Enhancing Performance of EEG-based Emotion Recognition Systems Using Feature Smoothing

Pham, Trung Duy; Tran, Dat; Ma, Wanli; Tran, Nga

doi:10.1007/978-3-319-26561-2_12

Cited by 16 publications

(4 citation statements)

References 16 publications

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“…There is another previous work [108], which presents the same hypothesis as ours, using pleasantness emotion in the case of odor-induced EEG signal measurement.…”

Section: Discussionmentioning

confidence: 70%

EEG-Based Empathic Safe Cobot

2022

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An empathic collaborative robot (cobot) was realized through the transmission of fear from a human agent to a robot agent. Such empathy was induced through an electroencephalographic (EEG) sensor worn by the human agent, thus realizing an empathic safe brain-computer interface (BCI). The empathic safe cobot reacts to the fear and in turn transmits it to the human agent, forming a social circle of empathy and safety. A first randomized, controlled experiment involved two groups of 50 healthy subjects (100 total subjects) to measure the EEG signal in the presence or absence of a frightening event. The second randomized, controlled experiment on two groups of 50 different healthy subjects (100 total subjects) exposed the subjects to comfortable and uncomfortable movements of a collaborative robot (cobot) while the subjects’ EEG signal was acquired. The result was that a spike in the subject’s EEG signal was observed in the presence of uncomfortable movement. The questionnaires were distributed to the subjects, and confirmed the results of the EEG signal measurement. In a controlled laboratory setting, all experiments were found to be statistically significant. In the first experiment, the peak EEG signal measured just after the activating event was greater than the resting EEG signal (p < 10−3). In the second experiment, the peak EEG signal measured just after the uncomfortable movement of the cobot was greater than the EEG signal measured under conditions of comfortable movement of the cobot (p < 10−3). In conclusion, within the isolated and constrained experimental environment, the results were satisfactory.

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“…There is another previous work [108], which presents the same hypothesis as ours, using pleasantness emotion in the case of odor-induced EEG signal measurement.…”

Section: Discussionmentioning

confidence: 70%

EEG-Based Empathic Safe Cobot

2022

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“…Pham et al ( 2015 ) proposed the importance of feature smoothing for emotional EEG classification. The denoised EEG signal is converted into the frequency domain signal by the fast Fourier transform.…”

Section: Methodsmentioning

confidence: 99%

SparNet: A Convolutional Neural Network for EEG Space-Frequency Feature Learning and Depression Discrimination

Deng

Fan

et al. 2022

Front. Neuroinform.

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Depression affects many people around the world today and is considered a global problem. Electroencephalogram (EEG) measurement is an appropriate way to understand the underlying mechanisms of major depressive disorder (MDD) to distinguish depression from normal control. With the development of deep learning methods, many researchers have adopted deep learning models to improve the classification accuracy of depression recognition. However, there are few studies on designing convolution filters for spatial and frequency domain feature learning in different brain regions. In this study, SparNet, a convolutional neural network composed of five parallel convolutional filters and the SENet, is proposed to learn EEG space-frequency domain characteristics and distinguish between depressive and normal control. The model is trained and tested by the cross-validation method of subject division. The results show that SparNet achieves a sensitivity of 95.07%, a specificity of 93.66%, and an accuracy of 94.37% in classification. Therefore, our results can conclude that the proposed SparNet model is effective in detecting depression using EEG signals. It also indicates that the combination of spatial information and frequency domain information is an effective way to identify patients with depression.

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“…The most common signal that shows the brain’s electrical activity is the electroencephalogram (EEG), which is widely used in extracting and analyzing brain system information due to its non-invasiveness, easy recording, and very high temporal resolution (Ebrahimzadeh et al, 2019a , 2021b ; Zhong et al, 2020 ; Sadjadi et al, 2021 ). The EEG signal actually measures the brain’s activity, which is responsible for regulating and controlling emotions (Soroush et al, 2020 ), so emotion recognition systems based on EEG signals have been favored by researchers (Takahashi, 2004 ; Bos, 2006 ; Petrantonakis and Hadjileontiadis, 2011 ; Bajaj and Pachori, 2015 ; Pham et al, 2015 ; Singh and Singh, 2017 ).…”

Section: Introductionmentioning

confidence: 99%

A Spherical Phase Space Partitioning Based Symbolic Time Series Analysis (SPSP—STSA) for Emotion Recognition Using EEG Signals

Tavakkoli

Nasrabadi

2022

Front. Hum. Neurosci.

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Emotion recognition systems have been of interest to researchers for a long time. Improvement of brain-computer interface systems currently makes EEG-based emotion recognition more attractive. These systems try to develop strategies that are capable of recognizing emotions automatically. There are many approaches due to different features extractions methods for analyzing the EEG signals. Still, Since the brain is supposed to be a nonlinear dynamic system, it seems a nonlinear dynamic analysis tool may yield more convenient results. A novel approach in Symbolic Time Series Analysis (STSA) for signal phase space partitioning and symbol sequence generating is introduced in this study. Symbolic sequences have been produced by means of spherical partitioning of phase space; then, they have been compared and classified based on the maximum value of a similarity index. Obtaining the automatic independent emotion recognition EEG-based system has always been discussed because of the subject-dependent content of emotion. Here we introduce a subject-independent protocol to solve the generalization problem. To prove our method’s effectiveness, we used the DEAP dataset, and we reached an accuracy of 98.44% for classifying happiness from sadness (two- emotion groups). It was 93.75% for three (happiness, sadness, and joy), 89.06% for four (happiness, sadness, joy, and terrible), and 85% for five emotional groups (happiness, sadness, joy, terrible and mellow). According to these results, it is evident that our subject-independent method is more accurate rather than many other methods in different studies. In addition, a subject-independent method has been proposed in this study, which is not considered in most of the studies in this field.

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Enhancing Performance of EEG-based Emotion Recognition Systems Using Feature Smoothing

Cited by 16 publications

References 16 publications

EEG-Based Empathic Safe Cobot

EEG-Based Empathic Safe Cobot

SparNet: A Convolutional Neural Network for EEG Space-Frequency Feature Learning and Depression Discrimination

A Spherical Phase Space Partitioning Based Symbolic Time Series Analysis (SPSP—STSA) for Emotion Recognition Using EEG Signals

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