The development of non-invasive brain-computer interfaces(BCIs)relies on multidisciplinary integration across neuroscience,artificial intelligence,flexible electronics,and systems engineering.Recent advances in deep l...The development of non-invasive brain-computer interfaces(BCIs)relies on multidisciplinary integration across neuroscience,artificial intelligence,flexible electronics,and systems engineering.Recent advances in deep learning have significantly improved the accuracy and robustness of neural signal decoding.Parallel progress in electrode design—particularly through the use of flexible and stretchable materials like nanostructured conductors and novel fabrication strategies—has enhanced wearability and operational stability.Nevertheless,key challenges persist,including individual variability,biocompatibility limitations,and susceptibility to interference in complex environments.Further validation and optimization are needed to address gaps in generalization capability,long-term reliability,and real-world operational robustness.This review systematically examines the representative progress in neural decoding algorithms and flexible bioelectronic platforms over the past decade,highlighting key design principles,material innovations,and integration strategies that are poised to advance non-invasive BCI capabilities.It also discusses the importance of multimodal data fusion,hardware-software co-optimization,and closed-loop control strategies.Furthermore,the review discusses the application potential and associated engineering challenges of this technology in clinical rehabilitation and industrial translation,aiming to provide a reference for advancing non-invasive BCIs toward practical and scalable deployment.展开更多
Pain,as a common symptom,seriously affects the patient's health.The aim of this work was to study the physiological responses of the brain and identify the features of Electroencephalography(EEG)signals related to...Pain,as a common symptom,seriously affects the patient's health.The aim of this work was to study the physiological responses of the brain and identify the features of Electroencephalography(EEG)signals related to friction pain.The results showed that the primary brain activation evoked by friction pain was located in the Prefrontal Cortex(PFC).The activation area decreased,and the negative activation intensity in the PFC region increased with increasing intensity of pain.The inhibitory interactions between different brain regions,especially between the PFC and primary somatosensory cortex(SI)regions were enhanced,and excitatory-inhibitory connections between the medial and lateral pain pathways were balanced during pain perception.The percentage power spectral density of theαrhythm(Dα),dominant singularity strength(αpeak)and longest vertical line(Vmax)of EEG signals induced by pain significantly decreased,and the percent-age power spectral density of theβrhythm(Dβ)significantly increased.The combination of multiple features of Dα,Dβ,αpeak and Vmax could significantly improve the average recognition accuracy of different pain states.This study elucidated the neural processing mechanisms of friction-induced pain,and EEG features associated with friction pain were extracted and recognized.It was helpful to study the brain feedback mechanisms of pain and control signals of Brain-Computer Interface(BCI)system related to pain.展开更多
The steady-state visual evoked potential(SSVEP)-based speller has emerged as a widely adopted paradigm in current brain–computer interface(BCI) systems due to its rapid processing and consistent performance across di...The steady-state visual evoked potential(SSVEP)-based speller has emerged as a widely adopted paradigm in current brain–computer interface(BCI) systems due to its rapid processing and consistent performance across different individuals. Calibration-free SSVEP algorithms, as opposed to their calibration-based counterparts, offer clear and intuitive mathematical principles, making them accessible to novice developers. During the World Robot Contest(WRC)2022, participants in the undergraduate category utilized various approaches to accomplish target detection in the calibration-free setting, successfully implementing the algorithms using MATLAB.The winning approach achieved an average information transfer rate of 198.94 bits/min in the final test, which is notably high given the calibration-free scenario. This paper presents an introduction to the underlying principles of the selected methods, accompanied by a comparison of their effectiveness through analysis of results from both the final test and offline experiments. Additionally, we propose that the youth competition of WRC could serve as an ideal starting point for beginners interested in studying and developing their own BCI systems.展开更多
Brain-computer interfaces(BCIs)represent an emerging technology that facilitates direct communication between the brain and external devices.In recent years,numerous review articles have explored various aspects of BC...Brain-computer interfaces(BCIs)represent an emerging technology that facilitates direct communication between the brain and external devices.In recent years,numerous review articles have explored various aspects of BCIs,including their fundamental principles,technical advancements,and applications in specific domains.However,these reviews often focus on signal processing,hardware development,or limited applications such as motor rehabilitation or communication.This paper aims to offer a comprehensive review of recent electroencephalogram(EEG)-based BCI applications in the medical field across 8 critical areas,encompassing rehabilitation,daily communication,epilepsy,cerebral resuscitation,sleep,neurodegenerative diseases,anesthesiology,and emotion recognition.Moreover,the current challenges and future trends of BCIs were also discussed,including personal privacy and ethical concerns,network security vulnerabilities,safety issues,and biocompatibility.展开更多
基金the National Natural Science Foundation of China for Distinguished Young Scholars(62325403)the National Natural Science Foundation of China(62504103 and 82002454)+4 种基金the Basic Research Program of Jiangsu(BK20251214)the Natural Science Foundation of Jiangsu Province(BK20230498)the China Postdoctoral Science Foundation under Grant Number 2025T180143 and 2025M770547the Medical Scientific Research Project of Jiangsu Health Commission(ZD2021011)the Jiangsu Funding Program for Excellent Postdoctoral Talent(2024ZB427)。
文摘The development of non-invasive brain-computer interfaces(BCIs)relies on multidisciplinary integration across neuroscience,artificial intelligence,flexible electronics,and systems engineering.Recent advances in deep learning have significantly improved the accuracy and robustness of neural signal decoding.Parallel progress in electrode design—particularly through the use of flexible and stretchable materials like nanostructured conductors and novel fabrication strategies—has enhanced wearability and operational stability.Nevertheless,key challenges persist,including individual variability,biocompatibility limitations,and susceptibility to interference in complex environments.Further validation and optimization are needed to address gaps in generalization capability,long-term reliability,and real-world operational robustness.This review systematically examines the representative progress in neural decoding algorithms and flexible bioelectronic platforms over the past decade,highlighting key design principles,material innovations,and integration strategies that are poised to advance non-invasive BCI capabilities.It also discusses the importance of multimodal data fusion,hardware-software co-optimization,and closed-loop control strategies.Furthermore,the review discusses the application potential and associated engineering challenges of this technology in clinical rehabilitation and industrial translation,aiming to provide a reference for advancing non-invasive BCIs toward practical and scalable deployment.
基金National Natural Science Foundation of China(grant number:52375224)Natural Science Foundation of Jiangsu Province(grant number:BK20242086)+2 种基金Priority Academic Program Development of Jiangsu Higher Education Institutions,a project supported by"the Fundamental Research Funds for the Central Universities"(grant number:202410976)Graduate Innovation Program of China University of Mining and Technology(grant number:2024WLKXJ075)Postgraduate Research&Practice Innovation Program of Jiangsu Province(grant number:KYCX24_2719).
文摘Pain,as a common symptom,seriously affects the patient's health.The aim of this work was to study the physiological responses of the brain and identify the features of Electroencephalography(EEG)signals related to friction pain.The results showed that the primary brain activation evoked by friction pain was located in the Prefrontal Cortex(PFC).The activation area decreased,and the negative activation intensity in the PFC region increased with increasing intensity of pain.The inhibitory interactions between different brain regions,especially between the PFC and primary somatosensory cortex(SI)regions were enhanced,and excitatory-inhibitory connections between the medial and lateral pain pathways were balanced during pain perception.The percentage power spectral density of theαrhythm(Dα),dominant singularity strength(αpeak)and longest vertical line(Vmax)of EEG signals induced by pain significantly decreased,and the percent-age power spectral density of theβrhythm(Dβ)significantly increased.The combination of multiple features of Dα,Dβ,αpeak and Vmax could significantly improve the average recognition accuracy of different pain states.This study elucidated the neural processing mechanisms of friction-induced pain,and EEG features associated with friction pain were extracted and recognized.It was helpful to study the brain feedback mechanisms of pain and control signals of Brain-Computer Interface(BCI)system related to pain.
基金Open Project of Key Laboratory of Intelligent Computing&Signal Processing,Ministry of Education(Grant No.2020A005)。
文摘The steady-state visual evoked potential(SSVEP)-based speller has emerged as a widely adopted paradigm in current brain–computer interface(BCI) systems due to its rapid processing and consistent performance across different individuals. Calibration-free SSVEP algorithms, as opposed to their calibration-based counterparts, offer clear and intuitive mathematical principles, making them accessible to novice developers. During the World Robot Contest(WRC)2022, participants in the undergraduate category utilized various approaches to accomplish target detection in the calibration-free setting, successfully implementing the algorithms using MATLAB.The winning approach achieved an average information transfer rate of 198.94 bits/min in the final test, which is notably high given the calibration-free scenario. This paper presents an introduction to the underlying principles of the selected methods, accompanied by a comparison of their effectiveness through analysis of results from both the final test and offline experiments. Additionally, we propose that the youth competition of WRC could serve as an ideal starting point for beginners interested in studying and developing their own BCI systems.
基金supported by the National Key R&D Program of China(2021YFF1200602)the National Science Fund for Excellent Overseas Scholars(0401260011)+3 种基金the National Defense Science and Technology Innovation Fund of Chinese Academy of Sciences(c02022088)the Tianjin Science and Technology Program(20JCZDJC00810)the National Natural Science Foundation of China(82202798)the Shanghai Sailing Program(22YF1404200).
文摘Brain-computer interfaces(BCIs)represent an emerging technology that facilitates direct communication between the brain and external devices.In recent years,numerous review articles have explored various aspects of BCIs,including their fundamental principles,technical advancements,and applications in specific domains.However,these reviews often focus on signal processing,hardware development,or limited applications such as motor rehabilitation or communication.This paper aims to offer a comprehensive review of recent electroencephalogram(EEG)-based BCI applications in the medical field across 8 critical areas,encompassing rehabilitation,daily communication,epilepsy,cerebral resuscitation,sleep,neurodegenerative diseases,anesthesiology,and emotion recognition.Moreover,the current challenges and future trends of BCIs were also discussed,including personal privacy and ethical concerns,network security vulnerabilities,safety issues,and biocompatibility.
