Eeg-Based Video Identification Using Graph Signal Modeling and Graph Convolutional Neural Network

Jang, Sungil; Moon, Seong-Eun; Lee, Jong-Seok

doi:10.1109/icassp.2018.8462207

Cited by 47 publications

(48 citation statements)

References 21 publications

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“…Brain signals provide comprehensive information regarding the mental state of a human subject. Jang et al [ 83 ] proposed the first method to apply deep learning on graph signals to EEG-based visual stimulus identification. The model converts the EEG into graph signals with appropriate graph structures and signal features as input to GCNs to identify the visual stimulus watched by a human subject.…”

Section: Case Studies Of Gnn For Medical Diagnosis and Analysismentioning

confidence: 99%

Graph-Based Deep Learning for Medical Diagnosis and Analysis: Past, Present and Future

Ahmedt-Aristizabal

Armin

Denman

et al. 2021

Sensors

130

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With the advances of data-driven machine learning research, a wide variety of prediction problems have been tackled. It has become critical to explore how machine learning and specifically deep learning methods can be exploited to analyse healthcare data. A major limitation of existing methods has been the focus on grid-like data; however, the structure of physiological recordings are often irregular and unordered, which makes it difficult to conceptualise them as a matrix. As such, graph neural networks have attracted significant attention by exploiting implicit information that resides in a biological system, with interacting nodes connected by edges whose weights can be determined by either temporal associations or anatomical junctions. In this survey, we thoroughly review the different types of graph architectures and their applications in healthcare. We provide an overview of these methods in a systematic manner, organized by their domain of application including functional connectivity, anatomical structure, and electrical-based analysis. We also outline the limitations of existing techniques and discuss potential directions for future research.

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Section: Case Studies Of Gnn For Medical Diagnosis and Analysismentioning

confidence: 99%

Graph-Based Deep Learning for Medical Diagnosis and Analysis: Past, Present and Future

Ahmedt-Aristizabal

Armin

Denman

et al. 2021

Sensors

130

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“…GCNN is an effective method to extract features from discrete spatial signals [127, 128], which can be used to explore the spatial connection of multi‐dimensional EEG signals for emotion recognition [118]. Jang et al [117] utilized GCNN for emotion recognition based on DEAP database. First of all, intra‐band graphs for each electrode were created by calculating the power and entropy of Delta (0–3 Hz), Theta (4–7 Hz), low Alpha (8–10 Hz), high Alpha (10–12 Hz), low Beta (13–16 Hz), middle Beta (17–20 Hz), and high Beta (21–29 Hz) while considering the relationship between electrodes.…”

Section: Eeg‐based Emotion Classifiersmentioning

confidence: 99%

“…GCNN is an effective method to extract features from discrete spatial signals [127,128], which can be used to explore the spatial connection of multi-dimensional EEG signals for emotion recognition [118]. Jang et al [117]…”

Section: Graph Convolutional Neural Networkmentioning

confidence: 99%

Video‐triggered EEG‐emotion public databases and current methods: A survey

Huang

et al. 2020

Brain Science Advances

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Emotions, formed in the process of perceiving external environment, directly affect human daily life, such as social interaction, work efficiency, physical wellness, and mental health. In recent decades, emotion recognition has become a promising research direction with significant application values. Taking the advantages of electroencephalogram (EEG) signals (i.e., high time resolution) and video‐based external emotion evoking (i.e., rich media information), video‐triggered emotion recognition with EEG signals has been proven as a useful tool to conduct emotion‐related studies in a laboratory environment, which provides constructive technical supports for establishing real‐time emotion interaction systems. In this paper, we will focus on video‐triggered EEG‐based emotion recognition and present a systematical introduction of the current available video‐triggered EEG‐based emotion databases with the corresponding analysis methods. First, current video‐triggered EEG databases for emotion recognition (e.g., DEAP, MAHNOB‐HCI, SEED series databases) will be presented with full details. Then, the commonly used EEG feature extraction, feature selection, and modeling methods in video‐triggered EEG‐based emotion recognition will be systematically summarized and a brief review of current situation about video‐triggered EEG‐based emotion studies will be provided. Finally, the limitations and possible prospects of the existing video‐triggered EEG‐emotion databases will be fully discussed.

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“…Quantitative Results: We compare with several baselines on the ZuCo 2.0 dataset and report the average values of F1 score and the accuracy, respectively, in Table 1. Unimodal baselines include methods that process EEG and EM independently, either using recurrent neural networks such as LSTMs [16] or graph convolutional networks (GCNs) similar to [21]. In contrast, multimodal baselines perform late fusion to process both EEG as well as EM signals.…”

Section: Experiments and Resultsmentioning

confidence: 99%

“…Our model generates sparser graphs with average node degree and total number of edges of 2.58 and 1688, respectively. This is a significant reduction in number of parameters in the best performing graph structure obtained by [17] (average node degree=8 and total edges = 3524). Qualitative Results: We analyze the eye-movement across words and the EEG signals for one particular reading sequence in Figure 2.…”

Section: Experiments and Resultsmentioning

confidence: 99%

Dynamic Graph Modeling Of Simultaneous EEG And Eye-Tracking Data For Reading Task Identification

Mathur

Mittal

Manocha

2021

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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We present a new approach, that we call AdaGTCN, for identifying human reader intent from Electroencephalogram (EEG) and Eye movement (EM) data in order to help differentiate between normal reading and task-oriented reading. Understanding the physiological aspects of the reading process (the cognitive load and the reading intent) can help improve the quality of crowd-sourced annotated data. Our method, Adaptive Graph Temporal Convolution Network (AdaGTCN), uses an Adaptive Graph Learning Layer and Deep Neighborhood Graph Convolution Layer for identifying the reading activities using time-locked EEG sequences recorded during word-level eye-movement fixations. Adaptive Graph Learning Layer dynamically learns the spatial correlations between the EEG electrode signals while the Deep Neighborhood Graph Convolution Layer exploits temporal features from a dense graph neighborhood to establish the state of the art in reading task identification over other contemporary approaches. We compare our approach with several baselines to report an improvement of 6.29% on the ZuCo 2.0 dataset, along with extensive ablation experiments.

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Eeg-Based Video Identification Using Graph Signal Modeling and Graph Convolutional Neural Network

Cited by 47 publications

References 21 publications

Graph-Based Deep Learning for Medical Diagnosis and Analysis: Past, Present and Future

Graph-Based Deep Learning for Medical Diagnosis and Analysis: Past, Present and Future

Video‐triggered EEG‐emotion public databases and current methods: A survey

Dynamic Graph Modeling Of Simultaneous EEG And Eye-Tracking Data For Reading Task Identification

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