Emotion recognition is vital for mental and physical health, but current methods often rely on active user participation (e.g., questionnaires) or wearable sensors, limiting their real-world adoption. This paper proposes a non-contact ballistocardiogram (BCG) based cross-subject emotion recognition framework. Subject-specific data distribution variations challenge model generalizability across domains. For this reason, we propose an LSCS-BDA method, extending Balanced Distribution Adaptation (BDA) with a Label Similarity Clustering for Selection (LSCS) module to optimize source domain selection. After feature extraction and baseline normalization, only the source domain individuals that are more similar to the target are included in the final source domain set through our method’s LSCS module (reducing training data to 9.78%), and finally the transfer is completed by the BDA algorithm. The experimental results show that the classification accuracy of our proposed method for three emotions of positive, negative, and neutral is 70.38%, which achieves higher accuracy and faster model training than classical transfer algorithms. The LSCS module in our method can be extended to other transfer learning methods. Our approach offers a new idea for contactless emotion recognition based on BCG and contributes to advancing its applications.

A Brain-Computer Interface (BCI) is designed for human-computer interactions without body movement. To improve the expression of input features closely related to a given BCI task, we propose a convolution network called GANet to analyze Event-Related Potential (ERP) in the BCI task. This model introduces the parallel convolution to extract multi-scale features in electroencephalogram (EEG) data. In addition, a Graph-based Attention (GA) mechanism is used to model interdependencies among different EEG channels. Experiments are conducted on a public dataset of 15 subjects in the specific-subject and cross-subject scenarios. The results indicate that the GANet achieves state-of-the-art performance with an accuracy of 99.75% in the specific-subject scenario and an accuracy of 81.37% in the cross-subject scenario. Different structures are discussed to analyze the contributions of the parallel convolution in feature extraction and the GA module in feature expression. GANet shows satisfied performance and good generalization in the BCI task. Our codes are publicly available at https://github.com/Debbie-85/GANet.