Ten challenges for EEG-based affective computing

The emerging field of affective computing focuses on enhancing computers’ability to understand and appropriately respond to people’s affective states in human-computer interactions,and has revealed significant potential for a wide spectrum of applications.Recently,the electroencephalography(EEG)based affective computing has gained increasing interest for its good balance between mechanistic exploration and real-world practical application.The present work reviewed ten theoretical and operational challenges for the existing affective computing researches from an interdisciplinary perspective of information technology,psychology,and neuroscience.On the theoretical side,we suggest that researchers should be well aware of the limitations of the commonly used emotion models,and be cautious about the widely accepted assumptions on EEG-emotion relationships as well as the transferability of findings based on different research paradigms.On the practical side,we propose several operational recommendations for the challenges about data collection,feature extraction,model implementation,online system design,as well as the potential ethical issues.The present review is expected to contribute to an improved understanding of EEG-based affective computing and promote further applications.

脑机音乐接口研究综述

脑机音乐接口(brain-computer music interface,BCMI)是一个通过用户大脑信号来与音乐进行交互的特定类型脑机接口系统,可用于音乐创作和表演、音乐治疗、情感状态调节以及娱乐等多个方面。BCMI系统涉及从大脑信号中提取有意义的控制信息,设计响应此类信息的音乐生成技术,以及对用户进行音乐反馈等多个方面的内容。本文主要阐述了BCMI的基本概念和研究历史、音乐认知的神经机制以及典型的BCMI系统,探讨了BCMI在实际应用过程中所能发挥的作用及其未来将面临的挑战。

Transformer-based ensemble deep learning model for EEG-based emotion recognition

Emotion recognition is one of the most important research directions in the field of brain–computer interface(BCI).However,to conduct electroencephalogram(EEG)-based emotion recognition,there exist difficulties regarding EEG signal processing;moreover,the performance of classification models in this regard is restricted.To counter these issues,the 2022 World Robot Contest successfully held an affective BCI competition,thus promoting the innovation of EEG-based emotion recognition.In this paper,we propose the Transformer-based ensemble(TBEM)deep learning model.TBEM comprises two models:a pure convolutional neural network(CNN)model and a cascaded CNN-Transformer hybrid model.The proposed model won the abovementioned affective BCI competition’s final championship in the 2022 World Robot Contest,demonstrating the effectiveness of the proposed TBEM deep learning model for EEG-based emotion recognition.