Cognitive Neurodynamics最新文献_第5页

A novel adaptive lightweight multimodal efficient feature inference network ALME-FIN for EEG emotion recognition. 一种新的自适应轻量级多模态高效特征推理网络ALME-FIN。

IF 3.1 3区工程技术

Cognitive Neurodynamics Pub Date : 2025-12-01 Epub Date: 2025-01-13 DOI: 10.1007/s11571-024-10186-x

Xiaoliang Guo, Shuo Zhai

{"title":"A novel adaptive lightweight multimodal efficient feature inference network ALME-FIN for EEG emotion recognition.","authors":"Xiaoliang Guo, Shuo Zhai","doi":"10.1007/s11571-024-10186-x","DOIUrl":"10.1007/s11571-024-10186-x","url":null,"abstract":"Enhancing the accuracy of emotion recognition models through multimodal learning is a common approach. However, challenges such as insufficient modal feature learning in multimodal inference and scarcity of sample data continue to pose obstacles that need to be overcome. Therefore, we propose a novel adaptive lightweight multimodal efficient feature inference network (ALME-FIN). We introduce a time-domain lightweight adaptive network (TDLAN) and a two-dimensional dynamic focusing network (TDDFN) for multimodal feature learning. The TDLAN incorporates the denoising process as an integral part of network training, achieving adaptive denoising for each sample through the continuous optimization of the trainable filtering threshold. Simultaneously, it incorporates an interactive convolutional sampling module, enabling lightweight multi-scale feature extraction in the time domain. TDDFN effectively extracts core image features while filtering out redundancies. During the training process, the Multi-network dynamic gradient adjustment framework (MDGAF) dynamically monitors the feature learning efficacy across different modalities. It timely adjusts the training gradients of networks to allocate additional optimization time for under-optimized modalities, thereby maximizing the utilization of multimodal feature information. Moreover, the introduction of a Multi-class relationship interaction module prior to the classifier aids the model in clearly understanding the relationships among different category samples. This approach enables the model to achieve relatively accurate emotion recognition even in scenarios of limited sample availability. Compared to existing multimodal learning techniques, ALME-FIN exhibits a more efficient multimodal feature inference method that can achieve satisfactory emotional recognition performance even with a limited number of samples.","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"19 1","pages":"24"},"PeriodicalIF":3.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11729629/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143000626","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Cross-subject mental workload recognition using bi-classifier domain adversarial learning. 基于双分类器领域对抗学习的跨学科心理工作量识别。

IF 3.1 3区工程技术

Cognitive Neurodynamics Pub Date : 2025-12-01 Epub Date: 2025-01-09 DOI: 10.1007/s11571-024-10215-9

Yueying Zhou, Pengpai Wang, Peiliang Gong, Peng Wan, Xuyun Wen, Daoqiang Zhang

{"title":"Cross-subject mental workload recognition using bi-classifier domain adversarial learning.","authors":"Yueying Zhou, Pengpai Wang, Peiliang Gong, Peng Wan, Xuyun Wen, Daoqiang Zhang","doi":"10.1007/s11571-024-10215-9","DOIUrl":"10.1007/s11571-024-10215-9","url":null,"abstract":"To deploy Electroencephalogram (EEG) based Mental Workload Recognition (MWR) systems in the real world, it is crucial to develop general models that can be applied across subjects. Previous studies have utilized domain adaptation to mitigate inter-subject discrepancies in EEG data distributions. However, they have focused on reducing global domain discrepancy, while neglecting local workload-categorical domain divergence. This degrades the workload-discriminating ability of subject-invariant features. To deal with this problem, we propose a novel joint category-wise and domain-wise alignment Domain Adaptation (cdaDA) algorithm, using bi-classifier learning and domain discriminative adversarial learning. The bi-classifier learning approach is adopted to address the similarities and differences between categories, helping to align EEG data within the same mental workload categories. Additionally, the domain discriminative adversarial learning technique is adopted to consider global domain information by minimizing global domain discrepancy. By integrating both local category information and global domain information, the cdaDA model performs a coarse-to-fine alignment and achieves promising cross-subject MWR results.","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"19 1","pages":"16"},"PeriodicalIF":3.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11718037/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142969952","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Regulatory mechanism of inhibitory interneurons with time-delay on epileptic seizures under sinusoidal sensory stimulation.

IF 3.1 3区工程技术

Cognitive Neurodynamics Pub Date : 2025-12-01 Epub Date: 2025-02-05 DOI: 10.1007/s11571-025-10227-z

Zhihui Wang, Xindan Wei, Lixia Duan

{"title":"Regulatory mechanism of inhibitory interneurons with time-delay on epileptic seizures under sinusoidal sensory stimulation.","authors":"Zhihui Wang, Xindan Wei, Lixia Duan","doi":"10.1007/s11571-025-10227-z","DOIUrl":"10.1007/s11571-025-10227-z","url":null,"abstract":"Epilepsy is a neurological disorder in which complex electrophysiological processes are closely linked to inherent nonlinear kinetic properties. This study investigates the effects of sinusoidal sensory stimulation bias and time-delay on the dynamics of epileptic seizures within a corticothalamic neural network model. The results indicate that an increase in sensory stimulation bias can prematurely terminate seizures, and high-frequency stimulation can induce a phenomenon of frequency resonance. Meanwhile, discharge states transitions are associated with the emergence of bifurcation points. Time-delay exerts a significant regulatory influence on pathways with delay embedding (I2-PY), whereas its impact on pathways without delay embedding (I1-I1 and thalamic relay nucleus (TC)-I2) is negligible. Under sinusoidal sensory stimulation, the responses of three pathways (I1-I1, I1-PY, and I2-PY) associated with inhibitory interneurons reveal that the inhibitory properties of interneurons can suppress seizures; however, an excessively strong inhibitory effect may also precipitate seizures and facilitate state transitions. These findings contribute to a deeper understanding of seizure dynamics and may guide future research in the transmission and evolution of seizures.","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"19 1","pages":"37"},"PeriodicalIF":3.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11799515/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143381743","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Reciprocal causation relationship between rumination thinking and sleep quality: a resting-state fMRI study.

IF 3.1 3区工程技术

Cognitive Neurodynamics Pub Date : 2025-12-01 Epub Date: 2025-02-20 DOI: 10.1007/s11571-025-10223-3

Shiyan Yang, Xu Lei

{"title":"Reciprocal causation relationship between rumination thinking and sleep quality: a resting-state fMRI study.","authors":"Shiyan Yang, Xu Lei","doi":"10.1007/s11571-025-10223-3","DOIUrl":"10.1007/s11571-025-10223-3","url":null,"abstract":"Rumination thinking is a type of negative repetitive thinking, a tendency to constantly focus on the causes, consequences and other aspects of negative events, which has implications for a variety of psychiatric disorders. Previous studies have confirmed a strong association between rumination thinking and poor sleep or insomnia, but the direction of causality between the two is not entirely clear. This study examined the relationship between rumination thinking and sleep quality using a longitudinal approach and resting-state functional MRI data. Participants were 373 university students (males: n = 84, 18.67 ± 0.76 years old) who completed questionnaires at two time points (T1 and T2) and had resting-state MRI data collected. The results of the cross-lagged model analysis revealed a bidirectional causal relationship between rumination thinking and sleep quality. Additionally, the functional connectivity (FC) of the precuneus and lingual gyrus was found to be negatively correlated with rumination thinking and sleep quality. Furthermore, mediation analysis showed that rumination thinking at T1 fully mediated the relationship between FC of the precuneus-lingual and sleep quality at T2. These findings suggest that rumination thinking and sleep quality are causally related in a bidirectional manner and that the FC of the precuneus and lingual gyrus may serve as the neural basis for rumination thinking to predict sleep quality. Overall, this study provides new insights for enhancing sleep quality and promoting overall health.Supplementary information: The online version contains supplementary material available at 10.1007/s11571-025-10223-3.","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"19 1","pages":"41"},"PeriodicalIF":3.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11842644/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143482404","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Shared neural dynamics of facial expression processing.

IF 3.1 3区工程技术

Cognitive Neurodynamics Pub Date : 2025-12-01 Epub Date: 2025-03-04 DOI: 10.1007/s11571-025-10230-4

Madeline Molly Ely, Géza Gergely Ambrus

{"title":"Shared neural dynamics of facial expression processing.","authors":"Madeline Molly Ely, Géza Gergely Ambrus","doi":"10.1007/s11571-025-10230-4","DOIUrl":"10.1007/s11571-025-10230-4","url":null,"abstract":"The ability to recognize and interpret facial expressions is fundamental to human social cognition, enabling navigation of complex interpersonal interactions and understanding of others' emotional states. The extent to which neural patterns associated with facial expression processing are shared between observers remains unexplored, and no study has yet examined the neural dynamics specific to different emotional expressions. Additionally, the neural processing dynamics of facial attributes such as sex and identity in relation to facial expressions have not been thoroughly investigated. In this study, we investigated the shared neural dynamics of emotional face processing using an explicit facial emotion recognition task, where participants made two-alternative forced choice (2AFC) decisions on the displayed emotion. Our data-driven approach employed cross-participant multivariate classification and representational dissimilarity analysis on EEG data. The results demonstrate that EEG signals can effectively decode the sex, emotional expression, and identity of face stimuli across different stimuli and participants, indicating shared neural codes for facial expression processing. Multivariate classification analyses revealed that sex is decoded first, followed by identity, and then emotion. Emotional expressions (angry, happy, sad) were decoded earlier when contrasted with neutral expressions. While identity and sex information were modulated by image-level stimulus features, the effects of emotion were independent of visual image properties. Importantly, our findings suggest enhanced processing of face identity and sex for emotional expressions, particularly for angry faces and, to a lesser extent, happy faces.Supplementary information: The online version contains supplementary material available at 10.1007/s11571-025-10230-4.","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"19 1","pages":"45"},"PeriodicalIF":3.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11880506/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143566282","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A novel predefined-time projective synchronization strategy for multi-modal memristive neural networks.

IF 3.1 3区工程技术

Cognitive Neurodynamics Pub Date : 2025-12-01 Epub Date: 2025-03-15 DOI: 10.1007/s11571-025-10234-0

Hui Zhao, Lei Zhou, Aidi Liu, Sijie Niu, Xizhan Gao, Xiju Zong, Xin Li, Lixiang Li

{"title":"A novel predefined-time projective synchronization strategy for multi-modal memristive neural networks.","authors":"Hui Zhao, Lei Zhou, Aidi Liu, Sijie Niu, Xizhan Gao, Xiju Zong, Xin Li, Lixiang Li","doi":"10.1007/s11571-025-10234-0","DOIUrl":"10.1007/s11571-025-10234-0","url":null,"abstract":"Due to its complexity, the problem of predefined-time synchronization in multimodal memristive neural networks has rarely been explored in the literature. This paper is the first to systematically study this issue, filling a research gap in the field and further enriching the related theoretical framework. First, a novel predefined-time stability theorem is proposed, which features more lenient judgment conditions compared to existing methods. This significantly enhances the generality of the stability theorem, making it applicable to a wider range of practical engineering projects. Second, based on the proposed predefined-time stability theorem, as well as the theories of differential inclusion, Filippov solutions, and set-valued mapping, a simple and practical feedback controller is developed. This controller establishes the necessary criteria for achieving predefined-time projective synchronization in multimodal memristive neural networks. Finally, two intricate simulation experiments are carefully designed. These experiments validate the effectiveness and feasibility of the theoretical derivations presented in this paper.","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"19 1","pages":"50"},"PeriodicalIF":3.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11910476/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143647570","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Rehabilitative game-based system for enhancing physical and cognitive abilities of neurological disorders.

IF 3.1 3区工程技术

Cognitive Neurodynamics Pub Date : 2025-12-01 Epub Date: 2025-03-10 DOI: 10.1007/s11571-025-10229-x

Neven Saleh, Ahmed M Salaheldin, Maged Badawi, Ahmed El-Bialy

{"title":"Rehabilitative game-based system for enhancing physical and cognitive abilities of neurological disorders.","authors":"Neven Saleh, Ahmed M Salaheldin, Maged Badawi, Ahmed El-Bialy","doi":"10.1007/s11571-025-10229-x","DOIUrl":"10.1007/s11571-025-10229-x","url":null,"abstract":"Neurological disorders affect the nervous system and can impair physical, cognitive, or emotional functions. They often result in challenges such as movement difficulties and the inability to perform daily activities. Common conditions include stroke, traumatic brain injury, and cerebral palsy. Physical therapy is a common approach to managing these disorders. Recently, virtual reality (VR), a technology that creates interactive, simulated environments, has been used in rehabilitation. This study presents a rehabilitative game-based system to improve patients' movements and cognitive abilities. Six games were designed using the Unity platform, namely, \"Piano,\" \"Connect,\" \"Drag & Drop,\" \"Little Intelligent,\" \"Memory,\" and \"Hack & Slash.\" The Oculus Quest 2 VR headset was used to simulate the virtual environment for gaming. A mobile application called \"Recover Me\" was created to facilitate communication between patients and physiotherapists. A score index was generated for each patient, indicating the performance. It enables monitoring and assessment of the patients, leading to customizing the treatment plan based on progress. The study proposed simulating monitoring and evaluation of the patients by training an artificial neural network model to predict scores for the developed games and consequently indicate the patient's actual status. A dataset of 50 patients with different injuries was used. Results indicate patient satisfaction with gaming and enjoyment. Moreover, a regression analysis was performed to detect the progress level of each patient, indicating that 60% of the tested patients had improved. A low-cost VR game-based system has proven effective in rehabilitating neurological disorders.","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"19 1","pages":"48"},"PeriodicalIF":3.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11893925/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143604010","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Focal attention peaks and laterality bias in problem gamblers: an eye-tracking investigation.

IF 3.1 3区工程技术

Cognitive Neurodynamics Pub Date : 2025-12-01 Epub Date: 2025-03-22 DOI: 10.1007/s11571-025-10238-w

Yayoi Shigemune, Akira Midorikawa

{"title":"Focal attention peaks and laterality bias in problem gamblers: an eye-tracking investigation.","authors":"Yayoi Shigemune, Akira Midorikawa","doi":"10.1007/s11571-025-10238-w","DOIUrl":"10.1007/s11571-025-10238-w","url":null,"abstract":"Problem gambling has been associated with attentional biases toward gambling-related stimuli, but less is known about how problem gamblers distribute their visual attention during gambling tasks. This eye-tracking study investigated differences in sustained visual attention between problem gamblers (PGs; n = 22) and non-problem gamblers (NPGs; n = 22) during a gambling task using neutral picture pairs. While total gaze time toward stimuli did not differ between the groups, PGs showed distinctive characteristics in their visual attentional allocation. Specifically, two-sample t-tests revealed that PGs exhibited significantly higher focal attention to right-sided stimuli in central zones (0-25 pixels) during decision-making, while NPGs demonstrated greater left-sided peripheral attention (76-100 pixels) during feedback. These patterns were further supported by a three-way ANOVA showing a significant group × zone × laterality interaction in the decision phase, confirming that PGs exhibited significantly higher right-sided attention in the central zone (0-25 and 26-50 pixels), while NPGs showed a tendency toward greater left-sided attention in the peripheral zone (76-100 pixels). Additionally, PGs demonstrated stronger rightward attentional bias in both phases. These differences in visual attention were associated with higher behavioral-approach-system, reward sensitivity, and sensation-seeking scores among PGs. The findings suggest that PGs exhibit distinctive characteristics in terms of sustained visual attention during gambling-related decision-making, even when viewing neutral stimuli. This distinctive distribution of visual attention may reflect fundamental differences in information processing and potential hemispheric imbalances in attention control mechanisms among PGs.","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"19 1","pages":"51"},"PeriodicalIF":3.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11929661/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143699809","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The length and the width of the human brain circuit connections are strongly correlated. 人类大脑回路连接的长度和宽度是紧密相关的。

IF 3.1 3区工程技术

Cognitive Neurodynamics Pub Date : 2025-12-01 Epub Date: 2025-01-09 DOI: 10.1007/s11571-024-10201-1

Dániel Hegedűs, Vince Grolmusz

{"title":"The length and the width of the human brain circuit connections are strongly correlated.","authors":"Dániel Hegedűs, Vince Grolmusz","doi":"10.1007/s11571-024-10201-1","DOIUrl":"10.1007/s11571-024-10201-1","url":null,"abstract":"The correlations of several fundamental properties of human brain connections are investigated in a consensus connectome, constructed from 1064 braingraphs, each on 1015 vertices, corresponding to 1015 anatomical brain areas. The properties examined include the edge length, the fiber count, or edge width, meaning the number of discovered axon bundles forming the edge and the occurrence number of the edge, meaning the number of individual braingraphs where the edge exists. By using our previously published robust braingraphs at https://braingraph.org, we have prepared a single consensus graph from the data and compared the statistical similarity of the edge occurrence numbers, edge lengths, and fiber counts of the edges. We have found a strong positive Spearman correlation between the edge occurrence numbers and the fiber count numbers, showing that statistically, the most frequent cerebral connections have the largest widths, i.e., the fiber count. We have found a negative Spearman correlation between the fiber lengths and fiber counts, showing that, typically, the shortest edges are the widest or strongest by their fiber counts. We have also found a negative Spearman correlation between the occurrence numbers and the edge lengths: it shows that typically, the long edges are infrequent, and the frequent edges are short.","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"19 1","pages":"21"},"PeriodicalIF":3.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11717732/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142969960","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Cross-session SSVEP brainprint recognition using attentive multi-sub-band depth identity embedding learning network. 基于关注多子带深度身份嵌入学习网络的SSVEP脑印识别。

IF 3.1 3区工程技术

Cognitive Neurodynamics Pub Date : 2025-12-01 Epub Date: 2025-01-09 DOI: 10.1007/s11571-024-10192-z

Chengxian Gu, Xuanyu Jin, Li Zhu, Hangjie Yi, Honggang Liu, Xinyu Yang, Fabio Babiloni, Wanzeng Kong

{"title":"Cross-session SSVEP brainprint recognition using attentive multi-sub-band depth identity embedding learning network.","authors":"Chengxian Gu, Xuanyu Jin, Li Zhu, Hangjie Yi, Honggang Liu, Xinyu Yang, Fabio Babiloni, Wanzeng Kong","doi":"10.1007/s11571-024-10192-z","DOIUrl":"10.1007/s11571-024-10192-z","url":null,"abstract":"Brainprint recognition technology, regarded as a promising biometric technology, encounters challenges stemming from the time-varied, low signal-to-noise ratio of brain signals, such as electroencephalogram (EEG). Steady-state visual evoked potentials (SSVEP) exhibit high signal-to-noise ratio and frequency locking, making them a promising paradigm for brainprint recognition. Consequently, the extraction of time-invariant identity information from SSVEP EEG signals is essential. In this paper, we propose an Attentive Multi-sub-band Depth Identity Embedding Learning Network for stable cross-session SSVEP brainprint recognition. To address the issue of low recognition accuracy across sessions, we introduce the Sub-band Attentive Frequency mechanism, which integrates the frequency-domain relevant characteristics of the SSVEP paradigm and focuses on exploring depth-frequency identity embedding information. Also, we employ Attentive Statistic Pooling to enhance the stability of frequency domain feature distributions across sessions. Extensive experimentation and validation were conducted on two multi-session SSVEP benchmark datasets. The experimental results show that our approach outperforms other state-of-art models on 2-second samples across sessions and has the potential to serve as a benchmark in multi-subject biometric recognition systems.","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"19 1","pages":"15"},"PeriodicalIF":3.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11717760/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142969951","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0