Electroencephalograph (EEG) Based Emotion Recognition System: A Review

Wagh, Kalyani P.; Vasanth, K.

doi:10.1007/978-981-10-8204-7_5

Cited by 28 publications

(22 citation statements)

References 41 publications

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“…For Experiment II, the accuracy of the four-class emotion recognition using the MLDW-PSO algorithm reached 76.67%, which is higher than the latest results reported in the review [32]. Previously, Chen et al [33] proposed a three-stage decision framework for recognizing four emotions of multiple subjects, and found that the classification accuracy for the same four emotions was 70.04%.…”

Section: Discussionmentioning

confidence: 61%

Enhancing BCI-Based Emotion Recognition Using an Improved Particle Swarm Optimization for Feature Selection

Qiu

et al. 2020

Sensors

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Electroencephalogram (EEG) signals have been widely used in emotion recognition. However, the current EEG-based emotion recognition has low accuracy of emotion classification, and its real-time application is limited. In order to address these issues, in this paper, we proposed an improved feature selection algorithm to recognize subjects’ emotion states based on EEG signal, and combined this feature selection method to design an online emotion recognition brain-computer interface (BCI) system. Specifically, first, different dimensional features from the time-domain, frequency domain, and time-frequency domain were extracted. Then, a modified particle swarm optimization (PSO) method with multi-stage linearly-decreasing inertia weight (MLDW) was purposed for feature selection. The MLDW algorithm can be used to easily refine the process of decreasing the inertia weight. Finally, the emotion types were classified by the support vector machine classifier. We extracted different features from the EEG data in the DEAP data set collected by 32 subjects to perform two offline experiments. Our results showed that the average accuracy of four-class emotion recognition reached 76.67%. Compared with the latest benchmark, our proposed MLDW-PSO feature selection improves the accuracy of EEG-based emotion recognition. To further validate the efficiency of the MLDW-PSO feature selection method, we developed an online two-class emotion recognition system evoked by Chinese videos, which achieved good performance for 10 healthy subjects with an average accuracy of 89.5%. The effectiveness of our method was thus demonstrated.

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Section: Discussionmentioning

confidence: 61%

Enhancing BCI-Based Emotion Recognition Using an Improved Particle Swarm Optimization for Feature Selection

Qiu

et al. 2020

Sensors

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“…40,41,42 This has led to an outpouring of studies that use machine learning algorithms to analyze brain signal data for emotion and attention recognition goals. 43,44,45 Verifying the reliability of decoder algorithms that classify emotions, attention, valence, arousal, stress and other attributes of human experience at a high temporal resolution has remained a persistent challenge. There is no consensus today about where the information boundary exists in noninvasively measurable brain signals which are known to change at the order of milliseconds and contain a wide variety of meaningful data.…”

Section: B Attention and Emotion Decoding From Brain Signalmentioning

confidence: 99%

Modeling The Effect of Background Sounds on Human Focus Using Brain Decoding Technology

Haruvi

Kopito

Brande-Eilat

et al. 2021

Preprint

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The goal of this study was to learn what properties of sound affect human focus the most. Participants (N=62, 18-65y) performed various tasks while listening to either no background sound (silence), popular music playlists for increasing focus (pre-recorded songs), or personalized soundscapes (audio composed in real-time to increase a specific individual's focus). While performing tasks on a tablet, participants wore headphones and brain signals were recorded using a portable electroencephalography headband. Participants completed four one-hour long sessions, each with different audio content, at home. We successfully generated brain-based models to predict individual participant focus levels over time and used these models to analyze the effects of various audio content during different tasks. We found that while participants were working, personalized soundscapes increased their focus significantly above silence (p=0.008), while music playlists did not have a significant effect. For the young adult demographic (18-36y), silence was significantly less effective at producing focus than audio content of any type tested (p=0.001-0.009). Personalized soundscapes enhanced focus the most relative to silence, but professionally crafted playlists of pre-recorded songs also increased focus during specific time intervals, especially for the youngest audience demographic. We also found that focus levels can be predicted from physical properties of sound, enabling human and artificial intelligence composers to test and refine audio to produce increases or decreases in listener focus with high temporal (millisecond) precision. Future research includes real-time adjustment of sound for other functional objectives, such as affecting listener enjoyment, calm, or memory.

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“…Support vectors represent the data points, which exist on the margin. SVM employs a kernel function to convert the feature space into a new domain to make the separation between the two classes of the datasets easier (Wagh & Vasanth, 2019). In this paper, the cubic kernel function is chosen as it achieved the highest results.…”

Section: Classification Stepmentioning

confidence: 99%

FUSI-CAD: Coronavirus (COVID-19) diagnosis based on the fusion of CNNs and handcrafted features

Ragab¹,

Attallah²

2020

PeerJ Computer Science

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The precise and rapid diagnosis of coronavirus (COVID-19) at the very primary stage helps doctors to manage patients in high workload conditions. In addition, it prevents the spread of this pandemic virus. Computer-aided diagnosis (CAD) based on artificial intelligence (AI) techniques can be used to distinguish between COVID-19 and non-COVID-19 from the computed tomography (CT) imaging. Furthermore, the CAD systems are capable of delivering an accurate faster COVID-19 diagnosis, which consequently saves time for the disease control and provides an efficient diagnosis compared to laboratory tests. In this study, a novel CAD system called FUSI-CAD based on AI techniques is proposed. Almost all the methods in the literature are based on individual convolutional neural networks (CNN). Consequently, the FUSI-CAD system is based on the fusion of multiple different CNN architectures with three handcrafted features including statistical features and textural analysis features such as discrete wavelet transform (DWT), and the grey level co-occurrence matrix (GLCM) which were not previously utilized in coronavirus diagnosis. The SARS-CoV-2 CT-scan dataset is used to test the performance of the proposed FUSI-CAD. The results show that the proposed system could accurately differentiate between COVID-19 and non-COVID-19 images, as the accuracy achieved is 99%. Additionally, the system proved to be reliable as well. This is because the sensitivity, specificity, and precision attained to 99%. In addition, the diagnostics odds ratio (DOR) is ≥ 100. Furthermore, the results are compared with recent related studies based on the same dataset. The comparison verifies the competence of the proposed FUSI-CAD over the other related CAD systems. Thus, the novel FUSI-CAD system can be employed in real diagnostic scenarios for achieving accurate testing for COVID-19 and avoiding human misdiagnosis that might exist due to human fatigue. It can also reduce the time and exertion made by the radiologists during the examination process.

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Electroencephalograph (EEG) Based Emotion Recognition System: A Review

Cited by 28 publications

References 41 publications

Enhancing BCI-Based Emotion Recognition Using an Improved Particle Swarm Optimization for Feature Selection

Enhancing BCI-Based Emotion Recognition Using an Improved Particle Swarm Optimization for Feature Selection

Modeling The Effect of Background Sounds on Human Focus Using Brain Decoding Technology

FUSI-CAD: Coronavirus (COVID-19) diagnosis based on the fusion of CNNs and handcrafted features

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