Topological EEG Nonlinear Dynamics Analysis for Emotion Recognition

Yan, Yan; Xuankun, Wu,; Li, Chengdong; He, Yini; Zhang, Zhicheng; Li, Huihui; Li, Ang; Wang, Lei

doi:10.1109/tcds.2022.3174209

Cited by 19 publications

(7 citation statements)

References 79 publications

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“…Investigations in cognitive systems have also been undertaken using three-dimensional data points as point clouds that offer modeling opportunity [81]. Emotion, in particular, can be investigated [82] using a point-cloud dataset, and a graph neural network can be a possible approach [83]. However, multi-contextual cognitive constructs like sarcasm and metaphor have not been investigated yet in such a setup.…”

Section: External Knowledge Integration Strategymentioning

confidence: 99%

Capturing the Concept Projection in Metaphorical Memes for Downstream Learning Tasks

Acharya,

Das,

Sudarshan

2024

IEEE Access

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Metaphorical memes, where a source concept is projected into a target concept, are an essential construct in figurative language. In this article, we present a novel approach for downstream learning tasks on metaphorical multimodal memes. Our proposed framework replaces traditional methods using metaphor annotations with a metaphor-capturing mechanism. Besides using the significant zero-shot learning capability of state-of-the-art pretrained encoders, this work introduces an alternative external knowledge enhancement strategy based on ChatGPT (chatbot generative pretrained transformer), demonstrating its effectiveness in bridging the intermodal semantic gap. We propose a new concept projection process consisting of three distinct components to capture the intramodal knowledge and intermodal concept gap in the forms of text modality embedding, visual modality embedding, and concept projection embedding. This approach leverages the attention mechanism of the Graph Attention Network for fusing the common aspects of external knowledge related to the knowledge in the text and image modality to implement the concept projection process. Our experimental results demonstrate the superiority of our proposed approach compared to existing methods.

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Section: External Knowledge Integration Strategymentioning

confidence: 99%

Capturing the Concept Projection in Metaphorical Memes for Downstream Learning Tasks

Acharya,

Das,

Sudarshan

2024

IEEE Access

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“…In recent years, there has been a growing interest in leveraging TDA to extract pertinent topological features from signals in the field of signal processing. For the analysis of EEG signals, recent research [3][4][5][6][7][8][9][10] has demonstrated that the extracted topological features contain valuable information relevant to various neurological disorders. As summarized in the survey [11], the following three approaches are commonly employed in the TDA analysis of EEG signals:…”

Section: Introductionmentioning

confidence: 99%

Towards Analysis of Multivariate Time Series Using Topological Data Analysis

Zheng,

Feng,

Ekstrom

2024

Mathematics

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Topological data analysis (TDA) has proven to be a potent approach for extracting intricate topological structures from complex and high-dimensional data. In this paper, we propose a TDA-based processing pipeline for analyzing multi-channel scalp EEG data. The pipeline starts with extracting both frequency and temporal information from the signals via the Hilbert–Huang Transform. The sequences of instantaneous frequency and instantaneous amplitude across all electrode channels are treated as approximations of curves in the high-dimensional space. TDA features, which represent the local topological structure of the curves, are further extracted and used in the classification models. Three sets of scalp EEG data, including one collected in a lab and two Brain–computer Interface (BCI) competition data, were used to validate the proposed methods, and compare with other state-of-art TDA methods. The proposed TDA-based approach shows superior performance and outperform the winner of the BCI competition. Besides BCI, the proposed method can also be applied to spatial and temporal data in other domains such as computer vision, remote sensing, and medical imaging.

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“…Fernández and Mateos (2022) used persistent homology to calculate topological biomarkers from the signals as features, and experiments have shown that biomarkers can effectively detect changes in brain dynamics ( Fernández and Mateos, 2022 ). Yan et al (2023) proposed an analysis method based on persistent homology for EEG emotion recognition, which extracts topological features from different EEG rhythm bands through VR filtering.…”

Section: Introductionmentioning

confidence: 99%

Automatic epileptic seizure detection based on persistent homology

Wang,

Liu,

Shi

et al. 2023

Front. Physiol.

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Epilepsy is a prevalent brain disease, which is quite difficult-to-treat or cure. This study developed a novel automatic seizure detection method based on the persistent homology method. In this study, a Vietoris–Rips (VR) complex filtration model was constructed based on the EEG data. And the persistent homology method was applied to calculate the VR complex filtration barcodes to describe the topological changes of EEG recordings. Afterward, the barcodes as the topological characteristics of EEG signals were fed into the GoogLeNet for classification. The persistent homology is applicable for multi-channel EEG data analysis, where the global topological information is calculated and the features are extracted by considering the multi-channel EEG data as a whole, without the multiple calculations or the post-stitching. Three databases were used to evaluate the proposed approach and the results showed that the approach had high performances in the epilepsy detection. The results obtained from the CHB-MIT Database recordings revealed that the proposed approach can achieve a segment-based averaged accuracy, sensitivity and specificity values of 97.05%, 96.71% and 97.38%, and achieve an event-based averaged sensitivity value of 100% with 1.22 s average detection latency. In addition, on the Siena Scalp Database, the proposed method yields averaged accuracy, sensitivity and specificity values of 96.42%, 95.23% and 97.6%. Multiple tasks of the Bonn Database also showed achieved accuracy of 99.55%, 98.63%, 98.28% and 97.68%, respectively. The experimental results on these three EEG databases illustrate the efficiency and robustness of our approach for automatic detection of epileptic seizure.

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Topological EEG Nonlinear Dynamics Analysis for Emotion Recognition

Cited by 19 publications

References 79 publications

Capturing the Concept Projection in Metaphorical Memes for Downstream Learning Tasks

Capturing the Concept Projection in Metaphorical Memes for Downstream Learning Tasks

Towards Analysis of Multivariate Time Series Using Topological Data Analysis

Automatic epileptic seizure detection based on persistent homology

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