Employing PCA and t-statistical approach for feature extraction and classification of emotion from multichannel EEG signal

Rahman, Md. Asadur; Hossain, Md. Foisal; Hossain, Mazhar; Ahmmed, Rasel

doi:10.1016/j.eij.2019.10.002

Cited by 115 publications

(42 citation statements)

References 27 publications

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“…These measures are based on the statistics of Hjorth mobility. Moreover, a novel hybrid feature extraction technique has been proposed (Asadur Rahman et al, 2019), which consists of PCA and t-statistics. In Guede-Fernández et al (2019), the analysis of respiratory rate variability of EEG has been utilized to monitor the state of drowsiness in a driver.…”

Section: Feature Extraction Approaches In Eeg-based Bci Systemsmentioning

confidence: 99%

Current Status, Challenges, and Possible Solutions of EEG-Based Brain-Computer Interface: A Comprehensive Review

et al. 2020

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Brain-Computer Interface (BCI), in essence, aims at controlling different assistive devices through the utilization of brain waves. It is worth noting that the application of BCI is not limited to medical applications, and hence, the research in this field has gained due attention. Moreover, the significant number of related publications over the past two decades further indicates the consistent improvements and breakthroughs that have been made in this particular field. Nonetheless, it is also worth mentioning that with these improvements, new challenges are constantly discovered. This article provides a comprehensive review of the state-of-the-art of a complete BCI system. First, a brief overview of electroencephalogram (EEG)-based BCI systems is given. Secondly, a considerable number of popular BCI applications are reviewed in terms of electrophysiological control signals, feature extraction, classification algorithms, and performance evaluation metrics. Finally, the challenges to the recent BCI systems are discussed, and possible solutions to mitigate the issues are recommended.

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Section: Feature Extraction Approaches In Eeg-based Bci Systemsmentioning

confidence: 99%

Current Status, Challenges, and Possible Solutions of EEG-Based Brain-Computer Interface: A Comprehensive Review

et al. 2020

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“…The activity, mobility, and complexity represent the signal power, mean frequency, and frequency change, respectively. Autoregression [129]- [131], PCA [132], [133], ICA, etc. methods are also used for extracting a feature from the EEG signal to recognize emotion.…”

Section: ) Hjorth Parametermentioning

confidence: 99%

Emotion Recognition From EEG Signal Focusing on Deep Learning and Shallow Learning Techniques

et al. 2021

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Recently, electroencephalogram-based emotion recognition has become crucial in enabling the Human-Computer Interaction (HCI) system to become more intelligent. Due to the outstanding applications of emotion recognition, e.g., person-based decision making, mind-machine interfacing, cognitive interaction, affect detection, feeling detection, etc., emotion recognition has become successful in attracting the recent hype of AI-empowered research. Therefore, numerous studies have been conducted driven by a range of approaches, which demand a systematic review of methodologies used for this task with their feature sets and techniques. It will facilitate the beginners as guidance towards composing an effective emotion recognition system. In this article, we have conducted a rigorous review on the state-ofthe-art emotion recognition systems, published in recent literature, and summarized some of the common emotion recognition steps with relevant definitions, theories, and analyses to provide key knowledge to develop a proper framework. Moreover, studies included here were dichotomized based on two categories: i) deep learning-based, and ii) shallow machine learning-based emotion recognition systems. The reviewed systems were compared based on methods, classifier, the number of classified emotions, accuracy, and dataset used. An informative comparison, recent research trends, and some recommendations are also provided for future research directions.

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“…The accuracy of the two kinds of emotion recognition was 85.11% when tested on the SEED dataset. Rahman et al (2020) first used PCA that can reduce the dimension of EEG signal, then extracted the standard deviation, mean absolute deviation, and power spectral density of EEG signal as classification features, and used t-statistic to select distinctive features. Finally, SVM and other classifiers were used to recognize emotions on the SEED dataset and the accuracy reached 84.3%.…”

Section: Emotion Recognition Based On Database For Emotion Analysis Using Physiological Signalsmentioning

confidence: 99%

Review on Emotion Recognition Based on Electroencephalography

Liu

Zhang

et al. 2021

Front. Comput. Neurosci.

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Emotions are closely related to human behavior, family, and society. Changes in emotions can cause differences in electroencephalography (EEG) signals, which show different emotional states and are not easy to disguise. EEG-based emotion recognition has been widely used in human-computer interaction, medical diagnosis, military, and other fields. In this paper, we describe the common steps of an emotion recognition algorithm based on EEG from data acquisition, preprocessing, feature extraction, feature selection to classifier. Then, we review the existing EEG-based emotional recognition methods, as well as assess their classification effect. This paper will help researchers quickly understand the basic theory of emotion recognition and provide references for the future development of EEG. Moreover, emotion is an important representation of safety psychology.

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Employing PCA and t-statistical approach for feature extraction and classification of emotion from multichannel EEG signal

Cited by 115 publications

References 27 publications

Current Status, Challenges, and Possible Solutions of EEG-Based Brain-Computer Interface: A Comprehensive Review

Current Status, Challenges, and Possible Solutions of EEG-Based Brain-Computer Interface: A Comprehensive Review

Emotion Recognition From EEG Signal Focusing on Deep Learning and Shallow Learning Techniques

Review on Emotion Recognition Based on Electroencephalography

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