The issue of electroencephalogram (EEG)-based emotion recognition has great academic and practical significance. Currently, there are numerous research trying to address this issue in the literature. Particularly, transfer learning has gradually become a new methodological trend for the issue in company with the popularity of deep learning. Motivated by capturing the research panorama, summarizing the technological essence, and forecasting the advancement tendency of transfer learning for EEG-based emotion recognition, this article contributes a review work. This work mainly includes five aspects: 1) introducing the issue of EEG-based emotion recognition and expounding the importance of transfer learning for it; 2) analyzing the transfer learning framework and comparing it with the traditional ones; 3) elucidating the issue difficulties and explaining the suitability and capability of transfer learning for this issue; 4) summarizing, categorizing, and exemplifying the typical transfer learning methods for this issue; and 5) clarifying the methodological merits, discussing the challenging problems, and predicting the prospective development of transfer learning for the issue. We expect these contributions can inspire innovation and reformation of the transfer learning methodology for EEG-based emotion recognition as well as other relevant topics in the not-so-far future.

Insect pest management is one of the main ways to improve the crop yield and quality in agriculture and it can accurately and timely detect insect pests, which is of great significance to agricultural production. In the past, most insect pest detection tasks relied on the experience of agricutural experts, which is time-consuming, laborious and subjective. In rencent years, various intelligent methods have emerged for detection. This paper employs three frontier Deep Convolutional Neural Network (DCNN) models—Faster-RCNN, Mask-RCNN and Yolov5, for efficient insect pest detection. In addition, we made two coco datasets by ourselves on the basis of Baidu AI insect detection dataset and IP102 dataset, and compared these three frontier deep learning models on the two coco datasets. In terms of Baidu AI insect detection dataset whose background is simple, the experimental results strongly recommend Yolov5 for the insect pest detection, because its accuracy reaches above 99% while Faster-RCNN’s and Mask-RCNN’s reach above 98%. Meanwhile, Yolov5 has the faster computational speed than Faster-RCNN and Mask-RCNN. Comparatively speaking, with regard to the IP102 dataset whose background is complex and categories are abundant, Faster-RCNN and Mask-RCNN have the higher accuracy, reaching 99%, than Yolov5 whose accuracy is about 97%.

Physiological and pathological information within electrocardiogram (ECG) is crucial for the diagnosis of heart disease. Computer-aided diagnosis for ECG signals has drawn growing research attention up to date. Automatic ECG analysis mainly includes signal de-noising, singularity detection, and arrhythmia classification. These three aspects are relevant: signal de-noising can work as the preprocessing stage for singularity detection and arrhythmia classification, while singularity detection is indispensable for the subsequent arrhythmia classification based on heart beats. Wavelet based methods play important roles in the three aspects, but these methods are scattered and unorganized in the literature. In order to manifest the value of these methods, this work contributes an overview and taxonomy on them. This work does comprehensive summary and systematic categorization on the methods for signal de-noising, singularity detection, and arrhythmia classification according to the deep analysis of their methodological characteristics. By doing so, this work not only uncovers the inner mechanism why wavelet based methods are suitable for ECG analysis, but also reveals the modelling principles these methods potentially follow. Finally, this work has provided an outlook for the developing prospect of “wavelets for ECG” in the future.

Electroencephalogram (EEG)-based emotion recognition is of great significance for aiding in clinical diagnosis, treatment, nursing and rehabilitation. Current research on this issue mainly focuses on utilizing various network architectures with different types of neurons to exploit the temporal, spectral, or spatial information from EEG for classification. However, most studies fail to take full advantage of the useful Temporal-Spectral-Spatial (TSS) information of EEG signals. In this paper, we propose a novel and effective Fractal Spike Neural Network (Fractal-SNN) scheme, which can exploit the multi-scale TSS information from EEG, for emotion recognition. Our designed Fractal-SNN block in the proposed scheme approximately simulates the biological neural connection structures based on spiking neurons and a new fractal rule, allowing for the extraction of discriminative multi-scale TSS features from the signals. Our designed training technique, inverted drop-path, can enhance the generalization ability of the Fractal-SNN scheme. Sufficient experiments on four public benchmark databases, DREAMER, DEAP, SEED-IV and MPED, under the subject-dependent protocols demonstrate the superiority of the proposed scheme over the related advanced methods. In summary, the proposed scheme provides a promising solution for EEG-based emotion recognition.