Benefiting from the research of machine learning (ML) and deep learning(DL), multivariate methods based on ML and DL have been the mainstream and successful analysis methods in Neural Engineering or Neuroimaging research, for example, assisting diagnosis based on brain Magnetic Resonance Imaging (MRI). However, many existing methods based on traditional ML methods cannot sufficiently extract discriminative features, especially feature patterns across long-distance brain areas, resulting in unsatisfactory classification performance. Designing an effective and robust classifier for different MRI images remains a challenge. In this paper, we introduced dilated 3D CNN method for classifying 3D MRI images combining CNN structure and dilated convolution with a small number of feature maps. We also presented a methodology framework based on dilated 3D CNN method, which can classify both single MRI images and image sequences. Our method and framework were evaluated on the structural MRI images of ADHD-200 dataset and fMRI images of a Schizophrenia dataset, demonstrating better performances than some other state-of-the-art methods.

With the development of brain-computer interfaces (BCI) technologies, EEG-based BCI applications have been deployed for medical purposes. Motor imagery (MI), applied to promote neural rehabilitation for stroke patients, is among the most common BCI paradigms that. The Electroencephalogram (EEG) signals, encompassing an extensive range of channels, render the training dataset a high-dimensional construct. This high dimensionality, inherent in such a dataset, tends to challenge traditional deep learning approaches, causing them to potentially disregard the intrinsic correlations amongst these channels. Such an oversight often culminates in erroneous data classification, presenting a significant drawback of these conventional methodologies. In our study, we propose a novel algorithmic structure of EEG channel-attention combined with Swin Transformer for motor pattern recognition in BCI rehabilitation. Effectively, the self-attention module from transformer architecture could captures temporal-spectral-spatial features hidden in EEG data. The experimental results verify that our proposed methods outperformed other state-of-art approaches with the average accuracy of 87.67%. It is implied that our method can extract high-level and latent connections among temporal-spectral features in contrast to traditional deep learning methods. This paper demonstrates that channel-attention combined with Swin Transformer methods has great potential for implementing high-performance motor pattern-based BCI systems.

Credit card fraud detection (CCFD) is an important issue concerned by financial institutions. Existing methods generally employ aggregated or raw features as their representations to train their detection models. Yet such features tend to fall short of effectively exposing the characteristics of various frauds. In this work, we propose a spatial-temporal gated network (STGN) to automatically learn new informative transactional representations containing users’ transactional behavioral information for CCFD. A gated recurrent neural net unit is specifically constructed with a time-aware gate and location-aware gate to extract users’ spatial and temporal transactional behaviors. A spatial-temporal attention module is designed to expose the transaction motive of users in their historical transactional behaviors, which allows the proposed model to better extract the fraudulent characteristics from successive transactions with time and location information. A representation interaction module is offered to make rational decisions and learn compositive transactional representations. A real-world transaction dataset is used in experiments to verify the efficacy of the learned new representations. The results demonstrate that our proposed model outperforms the state-of-the-art ones, thus greatly advancing the field of CCFD. Note to Practitioners —The features of transaction records reflect the characteristics of users’ transactional behaviors. Therefore, effective features are critical for accurate CCFD. However, fraudsters often pretend to be legitimate users during transactions to deceive the CCFD system. As a result, fraudulent behaviors become concealed within legitimate ones, signifying that original features are inadequate for accurate CCFD. Thus, it is imperative for researchers and practitioners to extract new features that can well expose fraud characteristics. While existing methods employing some transaction aggregation strategies can spot certain fraudulent behaviors, they fail to clearly cluster all the anomalous behaviors and distinguish them from legitimate behaviors. Therefore, this work is driven by the urgent demand to extract new informative features for CCFD. Its primary focus is to unveil the aggregation of fraudulent transactional behaviors from both temporal and spatial perspectives, enabling more accurate CCFD. Specifically, this work introduces a new STGN model that automatically learns new transactional representations incorporating users’ transactional behavioral information for CCFD. By comprehensively considering the time interval and location interval of consecutive user transactions, we thoroughly reveal the temporal and spatial aggregation of fraudulent behavior, which provides valuable insights for CCFD practitioners: 1) employing features that integrate the behavioral characteristics of fraudsters instead of the original features can enhance the model’s capability to identify frauds, and 2) taking into account the time and location intervals of users’ consecutive historical transactions can better uncover the behavioral characteristics of fraudsters.

In recent years, wearable sensor-based human activity recognition (HAR) is becoming more and more attractive, especially in health monitoring and sports management. However, in order to obtain high-quality HAR, it is often necessary to get sufficient labeled activity data, which is very difficult, time-consuming, and costly in a natural environment. To tackle this problem, multisource domain adaptation (DA) is a promising method that aims to learn enough multisource prior knowledge from labeled activity data, and then transfer this learned knowledge to the target unlabeled dataset. Thus, this article presents a novel multisource weighted DA with evidential reasoning (w-MSDAER) for HAR, which can effectively utilize complementary knowledge between multiple sources. Specifically, we first use the strategy of distribution alignment to learn local domain-invariant classifiers based on multisource domains. And then the reliabilities of these derived classifiers are comprehensively evaluated according to the belief function based technique for order preference by similarity to ideal solution (BF-TOPSIS). Finally, the discounting fusion method is used to fuse the local classification results. Comprehensive experiments are conducted on two open-source datasets, and the results show that the proposed w-MSDAER significantly outperforms other state-of-art methods.

Objective: The current state-of-the-art methods significantly improve the detection performance of the steady-state visual evoked potentials (SSVEPs) by using the individual calibration data. However, the time-consuming calibration sessions limit the number of training trials and may give rise to visual fatigue, which weakens the effectiveness of the individual training data. For addressing this issue, this study proposes a novel inter- and intra-subject maximal correlation (IISMC) method to enhance the robustness of SSVEP recognition via employing the inter- and intra-subject similarity and variability. Through efficient transfer learning, similar experience under the same task is shared across subjects. Methods: IISMC extracts subject-specific information and similar task-related information from oneself and other subjects performing the same task by maximizing the inter- and intra-subject correlation. Multiple weak classifiers are built from several existing subjects and then integrated to construct the strong classifiers by the average weighting. Finally, a powerful fusion predictor is obtained for target recognition. Results: The proposed framework is validated on a benchmark data set of 35 subjects, and the experimental results demonstrate that IISMC obtains better performance than the state of the art task-related component analysis (TRCA). Significance: The proposed method has great potential for developing high-speed BCIs.