The significant variations in emotional patterns across different cultures pose a major challenge for cross-cultural electroencephalogram (EEG) emotion recognition. Moreover, this task must address not only differences in feature distributions among different cultures but also among individuals within the same culture. Therefore, we propose a novel domain adaptation approach, the Multi-Source Multi-Target Domain Similarity Network (MSMTDS), which treats each subject as an individual source domain or target domain. Based on the assessment of similarities across both cross-cultural and intra-cultural dimensions, the model dynamically adjusts the weights assigned to each domain, allowing similar domains to play a dominant role in training while reducing the adverse impact of dissimilar domains. Additionally, given the scarcity of EEG data, MSMTDS fully leverages all available data to maximize performance. Extensive experiments on the SEED EEG emotion datasets from three distinct cultures (China, France, and Germany) demonstrate the effectiveness of our approach, achieving state-of-the-art results.

Research on emotion recognition based on EEG signals has made significant progress. Most of the existing studies have focused on supervised learning methods, but real-life data cannot meet the requirement of high quality with labels. In addition, EEG signals have individual variability and instability, which requires transfer learning to enhance the model generalization. In this paper, we propose a multi-view self-supervised domain adaptation model that combines self-supervised learning techniques with domain-adaptive transfer learning algorithms, which can solve the last two problems mentioned above. Specifically, we add a multi-class domain discriminator to construct the adversarial relationship between the sub-networks so that distribution discrepancy of different subjects can be reduced effectively. We conduct both subject-dependent and subject-independent experiments on the SEED and SEED-IV datasets to thoroughly evaluate the performance of our model. The results show that our model achieves outstanding emotion recognition performance even with limited labeled data. In the subject-dependent experiments on both datasets, our model achieves accuracy rates of 85.91% and 87.19% respectively, surpassing the original self-supervised masked autoencoder model by about 3%. In subject-independent experiments, our model demonstrates strong data distribution adaptation capabilities, achieving an accuracy of 69.72% and 62.87%, respectively on the SEED and SEED-IV datasets using only 90 samples for subject-independent experiments. This effectively mitigates the accuracy degradation caused by differences in data distribution across subjects. Furthermore, our model is capable of extracting meaningful features from corrupted EEG data, highlighting its robustness and effectiveness.

Our visual experience in daily life are dominated by dynamic change. Decoding such dynamic information from brain activity can enhance the understanding of the brain’s visual processing system. However, previous studies predominately focus on reconstructing static visual stimuli. In this paper, we explore to decode dynamic visual perception from electroencephalography (EEG), a neuroimaging technique able to record brain activity with high temporal resolution (1000 Hz) for capturing rapid changes in brains. Our contributions are threefold: Firstly, we develop a large dataset recording signals from 20 subjects while they were watching 1400 dynamic video clips of 40 concepts. This dataset fills the gap in the lack of EEG-video pairs. Secondly, we annotate each video clips to investigate the potential for decoding some specific meta information (e.g., color, dynamic, human or not) from EEG. Thirdly, we propose a novel baseline EEG2Video for video reconstruction from EEG signals that better aligns dynamic movements with high temporal resolution brain signals by Seq2Seq architecture. EEG2Video achieves a 2-way accuracy of 79.8% in semantic classification tasks and 0.256 in structural similarity index (SSIM). Overall, our works takes an important step towards decoding dynamic visual perception from EEG signals. Our dataset and code will be released soon.

Event detection tasks can enable the quick detection of events from texts and provide powerful support for downstream natural language processing tasks. Most such methods can only detect a fixed set of predefined event classes. To extend them to detect a new class without losing the ability to detect old classes requires costly retraining of the model from scratch. Incremental learning can effectively solve this problem, but it requires abundant data of new classes. In practice, however, the lack of high-quality labeled data of new event classes makes it difficult to obtain enough data for model training. To address the above mentioned issues, we define a new task, few-shot incremental event detection, which focuses on learning to detect a new event class with limited data, while retaining the ability to detect old classes to the extent possible. We created a benchmark dataset IFSED for the few-shot incremental event detection task based on FewEvent and propose two benchmarks, IFSED-K and IFSED-KP. Experimental results show that our approach has a higher F1-score than baseline methods and is more stable.