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dataset Refinement for Improving the Generalization Ability of the EEG Decoding Model 13

Dataset Refinement for Improving the Generalization Ability ...

引用

13th international Winter conference on Brain-Computer Interface, BCI 2025

作者： Kim, Sung-Jin Lee, Dae-Hyeok Han, Hyeon-Taek Dept. of Artificial Intelligence Korea University Seoul Korea Republic of Dept. of Brain and Cognitive Engineering Korea University Seoul Korea Republic of

ISBN: (纸本)9798331521929

Electroencephalography (EEG) is a generally used neuroimaging approach in brain-computer interfaces due to its non-invasive characteristics and convenience, making it an effective tool for understanding human intentions. Therefore, recent research has focused on decoding human intentions from EEG signals utilizing deep learning methods. However, since EEG signals are highly susceptible to noise during acquisition, there is a high possibility of the existence of noisy data in the dataset. Although pioneer studies have generally assumed that the dataset is well-curated, this assumption is not always met in the EEG dataset. In this paper, we addressed this issue by designing a dataset refinement algorithm that can eliminate noisy data based on metrics evaluating data influence during the training process. We applied the proposed algorithm to two motor imagery EEG public datasets and three different models to perform dataset refinement. The results indicated that retraining the model with the refined dataset consistently led to better generalization performance compared to using the original dataset. Hence, we demonstrated that removing noisy data from the training dataset alone can effectively improve the generalization performance of deep learning models in the EEG domain. © 2025 IEEE.

关键词： Deep learning

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Social Media Sentiment as an Indicator of Electoral Outcomes: A Study of Indian General Elections

Social Media Sentiment as an Indicator of Electoral Outcomes...

引用

2025 international conference on Pervasive Computational Technologies, ICPCT 2025

作者： Arya, Swati Agarwal, Amit Lakhera, Anshul Sharma, Uday Jain, Chitra Mittal, Ankush Coer University Computer Science and Engineering Uttarakhand Roorkee India AICoE Well Fargo Banglore India

ISBN: (纸本)9798331508685

The volatile nature of public opinion, increasingly driven by social media platforms such as Twitter, has emerged as a pivotal factor in electoral dynamics. This study targets the 2019 and 2024 Indian general elections, which featured prominent political figures Narendra Modi and Rahul Gandhi, to quantitatively assess public sentiment through advanced sentiment analysis techniques applied to Twitter data. Utilizing comprehensive datasets from these electoral periods, sentiment polarity was systematically analyzed using the Natural Language Toolkit (NLTK) and the VADER (Valence Aware Dictionary and sEntiment Reasoner) model. Sentiments were classified into positive, negative, and neutral categories, serving as predictors for election outcomes. This approach integrates sentiment scores with machine learning algorithms to model and predict electoral results. The study highlights the efficacy of social media analytics in capturing real-time public opinion, reinforcing the critical role of sentiment analysis and opinion mining in modern electoral studies. The findings provide a robust framework for understanding the influence of digital public sentiment on election outcomes, offering methodological advancements in the predictive modeling of electoral behavior. © 2025 IEEE.

关键词： Tweets

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SwinMixer Detector: A Two-Level Traffic Flow Detection System Based on Swin Transformer and MLP-Mixer 24th

SwinMixer Detector: A Two-Level Traffic Flow Detection Syste...

引用

24th international conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2024

作者： Lu, Lu Fang, Menghao Li, Xia Li, Minghui Lei, Tianwei Kong, Zixiao School of Cyber Science and Engineering University of International Relations Beijing100091 China Institute of Disaster and Emergency Medicine Tianjin University Tianjin300072 China Marine Engineering of College Dalian Maritime University Dalian116026 China School of Computer Science and Technology Beijing Institute of Technology Beijing100081 China

ISBN: (纸本)9789819615414

This paper explores the problem of boundary data classification ambiguity that arises when machine learning techniques are applied in the field of intrusion detection. The features and attributes of the boundary data place them in the boundaries or boundaries between different categories in the classification decision, and these data points may be close to the decision boundaries of different categories in the feature space, resulting in relative uncertainty or some ambiguity in their classification. To address this problem, this study innovatively proposes a secondary detection system called the SwinMixer Detector, which utilises the Swin Transformer and MLP-Mixer architecture. The system consists of a primary triage detection platform utilizing Swin Transformer classifier and a secondary detection platform utilizing MLP-Mixer, which can effectively solve the problem of fuzzy classification of boundary data. By validating on the SR-BH2020 dataset and the CIC-DDOS2019 dataset, the method in this paper achieves a precision of 96.40% and 95.81%, respectively, which provides a new research idea and solution for the field of intrusion detection. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Mixers (machinery)

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Nearest Neighbors Counterfactuals 10th

Nearest Neighbors Counterfactuals

引用

10th international conference on machine learning, Optimization, and data science, LOD 2024

作者： Magagnini, Marica Carrizosa, Emilio De Leone, Renato School of Science and Technology Università di Camerino Camerino62032 Italy Instituto de Matemáticas de la Universidad de Sevilla Seville41012 Spain

ISBN: (纸本)9783031824807

machine learning algorithms usually make decisions without providing any explanations on how they were reached. As a result, such models are not understood and trusted by the users. Counterfactuals are user-friendly explanations that provide valuable information to determine what should be changed in order to modify the outcome of a black box decision-making model without revealing the underlying algorithmic details. We present a novel technique to obtain counterfactual explanations by solving an optimization problem when a k-Nearest Neighborhood classifier is employed in a binary classification. Results from artificial and real datasets demonstrate the validity of the proposal. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

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EEG-based Multimodal Representation learning for Emotion Recognition 13

EEG-based Multimodal Representation Learning for Emotion Rec...

引用

13th international Winter conference on Brain-Computer Interface, BCI 2025

作者： Yin, Kang Shin, Hye-Bin Li, Dan Lee, Seong-Whan Dept. of Artificial Intelligence Korea University Seoul Korea Republic of Dept. of Brain and Cognitive Engineering Korea University Seoul Korea Republic of

ISBN: (纸本)9798331521929

Multimodal learning has been a popular area of research, yet integrating electroencephalogram (EEG) data poses unique challenges due to its inherent variability and limited availability. In this paper, we introduce a novel multimodal framework that accommodates not only conventional modalities such as video, images, and audio, but also incorporates EEG data. Our framework is designed to flexibly handle varying input sizes, while dynamically adjusting attention to account for feature importance across modalities. We evaluate our approach on a recently introduced emotion recognition dataset that combines data from three modalities, making it an ideal testbed for multimodal learning. The experimental results provide a benchmark for the dataset and demonstrate the effectiveness of the proposed framework. This work highlights the potential of integrating EEG into multimodal systems, paving the way for more robust and comprehensive applications in emotion recognition and beyond. © 2025 IEEE.

关键词： Emotion Recognition

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Evaluation of Efficient and Performance-Retaining cVEP-BCI Class Augmentation 13

Evaluation of Efficient and Performance-Retaining cVEP-BCI C...

引用

13th international Winter conference on Brain-Computer Interface, BCI 2025

作者： Fodor, Milán András Cantürk, Atilla Le, Thi-Phuong-Ngan Heisenberg, Gernot Volosyak, Ivan Faculty of Technology and Bionics Rhine-Waal University of Applied Sciences Kleve47533 Germany Institute of Information Science Technical University of Applied Sciences Cologne Cologne50678 Germany

ISBN: (纸本)9798331521929

Brain–computer interfaces (BCIs) enable direct communication between the brain and external devices. BCI spellers are one of their most significant applications, allowing users to operate a keyboard with their brain. BCIs based on code-modulated visual evoked potentials (cVEPs) offer the potential for class augmentation due to the cyclical nature of their stimuli. This approach allows for the use of less training data while creating additional classes in the spelling interface, without requiring additional data or causing significant performance loss. To definitively address concerns regarding the potential limitations of this technique, we derived four extra classes from a single class in our BCI speller and evaluated their performance. The results demonstrated no performance loss and no evidence of anomalously poor participant performance. This research advocates for class augmentation as a standard practice to reduce calibration time and improve the scalability, accessibility, and overall effectiveness of cVEP-based BCI spellers. © 2025 IEEE.

关键词： Electroencephalography

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Virtual learning machine for Tiny Devices 20th

Virtual Learning Machine for Tiny Devices

引用

20th Principle and Practice of data and Knowledge Acquisition Workshop, PKAW 2024, held in conjunction with the 21st Pacific Rim international conference on Artificial Intelligence, PRICAI 2024

作者： Kitagawa, Nozomi Yamauchi, Koichiro Graduate school of Engineering Department of Computer Science Chubu University Kasugai Japan

ISBN: (纸本)9789819600250

Recent developments in artificial intelligence (AI) have increased the demand for high-performance computational devices. However, edge devices are highly restricted in terms of computational power and memory capacity. To address this, a learning engine called swap kernel regression (SKR) [Masaharu et al. 2019] was developed that enables both inference and learning using an edge device. The key feature of SKR is ability to extend beyond the real physical capacity by collaborating with a second storage device. SKR thereby enables the virtual construction of a large-scale learning machine on a tiny device. We developed an improved version of SKR that maximizes performance by solving the thrashing problem and further reduces the computational cost. Using this improved version, we aim to achieve few-shot class-incremental learning (FSCIL), which continuously adapts to new tasks with limited samples, envisioned for on-site implementation. The proposed method uses a kernel machine that operates in a restricted environment in collaboration with a secondary storage system. The kernel parameters, which are not essential for calculating the output values for upcoming inputs, are stored in secondary storage to create a space in the main memory. The essential kernel parameters stored in secondary storage are loaded into main memory when required. Using this strategy, the system can realize recognition or regression tasks without compromising its generalization capability. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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Sarcasm Detection of Facebook's Posts Using machine learning Models 24

Sarcasm Detection of Facebook's Posts Using Machine Learning...

引用

8th international conference on Natural Language Processing and Information Retrieval, NLPIR 2024

作者： Chiu, Shu-I Jhou, Ting-Wei Department of Computer Science National Chengchi University Taipei Taiwan

ISBN: (纸本)9798400717383

The coronavirus disease 2019 (COVID-19) has brought massive challenges to the world, altering people's lives. We conducted a study on people's mental states during Taiwan's National Epidemic Level 3 Alert in 2021. On May 22, 2021, during a regular press conference held by the Taiwan Centers for Disease Control (CDC), the Minister of Health and Welfare introduced the term 'Retrospective Adjustment', which left the entire population in shock. Our approach consists of a two-stage task. Constructing the model is the first stage and detecting sarcasm is the second. First, we integrated TCNN and BiLSTM models. Second, we focused on misclassified by the model and performed feature engineering based on these misclassified data. After constructing features, we performed well using machine learning models. Finally, the experimental results show that our approach performs well in detecting sarcasm using linguistic and lexical-based features. In the second stage, the LSTM model detects sarcasm, achieving a performance of 0.73. We integrate the results of two stages to adjust accuracy. By improving the accuracy of the misclassified data to 0.6, the overall accuracy for negative posts has increased to 0.76. © 2024 Copyright held by the owner/author(s).

关键词： Deep learning

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Online machine learning for Real-Time Cell Culture Process Monitoring 37th

Online Machine Learning for Real-Time Cell Culture Process ...

引用

37th Australasian Joint conference on Artificial Intelligence, AJCAI 2024

作者： Khuat, Thanh Tung Bassett, Robert Otte, Ellen Gabrys, Bogdan Complex Adaptive Systems Laboratory The Data Science Institute University of Technology Sydney UltimoNSW2007 Australia CSL Innovation MelbourneVIC3000 Australia

ISBN: (纸本)9789819603503

In the biopharmaceutical industry, the production of monoclonal antibodies (mAbs) relies heavily on the optimization and real-time monitoring of cell culture processes. This study assesses the effectiveness of online machine learning (OML) models for the real-time monitoring of nutrient and metabolite concentrations, such as glucose, lactate, and ammonium, during the cell cultivation process using Raman spectroscopy as input data. The research addresses the challenge of limited data inherent in cell culture processes, particularly when transferring learned information to new bioreactor runs with different cell lines and media compositions. Traditional pretrained machine learning models often fail to generalize their learned information in such context. Our research demonstrates that OML models can dynamically adapt to the evolving conditions of cell culture processes, providing accurate and timely updates on critical nutrient and metabolite concentrations. We conducted a series of experiments comparing the performance of OML models against pretrained models in monitoring cell culture processes. The findings indicate that OML models exhibit superior performance in capturing the dynamic changes of nutrient and metabolite concentrations during cell cultivation. In contrast, pretrained models exhibit substantial performance degradation when applied to a completely new bioreactor run, highlighting the limitations of static learning approaches in handling the complex, dynamic nature of cell cultures. This study emphasizes the potential of OML in enhancing the robustness and efficiency of real-time process monitoring in mAb production, paving the way for more adaptive and resilient biomanufacturing processes. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Metabolites

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Curriculum learning with Sampling Scheduler for Imbalanced EEG-based Seizure Detection 13

Curriculum Learning with Sampling Scheduler for Imbalanced E...

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13th international Winter conference on Brain-Computer Interface, BCI 2025

作者： Choi, WooHyeok Kim, Jun-Mo Nam, Hyeonyeong Kam, Tae-Eui Dept. Artificial Intelligence Korea University Seoul Korea Republic of

ISBN: (纸本)9798331521929

Epileptic seizures pose serious health risks and significantly affect the quality of life for individuals with epilepsy, emphasizing the importance of accurate and timely detection. Despite advancements in electroencephalography (EEG) based seizure detection, class imbalance between seizure and non-seizure classes remains a major challenge, often leading to biased machine learning models and reduced generalizability. To address this, we propose a sampling scheduler approach inspired by dynamic curriculum learning. Unlike conventional resampling techniques, our method gradually adjusts the sampling ratio between seizure and non-seizure samples during training, enabling the model to effectively learn from imbalanced data. Experiments on the children’s hospital boston and the massachusetts institute of technology scalp EEG (CHB-MIT) dataset demonstrate that our approach outperforms conventional methods, achieving superior results in balanced accuracy, specificity, area under th curve, and geometirc mean. These findings highlight the potential of the sampling scheduler to address the imbalance problem and enhance EEG-based seizure detection. © 2025 IEEE.

关键词： Electroencephalography

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