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Motor imagery electroencephalogram (MI-EEG) is becoming increasingly important. This paper solves the problem of online signal recognition for motor imagery across subjects by finding common features across multiple subjects to improve the generality of the classification model. We analysed the EEG data from left/right-hand motor imagery of eight subjects and proposed a weighted time-domain (WTD) feature extraction method based on a weighted channel screening method. The classification model constructed by combining this feature extraction method with the support vector machine (SVM) classification method was faster in classification and achieved good cross-subject classification accuracy (The average offline classification accuracy was 91.39%). In this paper, an online control system for asynchronous brain-controlled wheelchairs was built with good performance. The online average motor imagery classification accuracy was 81.67%, and the average response time was 1.36s. This method contributes to bringing the online Brain-computer interface (BCI) system out of the laboratory and into wider application. [ABSTRACT FROM AUTHOR]

Design encompasses an iterative problem-solving process. While prior research has acknowledged the significance of mental iteration within this process, limited attention has been given to methods for enhancing it.This paper addresses this gap by investigating the impact of integrating artificial intelligence (AI) augmentation into the design iteration process. The primary objectives are to assess the potential augmentation of design by AI and to determine the optimal timing for AI integration. A comprehensive empirical investigation involving 48 designers and various AI-augmented conditions was conducted to substantiate the role of AI in design.The findings reveal that AI augmentation significantly enhances designer performance, improving both the efficiency of the iteration process and the quality of solutions generated in terms of depth and breadth. Comparative analyses with the Baseline demonstrate the positive impact of AI augmentation. Notably, performance under Real-time and On-demand conditions surpasses that of In-stage, emphasising the importance of selecting the right timing for AI integration.This study not only contributes theoretical support for the development of AI-augmented tools but also provides valuable insights for the advancement of design education. [ABSTRACT FROM AUTHOR]