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
膀胱原位癌(bladder caner in situ,BCIS)是膀胱粘膜移行上皮内形成稍突起的绒毛状红色片块,不侵犯基底膜,具有细胞分化不良等恶性形态的非乳头状尿路上皮的早期癌变.膀胱原位癌很少单独存在,常同时伴有恶性度高的、浸润深的或分化不良...膀胱原位癌(bladder caner in situ,BCIS)是膀胱粘膜移行上皮内形成稍突起的绒毛状红色片块,不侵犯基底膜,具有细胞分化不良等恶性形态的非乳头状尿路上皮的早期癌变.膀胱原位癌很少单独存在,常同时伴有恶性度高的、浸润深的或分化不良的乳头状癌.因此,将膀胱原位癌分为两类:一类为原发性原位癌;另一类为原位癌伴其他类型癌.展开更多
Deep learning methods have been widely applied in motor imagery(MI)-based brain-computer interfaces(BCI)for decoding electroencephalogram(EEG)signals.High temporal resolution and asymmetric spatial activation are fund...Deep learning methods have been widely applied in motor imagery(MI)-based brain-computer interfaces(BCI)for decoding electroencephalogram(EEG)signals.High temporal resolution and asymmetric spatial activation are fundamental properties of EEG during MI processes.However,due to the limited receptive field of convolutional kernels,traditional convolutional neural networks(CNNs)often focus only on local features,and are insufficient to cover neural processes across different frequency bands and duration scales.This limitation hinders the effective characterization of rhythmic activity changes in MI-EEG signals over time.Additionally,MI-EEG signals exhibit significant asymmetric activation between the left and right hemispheres.Traditional spatial feature extraction methods overlook the interaction between global and local regions at the spatial scale of EEG signals,resulting in inadequate spatial representation and ultimately limiting decoding accuracy.To address these limitations,in this study,a novel deep learning network that integrates multi-modal temporal features with spatially asymmetric feature modeling was proposed.The network first extracts multi-modal temporal information from EEG data channels,and then captures global and hemispheric spatial features in the spatial dimension and fuses them through an advanced fusion layer.Global dependencies are captured using a self-attention module,and a multi-scale convolutional fusion module is introduced to explore the relationships between the two types of temporal features.The fused features are classified through a classification layer to accomplish motor imagery task classification.To mitigate the issue of limited sample size,a data augmentation strategy based on signal segmentation and recombination is designed.Experimental results on the BCI Competition IV-2a(bbic-IV-2a)and BCI Competition IV-2b(bbic-IV-2a)datasets demonstrated that the proposed method achieved superior accuracy in multi-class motor imagery classification compared with existing models.On the BCI-IV-2a dataset,it attained an average classification accuracy of 84.36%,while also showing strong performance on the binary classification BCI-IV-2b dataset.These outcomes validate the capability of the proposed network to enhance MI-EEG classification accuracy.展开更多
In order to improve the performance of the braincomputer interface system of motor imagery(MI),the optimization of the MI strategy is an effective means.Therefore,this paper proposes a motor-tactile imagery(MTI)strate...In order to improve the performance of the braincomputer interface system of motor imagery(MI),the optimization of the MI strategy is an effective means.Therefore,this paper proposes a motor-tactile imagery(MTI)strategy to improve the classification accuracy of the MI-based brain-computer interface(BCI)system by adding a corresponding strong correlation of tactile imagery to each imaginary action of the MI.Electroencephalogram(EEG)signals generated by different strategies were collected,and the corresponding classification accuracy was obtained.The experimental results showed that the performance of the MTI-training group was significantly better than that of the MI group and the MTI-without-training group.The MTI strategy proposed in this study can significantly improve the performance of BCI.展开更多
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.
文摘膀胱原位癌(bladder caner in situ,BCIS)是膀胱粘膜移行上皮内形成稍突起的绒毛状红色片块,不侵犯基底膜,具有细胞分化不良等恶性形态的非乳头状尿路上皮的早期癌变.膀胱原位癌很少单独存在,常同时伴有恶性度高的、浸润深的或分化不良的乳头状癌.因此,将膀胱原位癌分为两类:一类为原发性原位癌;另一类为原位癌伴其他类型癌.
文摘Deep learning methods have been widely applied in motor imagery(MI)-based brain-computer interfaces(BCI)for decoding electroencephalogram(EEG)signals.High temporal resolution and asymmetric spatial activation are fundamental properties of EEG during MI processes.However,due to the limited receptive field of convolutional kernels,traditional convolutional neural networks(CNNs)often focus only on local features,and are insufficient to cover neural processes across different frequency bands and duration scales.This limitation hinders the effective characterization of rhythmic activity changes in MI-EEG signals over time.Additionally,MI-EEG signals exhibit significant asymmetric activation between the left and right hemispheres.Traditional spatial feature extraction methods overlook the interaction between global and local regions at the spatial scale of EEG signals,resulting in inadequate spatial representation and ultimately limiting decoding accuracy.To address these limitations,in this study,a novel deep learning network that integrates multi-modal temporal features with spatially asymmetric feature modeling was proposed.The network first extracts multi-modal temporal information from EEG data channels,and then captures global and hemispheric spatial features in the spatial dimension and fuses them through an advanced fusion layer.Global dependencies are captured using a self-attention module,and a multi-scale convolutional fusion module is introduced to explore the relationships between the two types of temporal features.The fused features are classified through a classification layer to accomplish motor imagery task classification.To mitigate the issue of limited sample size,a data augmentation strategy based on signal segmentation and recombination is designed.Experimental results on the BCI Competition IV-2a(bbic-IV-2a)and BCI Competition IV-2b(bbic-IV-2a)datasets demonstrated that the proposed method achieved superior accuracy in multi-class motor imagery classification compared with existing models.On the BCI-IV-2a dataset,it attained an average classification accuracy of 84.36%,while also showing strong performance on the binary classification BCI-IV-2b dataset.These outcomes validate the capability of the proposed network to enhance MI-EEG classification accuracy.
文摘In order to improve the performance of the braincomputer interface system of motor imagery(MI),the optimization of the MI strategy is an effective means.Therefore,this paper proposes a motor-tactile imagery(MTI)strategy to improve the classification accuracy of the MI-based brain-computer interface(BCI)system by adding a corresponding strong correlation of tactile imagery to each imaginary action of the MI.Electroencephalogram(EEG)signals generated by different strategies were collected,and the corresponding classification accuracy was obtained.The experimental results showed that the performance of the MTI-training group was significantly better than that of the MI group and the MTI-without-training group.The MTI strategy proposed in this study can significantly improve the performance of BCI.
