EEG Emotion Classification Based On Baseline Strategy

Xu, Jinzhang; Ren, Fuji; Bao, Yanwei

doi:10.1109/ccis.2018.8691174

Cited by 11 publications

(4 citation statements)

References 16 publications

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“…As a nonlinear dynamics-based feature type, the SE feature [37] is well suited for studying EEG signals. PSD [38][39][40] features are extensively applied to study the power distribution of EEGs and provide effective value for EEG-based emotion recognition.…”

Section: Feature Extraction Methodsmentioning

confidence: 99%

MES-CTNet: A Novel Capsule Transformer Network Base on a Multi-Domain Feature Map for Electroencephalogram-Based Emotion Recognition

Du,

Ding,

et al. 2024

Brain Sciences

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Emotion recognition using the electroencephalogram (EEG) has garnered significant attention within the realm of human–computer interaction due to the wealth of genuine emotional data stored in EEG signals. However, traditional emotion recognition methods are deficient in mining the connection between multi-domain features and fitting their advantages. In this paper, we propose a novel capsule Transformer network based on a multi-domain feature for EEG-based emotion recognition, referred to as MES-CTNet. The model’s core consists of a multichannel capsule neural network(CapsNet) embedded with ECA (Efficient Channel Attention) and SE (Squeeze and Excitation) blocks and a Transformer-based temporal coding layer. Firstly, a multi-domain feature map is constructed by combining the space–frequency–time characteristics of the multi-domain features as inputs to the model. Then, the local emotion features are extracted from the multi-domain feature maps by the improved CapsNet. Finally, the Transformer-based temporal coding layer is utilized to globally perceive the emotion feature information of the continuous time slices to obtain a final emotion state. The paper fully experimented on two standard datasets with different emotion labels, the DEAP and SEED datasets. On the DEAP dataset, MES-CTNet achieved an average accuracy of 98.31% in the valence dimension and 98.28% in the arousal dimension; it achieved 94.91% for the cross-session task on the SEED dataset, demonstrating superior performance compared to traditional EEG emotion recognition methods. The MES-CTNet method, utilizing a multi-domain feature map as proposed herein, offers a broader observation perspective for EEG-based emotion recognition. It significantly enhances the classification recognition rate, thereby holding considerable theoretical and practical value in the EEG emotion recognition domain.

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Section: Feature Extraction Methodsmentioning

confidence: 99%

MES-CTNet: A Novel Capsule Transformer Network Base on a Multi-Domain Feature Map for Electroencephalogram-Based Emotion Recognition

Du,

Ding,

et al. 2024

Brain Sciences

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“…This approach also significantly increases the accuracy of recognizing 2 classes of emotions (arousal and valence) and 4 classes of emotions (high arousal positive valence; high arousal negative valence; low arousal negative valence; and low arousal positive valence) [46]. Other studies have also been proposed a correlation approach to determine the baseline signals that has a high correlation with the stimulus medium [47]. This approach can overcome cross-subject emotion recognition.…”

Section: Rq 2: How Can An Eeg Signal Be Generated With Consideration Of Differences In Participant Characteristics?mentioning

confidence: 99%

The challenges of emotion recognition methods based on electroencephalogram signals: a literature review

Wirawan

Wardoyo

Lelono

2022

IJECE

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Electroencephalogram (EEG) signals in recognizing emotions have several advantages. Still, the success of this study, however, is strongly influenced by: i) the distribution of the data used, ii) consider of differences in participant characteristics, and iii) consider the characteristics of the EEG signals. In response to these issues, this study will examine three important points that affect the success of emotion recognition packaged in several research questions: i) What factors need to be considered to generate and distribute EEG data?, ii) How can EEG signals be generated with consideration of differences in participant characteristics?, and iii) How do EEG signals with characteristics exist among its features for emotion recognition? The results, therefore, indicate some important challenges to be studied further in EEG signals-based emotion recognition research. These include i) determine robust methods for imbalanced EEG signals data, ii) determine the appropriate smoothing method to eliminate disturbances on the baseline signals, iii) determine the best baseline reduction methods to reduce the differences in the characteristics of the participants on the EEG signals, iv) determine the robust architecture of the capsule network method to overcome the loss of knowledge information and apply it in more diverse data set.

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“…Recent improvement in consumer-grade wearable EEG devices captures interest among the HCI community in using EEG for emotion classification [91]. Particularly focused on arousal and valence affective response, previous studies have used the EEG classification approach in the context of using music stimuli [66], music videos [6,16,83,96,100,101], and video clips [48,56,58,68,88,93]. The immersive virtual reality (VR)'s ability to evoke emotion gives VR more interest to be used as a tool in emotion detection in general [28,33,35,36,47,105].…”

Section: Emotion Detection Based On Bci-vrmentioning

confidence: 99%

Co-Design with Myself: A Brain-Computer Interface Design Tool that Predicts Live Emotion to Enhance Metacognitive Monitoring of Designers

Yang

Feng

Kalantari

2023

Preprint

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EEG Emotion Classification Based On Baseline Strategy

Cited by 11 publications

References 16 publications

MES-CTNet: A Novel Capsule Transformer Network Base on a Multi-Domain Feature Map for Electroencephalogram-Based Emotion Recognition

MES-CTNet: A Novel Capsule Transformer Network Base on a Multi-Domain Feature Map for Electroencephalogram-Based Emotion Recognition

The challenges of emotion recognition methods based on electroencephalogram signals: a literature review

Co-Design with Myself: A Brain-Computer Interface Design Tool that Predicts Live Emotion to Enhance Metacognitive Monitoring of Designers

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