Electroencephalogram (EEG)-based emotion recognition is of great significance for aiding in clinical diagnosis, treatment, nursing and rehabilitation. Current research on this issue mainly focuses on utilizing various network architectures with different types of neurons to exploit the temporal, spectral, or spatial information from EEG for classification. However, most studies fail to take full advantage of the useful Temporal-Spectral-Spatial (TSS) information of EEG signals. In this paper, we propose a novel and effective Fractal Spike Neural Network (Fractal-SNN) scheme, which can exploit the multi-scale TSS information from EEG, for emotion recognition. Our designed Fractal-SNN block in the proposed scheme approximately simulates the biological neural connection structures based on spiking neurons and a new fractal rule, allowing for the extraction of discriminative multi-scale TSS features from the signals. Our designed training technique, inverted drop-path, can enhance the generalization ability of the Fractal-SNN scheme. Sufficient experiments on four public benchmark databases, DREAMER, DEAP, SEED-IV and MPED, under the subject-dependent protocols demonstrate the superiority of the proposed scheme over the related advanced methods. In summary, the proposed scheme provides a promising solution for EEG-based emotion recognition.

Fractal Spiking Neural Network Scheme for EEG-Based Emotion Recognition

Electroencephalogram (EEG) sensor data are useful and important for emotion recognition. However, cross-subject EEG emotion recognition suffers from the challenging problems of individual difference and noise disturbance. To cope with these problems, we propose a hierarchical 3-D MLP-based neural network (HMNN). This method consists of multiple hierarchical layers of 3D-MLPBlocks and a noise optimization module. The 3D-MLPBlock is designed to extract the multiperiod features of common emotional patterns across different individuals; the noise optimization module is devised to enhance the network robustness to noise disturbance. Experimental results on public benchmarks DEAP, DREAMER, and SEED-IV have demonstrated the superiority of HMNN over the related advanced approaches. Specifically, HMNN obtains the accuracies of 63.69%/60.03% for valence/aoursal classification on DEAP, 62.51%/64.49% for valence/arousal classification on DREAMER, and 62.29% for emotion classification on SEED-IV.

A Hierarchical Three-Dimensional MLP-Based Model for EEG Emotion Recognition

Recently, the microstate analysis method has been widely used to investigate the temporal and spatial dynamics of electroencephalogram (EEG) signals. However, most studies have focused on EEG at resting state, and few use microstate analysis to study emotional EEG. This paper aims to investigate the temporal and spatial patterns of EEG in emotional states, and the specific neurophysiological significance of microstates during the emotion cognitive process, and further explore the feasibility and effectiveness of applying the microstate analysis to emotion recognition.We proposed a KLGEV-criterion-based microstate analysis method, which can automatically and adaptively identify the optimal number of microstates in emotional EEG. The extracted temporal and spatial microstate features then served as novel feature sets to improve the performance of EEG emotion recognition. We evaluated the proposed method on two publicly available emotional EEG datasets: the SJTU Emotion EEG Dataset (SEED) and the Database for Emotion Analysis using Physiological Signals (DEAP).For the SEED dataset, 10 microstates were identified using the proposed method. These temporal and spatial features were fed into AutoGluon, an open-source automatic machine learning model, yielding an average three-class accuracy of 70.38% (±8.03%) in subject-dependent emotion recognition. For the DEAP dataset, the method identified 9 microstates. The average accuracy in the arousal dimension was 74.33% (±5.17%) and 75.49% (±5.70%) in the valence dimension, which were competitive performance compared to some previous machine-learning-based studies. Based on these results, we further discussed the neurophysiological relationship between specific microstates and emotions, which broaden our knowledge of the interpretability of EEG microstates. In particular, we found that arousal ratings were positively correlated with the activity of microstate C (anterior regions of default mode network) and negatively correlated with the activity of microstate D (dorsal attention network), while valence ratings were positively correlated with the activity of microstate B (visual network) and negatively correlated with the activity of microstate D (dorsal attention network).In summary, the findings in this paper indicate that the proposed KLGEV-criterion-based method can be employed to research emotional EEG signals effectively, and the microstate features are promising feature sets for EEG-based emotion recognition.

Emotion recognition based on microstate analysis from temporal and spatial patterns of electroencephalogram

Driver vigilance estimation is essential for fatigue and traffic accident reduction. Although previous algorithms for driver vigilance estimation have achieved high evaluation performance, several challenges remain open: 1) how to effectively utilize multimodal electroencephalography (EEG) and electrooculogram (EOG) signals remains challenging; 2) how to capture vector feature information in part-to-whole space more effectively; and 3) how to enhance the core ability of a network, including in-depth mining of features and eliminating redundant information as much as possible. To address these challenges, we propose a novel multimodal detection method for driver vigilance estimation, which consists of residual attention blocks and a capsule attention mechanism. Specifically, we propose a residual attention network to optimize the channel-space features of low-level capsules to adaptively rescale the features of each channel to fuse the interdependence among feature channels. Meanwhile, the part–whole relationship in the features is explored with the use of dynamic routing, the features with large contributions are strengthened, and the transmission efficiency of the neural network is ultimately improved. The experimental datasets show that the residual attention capsule network (Res-att-capsnet) outperforms the state-of-the-art baselines in both regression and classification tasks. The feasibility and effectiveness of our proposed method are thus demonstrated.

Residual Attention Capsule Network for Multimodal EEG- and EOG-Based Driver Vigilance Estimation

Due to severe cross-subject data variations in electroencephalogram (EEG) signals, the issue of subject-independent EEG-based emotion recognition remains challenging till today. To cope with this challenge, we propose a novel and effective dynamic stream selection network (DSSN), which can adaptively adjust its structure according to the characteristics of signal data from different individuals for this issue. DSSN consists of a tri-stream structure and a dynamic selection network. The tri-stream structure takes charge of extracting the spatial, the temporal, and the spatio-temporal features, respectively, for emotion classification. The dynamic selection network is responsible for selecting the most suitable stream for every subject. Subject-independent experiments on the benchmarks DEAP, DREAMER, and SEED-IV have readily demonstrated the advantage of DSSN over the related advanced approaches.

Dynamic Stream Selection Network for Subject-Independent EEG-Based Emotion Recognition

In recent years, there has been a growing research interest in using deep learning to resolve the issue of Electroencephalogram (EEG)-based emotion recognition. Current research emphasizes exploiting the useful information from each single EEG channel or each individual set of multi-channel EEG, but overlooks the correlation information among different multi-channel EEG sets. To explore such discriminative correlation information, we propose a novel and effective method“, Trainable Adjacency Relation Driven Graph Convolutional Network (TARDGCN)", which contains two complementary modules, TAR and GCN. TAR optimizes the local pair-wise positions of multi-channel EEG sets, which helps form an improved graphic representation for GCN to learn the global correlation among these sets for classification. The proposed method is capable of dealing with the problem of small sample size but large data variation in this issue. Our experimental results conducted on the databases DREAMER and DEAP in the subject-dependent and subject-independent modes show that TARDGCN outperforms the state-of-the-art approaches in classifying all of valence, arousal, and dominance. 

EEG-Based Emotion Recognition Using Trainable Adjacency Relation Driven Graph Convolutional Network

Biometrics have attracted growing research interests as information security and safety gain increasing attention to date. As a kind of important biomedical signal, electroencephalogram (EEG) contains valuable information about identity, emotionality, personality, and so on. Thus, automatically distinguishing the identities based on EEG is beneficial to the development of biometrics, forensics, and informatics. Although deep learning has absorbed much research attention for the issue of EEG-based person identification, the performance enhancement of this methodology seems to have hit a bottleneck recently. Hence, by rethinking the problems haunting this issue, we plan to reinvigorate the conventional method pipeline and put forward a novel and effective tensorial scheme away from the deep learning mainstream. Specifically, the proposed tensorial scheme extracts the effective tensorial representation from multichannel EEG at first; then, the scheme performs the designed tensorial learning to improve the discriminability of the feature space; and finally, the scheme carries out the devised tensorial measurement in the learned metric space for classification. Experimental results have demonstrated the superiority of the proposed scheme over the related advanced approaches by means of the challenging benchmark databases: DEAP, SEED, and DREAMER.

A Novel Tensorial Scheme for EEG-Based Person Identification

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Mingming Wang

Author Tools

Chat about Author

Papers

EEG-Based Emotion Recognition Using Trainable Adjacency Relation Driven Graph Convolutional Network

A Novel Tensorial Scheme for EEG-Based Person Identification