Interictal Epileptiform Discharge is essential for identifying epilepsy. However, the unpredictable and non-stationary nature of electroencephalogram (EEG) patterns poses considerable challenges for reliable identification. Manual interpretation of EEG is subjective and time-consuming. With advancements in machine learning and deep learning, computer-aided approaches for automated IED detection have been rapidly developed. The state-of-the-art convolutional neural network (CNN)-based methods have shown promising results but struggle to capture long-term dependencies in time-series data. In contrast, Transformer excels at modeling sequential information through self-attention mechanisms, overcoming the CNN limitations. This study proposes an IED Detector (IEDD) that integrates convolutional layers and a Transformer to detect IEDs. The IEDD initially employs convolutional layers to extract local features of IEDs, followed by a Transformer to model long-term dependencies. To further extract spatial features, EEG data are represented as a three-dimensional tensor with embedded channel topology, where a CNN captures spatial features at each sampling point and a Long Short-Term Memory (LSTM) network models their temporal evolution. Additionally, due to the scarcity of IED data, a novel Transformer-based Generative Adversarial Network (GAN) is developed to augment the IED dataset. Experimental results show the proposed approach achieves an average accuracy of 96.11% on the augmented Dataset 1 and 95.25% on Dataset 2 for binary classification, with an average sensitivity of 87.26% and precision of 89.96% for multi-label classification. These findings provide valuable insights into advancing deep learning and Transformer-based approaches for automated IED detection.

In clinical acupuncture practice, needle twirling (NT) and needle retention (NR) are strategically combined to achieve different therapeutic effects, highlighting the importance of distinguishing between different acupuncture states. Scalp EEG has been proven significantly relevant to brain activity and acupuncture stimulation. In this work, we designed an acupuncture paradigm to collect scalp EEG to study the differences in EEG changes during different acupuncture states. Since deep learning (DL) has been increasingly used in EEG analysis, we propose the Acupuncture Transformer Detector (ATD), a model based on Convolutional Neural Networks (CNN) and Transformer technology. ATD encapsulates the local and global features of EEG under the acupuncture states of Zusanli acupoint (ST-36) in an end-to-end classification framework. The experiment results from 28 healthy participants show that the proposed model can efficiently classify the EEG in different states, with an accuracy of $85.47\pm 0.73\%$. In this study, time-frequency analysis revealed that power changes were mainly confined to the delta frequency band under different acupuncture states. Brain topography revealed that ST-36 was activated primarily on the left frontal and parieto-occipital areas. This method provides new ideas for automatic recognition of acupuncture status from the perspective of DL, offering new solutions for standardizing acupuncture procedures.

High-quality scalp EEG datasets are extremely valuable for motor imagery (MI) analysis. However, due to electrode size and montage, different datasets inevitably experience channel information loss, posing a significant challenge for MI decoding. A 2D representation that focuses on the time domain may loss the spatial information in EEG. In contrast, a 3D representation based on topography may suffer from channel loss and introduce noise through different padding methods. In this paper, we propose a framework called Reference Electrode Standardization Interpolation Network (REI-Net). Through an interpolation of 3D representation, REI-Net retains the temporal information in 2D scalp EEG while improving the spatial resolution within a certain montage. Additionally, to overcome the data variability caused by individual differences, transfer learning is employed to enhance the decoding robustness. Our approach achieves promising performance on two widely-recognized MI datasets, with an accuracy of 77.99% on BCI-C IV-2a and an accuracy of 63.94% on Kaya2018. The proposed algorithm outperforms the SOTAs leading to more accurate and robust results.

Photometric stereo aims to regress object surface normal from a set of images observed under varying illuminations. Although existing methods have achieved promising results, the irregular high-frequency detail is ignored, especially in complex and tiny surface folds. To address this problem, a coarse-to-fine reconstruction framework is proposed for non-Lambertian photometric stereo. Specifically, a coarse network is designed to roughly predict object surface normal, which learns the mapping from observed images to coarse surface normal. Then, to deal with the high-frequency information loss, we introduce a fine network to extract high-frequency information by leveraging both coarse surface normal and observation images. Meanwhile, to provide more supervision, we design a reconstruction module to reconstruct observed images from predicted surface normal and illuminations. Extensive experiments have demonstrated that the proposed method outperforms existing works and restores high-frequency detail effectively. In addition, the proposed method promotes the robustness under sparse illuminations.

With the development of computer science and multimedia technology, Web-based learning becomes increasingly popular. User evaluation plays a significant role in the process of guided learning. In recent years, there is great progress in the development of evaluation technology. However, few evaluation methods fully take online learning activity analysis and individual differences into account. This paper proposes a personalized user evaluation model for Web-based learning systems. The model is utilized to record and analyze various learning activities throughout the entire learning process. Considering individual differences, learners are clustered and specific evaluation standards are set for different clusters. Comprehensive evaluation is achieved by combining Analytic Hierarchy Process, Fuzzy C-Means clustering and normalization algorithm. Through the comparison with several other common evaluation methods, experimental results show that the proposed method outperforms existing ones on the accuracy of learner evaluation.