments are performed on a high-dimensional QSAR dataset, which comprises 6394 features. The transformed low-dimensional dataset is inputted into a deep learning classification model to predict a QSAR biological activity. Three approaches are adopted to validate and measure the proposed techniques: (i) comparing the performance of the classification models, (ii) visualizing the relationship (correlation) between features in the low-dimension Euclidean space, and (iii) validating the proposed techniques using an external dataset. To the best of our knowledge, this study is the first to investigate and compare the aforementioned feature-extraction techniques in QSAR modeling context. The results obtained provide invaluable insights regarding the behavior of different techniques with both negative and positive classes. With linear PCA as a baseline, we prove that the investigated techniques substantially outperform the baseline in multiple accuracy measures and demonstrate useful ways of extracting significant features. INDEX TERMS Autoencoder, blood-brain barrier (BBB) permeability, deep generalized autoencoder (dGAE), dimensioanlity reduction, feature extraction, Gaussian random projection, principal component analysis, quantitative structure-activity relation (QSAR), sparse random projection.

show abstract

A Feature Extraction Method Based on Differential Entropy and Linear Discriminant Analysis for Emotion Recognition

Cited by 70 publications

References 44 publications

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

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

Physiological Sensors Based Emotion Recognition While Experiencing Tactile Enhanced Multimedia

Feature Extraction Methods in Quantitative Structure–Activity Relationship Modeling: A Comparative Study

Contact Info

Product

Resources

About