Visual Explanations of Deep Convolutional Neural Network for eye blinks detection in EEG-based BCI applications

Giudice, Michele Lo; Mammone, Nadia; Ieracitano, Cosimo; Campolo, Maurizio; Bruna, Arcangelo; Tomaselli, Venera; Morabito, F.

doi:10.1109/ijcnn55064.2022.9892567

Cited by 3 publications

(2 citation statements)

References 25 publications

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“…The Generative Adversarial Network (GAN) is an efficient approach for medical image enhancements and quality improvements by performing a max-min game between two subnetworks in the architecture [69][70][71][72] . The network of generator and discriminator is responsible for maximizing the loss of embedded image and minimizing the reward respectively.…”

Section: Generative Adversarial Networkmentioning

confidence: 99%

AI-Based Advanced Approaches and Dry Eye Disease Detection Based on Multi-Source Evidence: Cases, Applications, Issues, and Future Directions

Wang,

Xing,

Pan

et al. 2024

Big Data Min. Anal.

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This study explores the potential of Artificial Intelligence (AI) in early screening and prognosis of Dry Eye Disease (DED), aiming to enhance the accuracy of therapeutic approaches for eye-care practitioners.Despite the promising opportunities, challenges such as diverse diagnostic evidence, complex etiology, and interdisciplinary knowledge integration impede the interpretability, reliability, and applicability of AI-based DED detection methods. The research conducts a comprehensive review of datasets, diagnostic evidence, and standards, as well as advanced algorithms in AI-based DED detection over the past five years. The DED diagnostic methods are categorized into three groups based on their relationship with AI techniques: (1) those with ground truth and/or comparable standards, (2) potential AI-based methods with significant advantages, and (3) supplementary methods for AI-based DED detection. The study proposes suggested DED detection standards, the combination of multiple diagnostic evidence, and future research directions to guide further investigations. Ultimately, the research contributes to the advancement of ophthalmic disease detection by providing insights into knowledge foundations, advanced methods, challenges, and potential future perspectives, emphasizing the significant role of AI in both academic and practical aspects of ophthalmology.

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Section: Generative Adversarial Networkmentioning

confidence: 99%

AI-Based Advanced Approaches and Dry Eye Disease Detection Based on Multi-Source Evidence: Cases, Applications, Issues, and Future Directions

Wang,

Xing,

Pan

et al. 2024

Big Data Min. Anal.

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show abstract

“…In [23], the authors proposed an EEG signal analysis-based BCI system that can automatically recognize and decode voluntary eye blinks using Deep Learning. The primary goal of this study was to examine the explainability of the proposed CNN with the ultimate goal of determining which EEG signal segments are most crucial to the process of distinguishing between intentional and involuntary blinks.…”

Section: Related Workmentioning

confidence: 99%

Explainable Data Poison Attacks on Human Emotion Evaluation Systems Based on EEG Signals

Zhang

Umar

et al. 2023

IEEE Access

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The major aim of this paper is to explain the data poisoning attacks using label-flipping during the training stage of the electroencephalogram (EEG) signal-based human emotion evaluation systems deploying Machine Learning models from the attackers' perspective. Human emotion evaluation using EEG signals has consistently attracted a lot of research attention. The identification of human emotional states based on EEG signals is effective to detect potential internal threats caused by insider individuals. Nevertheless, EEG signal-based human emotion evaluation systems have shown several vulnerabilities to data poison attacks. Besides, due to the instability and complexity of the EEG signals, it is challenging to explain and analyze how data poison attacks influence the decision process of EEG signal-based human emotion evaluation systems. In this paper, from the attackers' side, data poison attacks occurring in the training phases of six different Machine Learning models including Random Forest, Adaptive Boosting (AdaBoost), Extra Trees, XGBoost, Multilayer Perceptron (MLP), and K-Nearest Neighbors (KNN) intrude on the EEG signal-based human emotion evaluation systems using these Machine Learning models. This seeks to reduce the performance of the aforementioned Machine Learning models with regard to the classification task of 4 different human emotions using EEG signals. The findings of the experiments demonstrate that the suggested data poison assaults are model-independently successful, although various models exhibit varying levels of resilience to the attacks. In addition, the data poison attacks on the EEG signal-based human emotion evaluation systems are explained with several Explainable Artificial Intelligence (XAI) methods including Shapley Additive Explanation (SHAP) values, Local Interpretable Model-agnostic Explanations (LIME), and Generated Decision Trees. And the codes of this paper are publicly available on GitHub.

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An Embedded EOG-based Brain Computer Interface System for Robotic Control

Chepyk,

Bruna,

Campolo

et al. 2023

2023 8th International Conference on Smart and Sustainable Technologies (SpliTech)

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Visual Explanations of Deep Convolutional Neural Network for eye blinks detection in EEG-based BCI applications

Cited by 3 publications

References 25 publications

AI-Based Advanced Approaches and Dry Eye Disease Detection Based on Multi-Source Evidence: Cases, Applications, Issues, and Future Directions

AI-Based Advanced Approaches and Dry Eye Disease Detection Based on Multi-Source Evidence: Cases, Applications, Issues, and Future Directions

Explainable Data Poison Attacks on Human Emotion Evaluation Systems Based on EEG Signals

An Embedded EOG-based Brain Computer Interface System for Robotic Control

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