Existing deep learning based de-raining approaches have resorted to the convolutional architectures. However, the intrinsic limitations of convolution, including local receptive fields and independence of input content, hinder the model’s ability to capture long-range and complicated rainy artifacts. To overcome these limitations, we propose an effective and efficient transformer-based architecture for the image de-raining. Firstly, we introduce general priors of vision tasks, i.e., locality and hierarchy, into the network architecture so that our model can achieve excellent de-raining performance without costly pre-training. Secondly, since the geometric appearance of rainy artifacts is complicated and of significant variance in space, it is essential for de-raining models to extract both local and non-local features. Therefore, we design the complementary window-based transformer and spatial transformer to enhance locality while capturing long-range dependencies. Besides, to compensate for the positional blindness of self-attention, we establish a separate representative space for modeling positional relationship, and design a new relative position enhanced multi-head self-attention. In this way, our model enjoys powerful abilities to capture dependencies from both content and position, so as to achieve better image content recovery while removing rainy artifacts. Experiments substantiate that our approach attains more appealing results than state-of-the-art methods quantitatively and qualitatively.

Recently, deep neural networks (DNNs) have been applied to emotion recognition tasks based on electroencephalography (EEG), and have achieved better performance than traditional algorithms. However, DNNs still have the disadvantages of too many hyperparameters and lots of training data. To overcome these shortcomings, in this paper, we propose a method for multi-channel EEG-based emotion recognition using deep forest. First, we consider the effect of baseline signal to preprocess the raw artifact-eliminated EEG signal with baseline removal. Secondly, we construct 2D frame sequences by taking the spatial position relationship across channels into account. Finally, 2D frame sequences are input into the classification model constructed by deep forest that can mine the spatial and temporal information of EEG signals to classify EEG emotions. The proposed method can eliminate the need for feature extraction in traditional methods and the classification model is insensitive to hyperparameter settings, which greatly reduce the complexity of emotion recognition. To verify the feasibility of the proposed model, experiments were conducted on two public DEAP and DREAMER databases. On the DEAP database, the average accuracies reach to 97.69% and 97.53% for valence and arousal, respectively; on the DREAMER database, the average accuracies reach to 89.03%, 90.41%, and 89.89% for valence, arousal and dominance, respectively. These results show that the proposed method exhibits higher accuracy than the state-of-art methods.

Electroencephalography (EEG) recordings are often contaminated by muscle artifacts. In the literature, a number of methods have been proposed to deal with this problem. Yet most denoising muscle artifact methods are designed for either single-channel EEG or hospital-based, high-density multichannel recordings, not the few-channel scenario seen in most ambulatory EEG instruments. In this paper, we propose utilizing interchannel dependence information seen in the few-channel situation by combining multivariate empirical mode decomposition and canonical correlation analysis (MEMD-CCA). The proposed method, called MEMD-CCA, first utilizes MEMD to jointly decompose the few-channel EEG recordings into multivariate intrinsic mode functions (IMFs). Then, CCA is applied to further decompose the reorganized multivariate IMFs into the underlying sources. Reconstructing the data using only artifact-free sources leads to artifact-attenuated EEG. We evaluated the performance of the proposed method through simulated, semisimulated, and real data. The results demonstrate that the proposed method is a promising tool for muscle artifact removal in the few-channel setting.

Electroencephalography (EEG) has been widely used for studying brain function. As cortical signals recorded by the EEG are very weak, they are often obscured by motion artifacts and various non-brain physiological activities, such as eye movement, heart rhythms, and muscle activity, adversely affecting subsequent analysis and interpretation. Over the past decades, a number of techniques have been developed for preprocessing EEG recordings to improve the signal-to-noise ratio. However, based on our extensive literature survey, despite an increasing trend recently, only 14.72% of published studies on EEG preprocessing were involved with the removal of electromygraphic (EMG) artifacts. Given that ambulatory healthcare systems are continuously emerging, artifacts induced by muscle contraction become unavoidable, whereas in the past data tended to be collected in well-controlled clinical/laboratory settings. Motivated by the fact that EMG artifact removal is becoming an important issue to be addressed, we investigated the state-of-the-art muscle artifact removal methods systematically and comparatively, from the perspective of signal processing. In this review, we first present the signal characteristics from brain and muscle activity, and highlight the importance of this issue for subsequent artifact removal. We then provide an overview and taxonomy of representative methods. Based on the results in reported studies, we describe the pros and cons of different methods and give suggestions on selecting a suitable technique in different scenarios (e.g., single-channel, few-channel, and multichannel). Finally, we discuss remaining challenges and provide feasible recommendations for further exploration in this field.

Objective: Epileptic seizure prediction based on scalp electroencephalogram (EEG) is of great significance for improving the quality of life of patients with epilepsy. In recent years, a number of studies based on deep learning methods have been proposed to address this issue and achieve excellent performance. However, most studies on epileptic seizure prediction by EEG fail to take full advantage of temporal-spatial multi-scale features of EEG signals, while EEG signals carry information in multiple temporal and spatial scales. To this end, in this study, we proposed an end-to-end framework by using a temporal-spatial multi-scale convolutional neural network with dilated convolutions for patient-specific seizure prediction. Methods: Specifically, the model divides the EEG processing pipeline into two stages: the temporal multi-scale stage and the spatial multi-scale stage. In each stage, we firstly extract the multi-scale features along the corresponding dimension. A dilated convolution block is then conducted on these features to expand our model’s receptive fields further and systematically aggregate global information. Furthermore, we adopt a feature-weighted fusion strategy based on an attention mechanism to achieve better feature fusion and eliminate redundancy in the dilated convolution block. Results: The proposed model obtains an average sensitivity of 93.3%, an average false prediction rate of 0.007 per hour, and an average proportion of time-in-warning of 6.3% testing in 16 patients from the CHB-MIT dataset with the leave-one-out method. Conclusion: Our model achieves superior performance in comparison to state-of-the-art methods, providing a promising solution for EEG-based seizure prediction.