Multimodal treatment for spinal cord injury: a sword of neuroregeneration upon neuromodulation

Spinal cord injury is linked to the interruption of neural pathways,which results in irreversible neural dysfunction.Neural repair and neuroregeneration are critical goals and issues for rehabilitation in spinal cord injury,which require neural stem cell repair and multimodal neuromodulation techniques involving personalized rehabilitation strategies.Besides the involvement of endogenous stem cells in neurogenesis and neural repair,exogenous neural stem cell transplantation is an emerging effective method for repairing and replacing damaged tissues in central nervous system diseases.However,to ensure that endogenous or exogenous neural stem cells truly participate in neural repair following spinal cord injury,appropriate interventional measures(e.g.,neuromodulation)should be adopted.Neuromodulation techniques,such as noninvasive magnetic stimulation and electrical stimulation,have been safely applied in many neuropsychiatric diseases.There is increasing evidence to suggest that neuromagnetic/electrical modulation promotes neuroregeneration and neural repair by affecting signaling in the nervous system;namely,by exciting,inhibiting,or regulating neuronal and neural network activities to improve motor function and motor learning following spinal cord injury.Several studies have indicated that fine motor skill rehabilitation training makes use of residual nerve fibers for collateral growth,encourages the formation of new synaptic connections to promote neural plasticity,and improves motor function recovery in patients with spinal cord injury.With the development of biomaterial technology and biomechanical engineering,several emerging treatments have been developed,such as robots,brain-computer interfaces,and nanomaterials.These treatments have the potential to help millions of patients suffering from motor dysfunction caused by spinal cord injury.However,large-scale clinical trials need to be conducted to validate their efficacy.This review evaluated the efficacy of neural stem cells and magnetic or electrical stimulation combined with rehabilitation training and intelligent therapies for spinal cord injury according to existing evidence,to build up a multimodal treatment strategy of spinal cord injury to enhance nerve repair and regeneration.

情感脑机接口研究综述

情感智能研究的一个重要目标是让机器对人的情绪进行实时、准确的判别,并在此基础上进行更加自然、友好的人机交互。情感脑机接口是一种对人的情绪进行识别和(或)调控的脑机接口,是目前实现情感智能的主要途径。阐述了情感脑机接口的基本概念、工作原理、研究现状、代表性应用和发展趋势,探讨了情感脑机接口在通用人工智能发展过程中所能发挥的作用以及情感脑机接口研究面临的挑战。

用异质迁移学习构建跨被试脑电情感模型

由于脑电信号的个体差异性和非平稳特性对情感模型性能产生影响,如何构建跨被试脑电情感模型是情感脑-机接口领域的一个重要研究方向.本文提出一种新的从眼睛的扫视轨迹进行知识迁移的异质迁移学习方法,以提升跨被试脑电情感模型的性能.该方法的主要神经生理学依据是,被试的视觉注视诱发了大脑特定的神经活动,而这些神经活动产生的脑电信号可以为情绪识别提供重要的情境线索.为了量化不同被试之间的域差异,我们引入了基于扫视轨迹和基于脑电信号的核矩阵,并提出了改进的直推式参数迁移学习算法,以实现跨被试脑电情感模型的构建.该方法与传统方法相比,具有两个优点:一是利用了目标被试容易获取的眼动追踪数据进行被试迁移,二是在目标被试只有眼动追踪数据的情况下,仍然能够从其他被试的历史数据中学到脑电信号的情绪类别判别信息.为了验证所提方法的有效性,我们对本文提出的方法与已有的迁移方法在三类情绪识别的脑电和眼动数据集上进行了系统的对比实验研究.实验结果表明,基于眼动轨迹的迁移模型取得了与基于脑电信号的迁移模型相当的识别性能.相对于传统的通用分类器50.46%的平均准确率,基于眼动轨迹的迁移模型的平均准确率达到了69.72%.

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.

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.

基于多模态情感脑机接口的抑郁症客观评估与调控治疗

本文探讨多模态情感脑机接口在抑郁症客观评估和难治性抑郁症脑深部电刺激治疗中的应用。在抑郁症客观评估方面,首先将传统的量表转化成情感交互试验。基于多模态情感脑机接口,同步采集脑电和眼动等多模态数据。通过多模态深度学习和迁移学习等深度学习技术,构建可精确区分抑郁状态的客观评估系统。在难治性抑郁症脑深部电刺激神经调控治疗方面,基于多模态情感脑机接口和强化学习算法,实现脑深部电刺激刺激参数的自动调节和个性化,提升难治性抑郁症治疗的效果。