EEG emotion recognition using improved graph neural network with channel selection

Lin, Xuefen; Chen, Jielin; Ma, Wenxue; Tang, Wei; Wang, Yuchen

doi:10.1016/j.cmpb.2023.107380

Cited by 46 publications

(14 citation statements)

References 31 publications

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“…However, only 0–1 channel was filtered out among 44.44% of participants. Some researchers have presented an iterative channel selection graph neural network (CSGNN) for recognizing emotions [ 56 ]. This method focuses on selecting channels based on brain regions, and the results showed that the decoding accuracy decreased when the channels from different brain regions were reduced progressively by 20%.…”

Section: Discussionmentioning

confidence: 99%

Sparse Logistic Regression-Based EEG Channel Optimization Algorithm for Improved Universality across Participants

Shi

Koike

2023

Bioengineering

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Electroencephalogram (EEG) channel optimization can reduce redundant information and improve EEG decoding accuracy by selecting the most informative channels. This article aims to investigate the universality regarding EEG channel optimization in terms of how well the selected EEG channels can be generalized to different participants. In particular, this study proposes a sparse logistic regression (SLR)-based EEG channel optimization algorithm using a non-zero model parameter ranking method. The proposed channel optimization algorithm was evaluated in both individual analysis and group analysis using the raw EEG data, compared with the conventional channel selection method based on the correlation coefficients (CCS). The experimental results demonstrate that the SLR-based EEG channel optimization algorithm not only filters out most redundant channels (filters 75–96.9% of channels) with a 1.65–5.1% increase in decoding accuracy, but it can also achieve a satisfactory level of decoding accuracy in the group analysis by employing only a few (2–15) common EEG electrodes, even for different participants. The proposed channel optimization algorithm can realize better universality for EEG decoding, which can reduce the burden of EEG data acquisition and enhance the real-world application of EEG-based brain–computer interface (BCI).

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Section: Discussionmentioning

confidence: 99%

Sparse Logistic Regression-Based EEG Channel Optimization Algorithm for Improved Universality across Participants

Shi

Koike

2023

Bioengineering

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“…An improved graph convolution model with dynamic channel selection by Lin et al [45] has been suggested. The proposed model combines the advantages of 1D Conv and graph convolution to capture intra-and inter-channel EEG features.…”

Section: Related Studiesmentioning

confidence: 99%

Developing an EEG-Based Emotion Recognition Using Ensemble Deep Learning Methods and Fusion of Brain Effective Connectivity Maps

Bagherzadeh,

Shalbaf,

Shoeibi

et al. 2024

IEEE Access

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The objective of this paper is to develop a novel emotion recognition system from electroencephalogram (EEG) signals using effective connectivity and deep learning methods. Emotion recognition is an important task for various applications such as human-computer interaction and, mental health diagnosis. The paper aims to improve the accuracy and robustness of emotion recognition by combining different effective connectivity (EC) methods and pre-trained convolutional neural networks (CNNs), as well as long short-term memory (LSTM). EC methods measure information flow in the brain during emotional states using EEG signals. We used three EC methods: transfer entropy (TE), partial directed coherence (PDC), and direct directed transfer function (dDTF). We estimated a fused image from these methods for each five-second window of 32-channel EEG signals. Then, we applied six pre-trained CNNs to classify the images into four emotion classes based on the two-dimensional valence-arousal model. We used the leave-one-subject-out crossvalidation strategy to evaluate the classification results. We also used an ensemble model to select the best results from the best pre-trained CNNs using the majority voting approach. Moreover, we combined the CNNs with LSTM to improve recognition performance. We achieved the average accuracy and F-score of 98.76%, 98.86%, 98.66 and 98.88% for classifying emotions using DEAP and MAHNOB-HCI datasets, respectively. Our results show that fused images can increase the accuracy and that an ensemble and combination of pretrained CNNs and LSTM can achieve high accuracy for automated emotion recognition. Our model outperformed other state-of-the-art systems using the same datasets for four-class emotion classification.

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“…BCIs are employed to classify and interpret human emotional states based on brain activity patterns [93][94][95][96][97][98]. In this context, Teo and Chia [99] worked on EEG data, preprocessed, and segmented into epochs, which were then used to extract spectral features using the Fast Fourier Transform (FFT) algorithm.…”

Section: Emotion Classificationmentioning

confidence: 99%

A Comprehensive Survey of Brain-Computer Interface Technology in Healthcare: Research Perspectives

Vimal Cruz,

Jamal,

Sethuraman

2024

Preprint

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The Brain-Computer Interface (BCI) technology has emerged as a groundbreaking innovation with profound implications across diverse domains, particularly in healthcare. By establishing a direct communication pathway between the human brain and external devices, BCI systems offer unprecedented opportunities for diagnosis, treatment, and rehabilitation, thereby reshaping the landscape of medical practice. However, despite its immense potential, the widespread adoption of BCI technology in clinical settings faces several challenges. These include the need for robust signal acquisition and processing techniques, ensuring user safety and privacy, addressing ethical considerations, and optimizing user training and adaptation. Overcoming these challenges is crucial to unleashing the complete potential of BCI technology in healthcare and realizing its promise of personalized, patient-centric care. This review work underscores the transformative potential of BCI technology in revolutionizing medical practice. This paper offers a comprehensive analysis of medical-oriented BCI applications by exploring the various uses of BCI technology and its potential to transform patient care.

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EEG emotion recognition using improved graph neural network with channel selection

Cited by 46 publications

References 31 publications

Sparse Logistic Regression-Based EEG Channel Optimization Algorithm for Improved Universality across Participants

Sparse Logistic Regression-Based EEG Channel Optimization Algorithm for Improved Universality across Participants

Developing an EEG-Based Emotion Recognition Using Ensemble Deep Learning Methods and Fusion of Brain Effective Connectivity Maps

A Comprehensive Survey of Brain-Computer Interface Technology in Healthcare: Research Perspectives

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