作者: 令人苦惱 時間: 2025-3-21 20:38 作者: 白楊魚 時間: 2025-3-22 02:27 作者: 團(tuán)結(jié) 時間: 2025-3-22 06:06
Rich Dynamics Induced by Synchronization Varieties in the Coupled Thalamocortical Circuitry Modelinteractive modes and weights. These results provide the complementary synchronization effects and conditions for the basic 3-node motifs. This may facilitate to construct the architecture based on patient EEG data and reveal the abnormal information expression of epileptic oscillatory network.作者: Kidney-Failure 時間: 2025-3-22 09:10
Functional Connectivity Analysis Using the Oddball Auditory Paradigm for Attention Tasksents are carried out in subjects responding to an oddball paradigm. Results show statistical differences between target and non-target labels, making the proposed methodology a suitable alternative to support cognitive neurophysiological applications.作者: 惡名聲 時間: 2025-3-22 13:32 作者: 善變 時間: 2025-3-22 17:07 作者: ARIA 時間: 2025-3-22 23:28
Emotion Recognition Based on Gramian Encoding Visualizationn 3 real world datasets to learn high-level features from GAF images. The classification results of our method are better than the state-of-the-art approaches. This method makes visualization based emotion recognition become possible, which is beneficial in the real medical fields, such as making cognitive disease diagnosis more intuitively.作者: Obstreperous 時間: 2025-3-23 05:05
Using the Partial Directed Coherence to Understand Brain Functional Connectivity During Movement Imaot. Our preliminary results show that it is possible to relate the changes in the magnitude of the PDC to different connectivity patterns in the measurement network we have considered, and those changes are in agreement with brain functional connectivity that has been reported in other studies based mainly in magnetic resonance imaging.作者: Mucosa 時間: 2025-3-23 06:18 作者: 駭人 時間: 2025-3-23 11:53
Complex Mathematical ActivitiesThese aspects are backed by recent neuroscientific models and literature. Simulation experiments have been performed by creating scenarios for student learning through rewards and controlling their motivation through regulation. Mathematical analysis is provided to verify the dynamic properties of the model.作者: 宇宙你 時間: 2025-3-23 15:25 作者: Vo2-Max 時間: 2025-3-23 21:50 作者: 人工制品 時間: 2025-3-23 22:12 作者: Neutropenia 時間: 2025-3-24 02:39
Education in a Post-industrial Worldusing simulated and real EEG signals. It can be concluded that the enhanced IRA-L1 method with the frequency-spatio-temporal stage improves the quality of the brain reconstruction performance in terms of the Wasserstein metric, in comparison with the other methods, for both simulated and real EEG signals.作者: ARK 時間: 2025-3-24 07:28
https://doi.org/10.1057/978-1-137-52095-1strained minimization problem, we develop an efficient numerical algorithm based on the alternating direction method of multipliers (ADMM). Numerical experiments have shown the great potential of the proposed method in terms of accuracy and focality.作者: palpitate 時間: 2025-3-24 13:09 作者: 極深 時間: 2025-3-24 18:48 作者: 空氣 時間: 2025-3-24 20:44
Current Design with Minimum Error in Transcranial Direct Current Stimulationstrained minimization problem, we develop an efficient numerical algorithm based on the alternating direction method of multipliers (ADMM). Numerical experiments have shown the great potential of the proposed method in terms of accuracy and focality.作者: MANIA 時間: 2025-3-24 23:23 作者: 草率男 時間: 2025-3-25 07:24
Complex Mathematical Activities different rhythms and time scales are different. Through the results of the classification accuracy of different rhythms and different time scales, the optimal rhythm and time scale of the RT-ERM model are obtained, and the classification of emotional EEG is carried out by the best time scales corr作者: PANG 時間: 2025-3-25 09:27
https://doi.org/10.1007/978-1-4842-2214-0ation correspond to district cognitive processes at the neural level. Importantly, the proposed framework provides a novel approach that can facilitate the study of the neural correlates of spatial cognition.作者: 完成才能戰(zhàn)勝 時間: 2025-3-25 15:37
MXenes and MXenes-based Compositesferent subjects, quantifying for the first time how attribute weightings for the same word are modified by context. Such dynamic representations of meaning could be used in future natural language processing systems, allowing them to mirror human performance more accurately.作者: Haphazard 時間: 2025-3-25 16:07 作者: 小步舞 時間: 2025-3-25 20:08 作者: GILD 時間: 2025-3-26 03:43
https://doi.org/10.1057/978-1-137-59194-4rding trials required for speech decoding using a machine learning algorithm. We used a wavelet filter for generating the denoised neural features to train an Artificial Neural Network (ANN) for speech decoding. We found that wavelet based denoising increased the SNR of the neural signal prior to an作者: 退潮 時間: 2025-3-26 07:05 作者: flavonoids 時間: 2025-3-26 11:41 作者: 晚間 時間: 2025-3-26 12:50
An EEG-Based Emotion Recognition Model with Rhythm and Time Characteristics different rhythms and time scales are different. Through the results of the classification accuracy of different rhythms and different time scales, the optimal rhythm and time scale of the RT-ERM model are obtained, and the classification of emotional EEG is carried out by the best time scales corr作者: 構(gòu)成 時間: 2025-3-26 16:50
Perspective Taking vs Mental Rotation: CSP-Based Single-Trial Analysis for Cognitive Process Disambiation correspond to district cognitive processes at the neural level. Importantly, the proposed framework provides a novel approach that can facilitate the study of the neural correlates of spatial cognition.作者: Ischemic-Stroke 時間: 2025-3-26 21:09
Combining fMRI Data and Neural Networks to Quantify Contextual Effects in the Brainferent subjects, quantifying for the first time how attribute weightings for the same word are modified by context. Such dynamic representations of meaning could be used in future natural language processing systems, allowing them to mirror human performance more accurately.作者: 未開化 時間: 2025-3-27 03:26 作者: abracadabra 時間: 2025-3-27 06:44 作者: chandel 時間: 2025-3-27 11:58 作者: nonplus 時間: 2025-3-27 17:08
Brain Informatics978-3-030-05587-5Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: Grasping 時間: 2025-3-27 21:45
Lecture Notes in Computer Sciencehttp://image.papertrans.cn/b/image/190196.jpg作者: 發(fā)微光 時間: 2025-3-27 23:49 作者: 不如樂死去 時間: 2025-3-28 02:20
978-3-030-05586-8Springer Nature Switzerland AG 2018作者: HAIL 時間: 2025-3-28 07:40
https://doi.org/10.1007/978-3-319-26255-0osis and so on, due to poor direct understanding of physiological signals. In view of the fact that people’s ability to understand two-dimensional images is much higher than one-dimensional signals, we use Gramian Angular Fields to visualize time series signals. GAF images are represented as a Grami作者: CREEK 時間: 2025-3-28 12:04 作者: Angiogenesis 時間: 2025-3-28 17:26 作者: V洗浴 時間: 2025-3-28 20:54 作者: 否認(rèn) 時間: 2025-3-29 02:59
https://doi.org/10.1057/978-1-137-52095-1research and clinic applications due to its convenient implementation and modulation of the brain functionality. In this paper, we propose a novel multi-electrode tDCS current configuration model that minimizes the total error under the safety constraints. After rewriting the model as a linearly con作者: 把手 時間: 2025-3-29 05:07
https://doi.org/10.1057/978-1-137-52095-1signing the set of Regions-of-interest (ROIs) over the cortical surface, (ii) estimating the ROI time-courses using a dynamic inverse problem formulation, (iii) estimating the pairwise functional connectivity between ROIs, and (iv) feeding a Support Vector Machine Classifier with the estimated conne作者: Control-Group 時間: 2025-3-29 07:15 作者: 流逝 時間: 2025-3-29 13:56
https://doi.org/10.1007/978-1-4842-2214-0formation belonging to each EEG channel. Nevertheless, several studies have characterized cognitive functions as synchronized brain networks depending on the underlying neural interactions. As a result, connectivity analysis provides essential information for improving both the interpretation and in作者: Misnomer 時間: 2025-3-29 18:29
https://doi.org/10.1007/978-1-4842-2214-0t an imagined spatial perspective) represent the two most well-known and used types of spatial transformation. Yet, these two spatial transformations are conceptually, visually, and mathematically equivalent. Thus, an active debate in the field is whether these two types of spatial transformations a作者: Offensive 時間: 2025-3-29 22:01
Peng Wu,Hao Xu,Le Xu,Yueming Liu,Mingyuan Hement imagery tasks, as well as the directionality of such coupling. For this, we consider the multivariate autoregressive model of the signals from a selection of eleven EEG channels that are assumed as a fully-connected measurement network. Then, we aim to find differences in connectivity patterns 作者: maculated 時間: 2025-3-30 00:45 作者: AORTA 時間: 2025-3-30 04:37 作者: 甜得發(fā)膩 時間: 2025-3-30 09:20 作者: enchant 時間: 2025-3-30 14:07 作者: 修飾語 時間: 2025-3-30 20:15
Emotion Recognition Based on Gramian Encoding Visualizationosis and so on, due to poor direct understanding of physiological signals. In view of the fact that people’s ability to understand two-dimensional images is much higher than one-dimensional signals, we use Gramian Angular Fields to visualize time series signals. GAF images are represented as a Grami作者: Intrepid 時間: 2025-3-30 20:58 作者: ALE 時間: 2025-3-31 02:25 作者: Liability 時間: 2025-3-31 05:53 作者: scoliosis 時間: 2025-3-31 12:39
Current Design with Minimum Error in Transcranial Direct Current Stimulationresearch and clinic applications due to its convenient implementation and modulation of the brain functionality. In this paper, we propose a novel multi-electrode tDCS current configuration model that minimizes the total error under the safety constraints. After rewriting the model as a linearly con作者: 吸引力 時間: 2025-3-31 15:42
Assessment of Source Connectivity for Emotional States Discriminationsigning the set of Regions-of-interest (ROIs) over the cortical surface, (ii) estimating the ROI time-courses using a dynamic inverse problem formulation, (iii) estimating the pairwise functional connectivity between ROIs, and (iv) feeding a Support Vector Machine Classifier with the estimated conne
