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.

Neuropsychology-inspired models have been utilized in recent advances in EEG emotion recognition, such as convolutional networks for spatial features and Transformers for temporal dependencies. While these methods benefit from domain knowledge like frequency-band features and spatial correlations, most overlook the fundamental fact that EEG signals are complex mixtures of neural source activities recorded at the scalp. EEG signals presenting challenges for emotion recognition, particularly in cross-subject scenarios due to significant inter-subject variance. Inspired by neurophysiological principles, we propose a novel framework, named Sera, for EEG-based emotion recognition that explicitly separates source activities and aligns representations across subjects. Sera introduces two key components: (1) a variational autoencoder (VAE) with multiple multi-stage decoders (M2VAE) designed to disentangle EEG signals into independent sources, mimicking the neural generation process, and (2) a coarse-to-fine representation alignment block (CFRA) to mitigate subject-to-subject variability. The coarse alignment employs adversarial training with a domain discriminator, while the fine-grained alignment matches covariance matrices to capture temporal correlations within EEG segments. Extensive experiments demonstrate that Sera outperforms the state-of-the-art methods with improvements ranging from 1% to 5%, averaging 3.14% and 3.05% on the DEAP and DREAMER datasets, respectively, confirming its effectiveness and neurophysiological grounding. The code is available at: https://github.com/JZH98/Sera-code.

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.

Existing machine learning methods for classification and recognition of EEG motor imagery usually suffer from reduced accuracy for limited training data. To address this problem, this paper proposes a multi-rhythm capsule network (FBCapsNet) that uses as little EEG information as possible with key features to classify motor imagery and further improves the classification efficiency. The network conforms to a small recognition model with only 3 acquisition channels but it can effectively use the limited data for feature learning. Based on the BCI Competition IV 2b data set, experimental results show that the proposed network can achieve 2.41% better performance than existing cutting-edge methods.