Cognitive Neurodynamics最新文献

{"title":"Emotion analysis of EEG signals using proximity-conserving auto-encoder (PCAE) and ensemble techniques.","authors":"R Mathumitha, A Maryposonia","doi":"10.1007/s11571-024-10187-w","DOIUrl":"10.1007/s11571-024-10187-w","url":null,"abstract":"<p><p>Emotion recognition plays a crucial role in brain-computer interfaces (BCI) which helps to identify and classify human emotions as positive, negative, and neutral. Emotion analysis in BCI maintains a substantial perspective in distinct fields such as healthcare, education, gaming, and human-computer interaction. In healthcare, emotion analysis based on electroencephalography (EEG) signals is deployed to provide personalized support for patients with autism or mood disorders. Recently, several deep learning (DL) based approaches have been developed for accurate emotion recognition tasks. Yet, previous works often struggle with poor recognition accuracy, high dimensionality, and high computational time. This research work designed an innovative framework named Proximity-conserving Auto-encoder (PCAE) for accurate emotion recognition based on EEG signals and resolves challenges faced by traditional emotion analysis techniques. For preserving local structures among the EEG data and reducing dimensionality, the proposed PCAE approach is introduced and it captures the essential features related to emotional states. The EEG data are collected from the EEG Brainwave dataset using a Muse EEG headband and applying preprocessing steps to enhance signal quality. The proposed PCAE model incorporates multiple convolution and deconvolution layers for encoding and decoding and deploys a Local Proximity Preservation Layer for preserving local correlations in the latent space. In addition, it develops a Proximity-conserving Squeeze-and-Excitation Auto-encoder (PC-SEAE) model to further improve the feature extraction ability of the PCAE technique. The proposed PCAE technique utilizes Maximum Mean Discrepancy (MMD) regularization to decrease the distribution discrepancy between input data and the extracted features. Moreover, the proposed model designs an ensemble model for emotion categorization that incorporates a one-versus-support vector machine (SVM), random forest (RF), and Long Short-Term Memory (LSTM) networks by utilizing each classifier's strength to enhance classification accuracy. Further, the performance of the proposed PCAE model is evaluated using diverse performance measures and the model attains outstanding results including accuracy, precision, and Kappa coefficient of 98.87%, 98.69%, and 0.983 respectively. This experimental validation proves that the proposed PCAE framework provides a significant contribution to accurate emotion recognition and classification systems.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"19 1","pages":"32"},"PeriodicalIF":3.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11757850/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143045767","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}

{"title":"Pragmatic information of aesthetic appraisal.","authors":"Peter Beim Graben","doi":"10.1007/s11571-025-10225-1","DOIUrl":"10.1007/s11571-025-10225-1","url":null,"abstract":"<p><p>A phenomenological model for aesthetic appraisal is proposed in terms of pragmatic information for a dynamic update semantics over belief states of an aesthetic appreciator. The model qualitatively correlates with aesthetic pleasure ratings in an experimental study on cadential effects in Western tonal music, conducted by Cheung et al. (Curr Biol 29(23):4084-4092.e4, 2019). Finally, related computational and neurodynamical accounts are discussed.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"19 1","pages":"39"},"PeriodicalIF":3.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11803012/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143381740","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}

{"title":"Partial face visibility and facial cognition: event-related potential and eye tracking investigation.","authors":"Ingon Chanpornpakdi, Yodchanan Wongsawat, Toshihisa Tanaka","doi":"10.1007/s11571-025-10231-3","DOIUrl":"10.1007/s11571-025-10231-3","url":null,"abstract":"<p><p>Face masks became a part of everyday life during the SARS-CoV-2 pandemic. Previous studies showed that the face cognition mechanism involves holistic face processing, and the absence of face features could lower the cognition ability. This is opposed to the experience during the pandemic, when people could correctly recognize faces, although the mask covered a part of the face. This paper clarifies the partial face cognition mechanism of the full and partial faces based on the electroencephalogram (EEG) and eye-tracking data. We observed two event-related potentials, P3a in the frontal lobe and P3b in the parietal lobe, as subcomponents of P300. The amplitude of both P3a and P3b were lowered when the eyes were invisible, and the amplitude of P3a evoked by the nose covered was larger than the full face. The eye-tracking data showed that 16 out of 18 participants focused on the eyes associated with the EEG results. Our results demonstrate that the eyes are the most crucial feature of facial cognition. Moreover, the face with the nose covered might enhance cognition ability due to the visual working memory capacity. Our experiment also shows the possibility of people recognizing faces using both holistic and structural face processing. In addition, we calculated canonical correlation using the P300 and the total fixation duration of the eye-tracking data. The results show high correlation in the cognition of the full face and the face and nose covered ( <math> <mrow><msub><mi>R</mi> <mi>c</mi></msub> <mo>=</mo> <mn>0.93</mn></mrow> </math> ) which resembles the masked face. The finding suggests that people can recognize the masked face as well as the full face in similar cognition patterns.</p><p><strong>Supplementary information: </strong>The online version contains supplementary material available at 10.1007/s11571-025-10231-3.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"19 1","pages":"47"},"PeriodicalIF":3.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11893966/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143604009","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}

{"title":"Individuals with high autistic traits exhibit altered interhemispheric brain functional connectivity patterns.","authors":"Junling Wang, Ludan Zhang, Sitong Chen, Huiqin Xue, Minghao Du, Yunuo Xu, Shuang Liu, Dong Ming","doi":"10.1007/s11571-024-10213-x","DOIUrl":"10.1007/s11571-024-10213-x","url":null,"abstract":"<p><p>Individuals with high autistic traits (AT) encounter challenges in social interaction, similar to autistic persons. Precise screening and focused interventions positively contribute to improving this situation. Functional connectivity analyses can measure information transmission and integration between brain regions, providing neurophysiological insights into these challenges. This study aimed to investigate the patterns of brain networks in high AT individuals to offer theoretical support for screening and intervention decisions. EEG data were collected during a 4-min resting state session with eyes open and closed from 48 participants. Using the Autism Spectrum Quotient (AQ) scale, participants were categorized into the high AT group (HAT, n = 15) and low AT groups (LAT, n = 15). We computed the interhemispheric and intrahemispheric alpha coherence in two groups. The correlation between physiological indices and AQ scores was also examined. Results revealed that HAT exhibited significantly lower alpha coherence in the homologous hemispheres of the occipital cortex compared to LAT during the eyes-closed resting state. Additionally, significant negative correlations were observed between the degree of AT (AQ scores) and the alpha coherence in the occipital cortex, as well as in the right frontal and left occipital regions. The findings indicated that high AT individuals exhibit decreased connectivity in the occipital region, potentially resulting in diminished ability to process social information from visual inputs. Our discovery contributes to a deeper comprehension of the neural underpinnings of social challenges in high AT individuals, providing neurophysiological signatures for screening and intervention strategies for this population.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"19 1","pages":"9"},"PeriodicalIF":3.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11717774/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142969955","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}

{"title":"A multidimensional adaptive transformer network for fatigue detection.","authors":"Dingming Wu, Liu Deng, Quanping Lu, Shihong Liu","doi":"10.1007/s11571-025-10224-2","DOIUrl":"10.1007/s11571-025-10224-2","url":null,"abstract":"<p><p>Variations in information processing patterns induced by operational directives under varying fatigue conditions within the cerebral cortex can be identified and analyzed through electroencephalogram (EEG) signals. The inherent complexity of EEG signals poses significant challenges in the effective detection of driver fatigue across diverse task scenarios. Recent advancements in deep learning, particularly the Transformer architecture, have shown substantial benefits in the retrieval and integration of multi-dimensional information. Nevertheless, the majority of current research primarily focuses on the application of Transformers for temporal information extraction, often overlooking other dimensions of EEG data. In response to this gap, the present study introduces a Multidimensional Adaptive Transformer Recognition Network specifically tailored for the identification of driving fatigue states. This network features a multidimensional Transformer architecture for feature extraction that adaptively assigns weights to various information dimensions, thereby facilitating feature compression and the effective extraction of structural information. This methodology ultimately enhances the model's accuracy and generalization capabilities. The experimental results indicate that the proposed methodology outperforms existing research methods when utilized with the SEED-VIG and SFDE datasets. Additionally, the analysis of multidimensional and frequency band features highlights the ability of the proposed network framework to elucidate differences in various multidimensional features during the identification of fatigue states.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"19 1","pages":"43"},"PeriodicalIF":3.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11842677/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143482399","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}

{"title":"Classification for Alzheimer's disease and frontotemporal dementia via resting-state electroencephalography-based coherence and convolutional neural network.","authors":"Rundong Jiang, Xiaowei Zheng, Jiamin Sun, Lei Chen, Guanghua Xu, Rui Zhang","doi":"10.1007/s11571-025-10232-2","DOIUrl":"10.1007/s11571-025-10232-2","url":null,"abstract":"<p><p>The study aimed to diagnose of Alzheimer's Disease (AD) and Frontotemporal Dementia (FTD) based on brain functional connectivity features extracted via resting-state Electroencephalographic (EEG) signals, and subsequently developed a convolutional neural network (CNN) model, Coherence-CNN, for classification. First, a publicly available dataset of EEG resting state-closed eye recordings containing 36 AD subjects, 23 FTD subjects, and 29 cognitively normal (CN) subjects was used. Then, coherence metrics were utilized to quantify brain functional connectivity, and the differences in coherence between groups across various frequency bands were investigated. Next, spectral clustering was used to analyze variations and differences in brain functional connectivity related to disease states, revealing distinct connectivity patterns in brain electrode position maps. The results demonstrated that brain functional connectivity between different regions was more robust in the CN group, while the AD and FTD groups exhibited various degrees of connectivity decline, reflecting the pronounced differences in connectivity patterns associated with each condition. Furthermore, Coherence-CNN was developed based on CNN and the feature of coherence for three-class classification, achieving a commendable accuracy of 94.32% through leave-one-out cross-validation. This study revealed that Coherence-CNN demonstrated significant performance for distinguishing AD, FTD, and CN groups, supporting the disorder of brain functional connectivity in AD and FTD.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"19 1","pages":"46"},"PeriodicalIF":3.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11880455/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143572409","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}

{"title":"On hyper-parameter selection for guaranteed convergence of RMSProp.","authors":"Jinlan Liu, Dongpo Xu, Huisheng Zhang, Danilo Mandic","doi":"10.1007/s11571-022-09845-8","DOIUrl":"10.1007/s11571-022-09845-8","url":null,"abstract":"<p><p>RMSProp is one of the most popular stochastic optimization algorithms in deep learning applications. However, recent work has pointed out that this method may not converge to the optimal solution even in simple convex settings. To this end, we propose a time-varying version of RMSProp to fix the non-convergence issues. Specifically, the hyperparameter, <math><msub><mi>β</mi> <mi>t</mi></msub> </math> , is considered as a time-varying sequence rather than a fine-tuned constant. We also provide a rigorous proof that the RMSProp can converge to critical points even for smooth and non-convex objectives, with a convergence rate of order <math><mrow><mi>O</mi> <mo>(</mo> <mo>log</mo> <mi>T</mi> <mo>/</mo> <msqrt><mi>T</mi></msqrt> <mo>)</mo></mrow> </math> . This provides a new understanding of RMSProp divergence, a common issue in practical applications. Finally, numerical experiments show that time-varying RMSProp exhibits advantages over standard RMSProp on benchmark datasets and support the theoretical results.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":" ","pages":"3227-3237"},"PeriodicalIF":3.1,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11655782/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41393024","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}

{"title":"Enhanced brain network flexibility by physical exercise in female methamphetamine users.","authors":"Xiaoying Qi, Yingying Wang, Yingzhi Lu, Qi Zhao, Yifan Chen, Chenglin Zhou, Yuguo Yu","doi":"10.1007/s11571-022-09848-5","DOIUrl":"10.1007/s11571-022-09848-5","url":null,"abstract":"<p><p>Methamphetamine (MA) abuse is increasing worldwide, and evidence indicates that MA causes degraded cognitive functions such as executive function, attention, and flexibility. Recent studies have shown that regular physical exercise can ameliorate the disturbed functions. However, the potential functional network alterations resulting from physical exercise have not been extensively studied in female MA users. We collaborated with a drug rehabilitation center for this study to investigate changes in brain activity and network dynamics after two types of acute and long-term exercise interventions based on 64-channel electroencephalogram recordings of seventy-nine female MA users, who were randomly divided into three groups: control group (CG), dancing group (DG) and bicycling group (BG). Over a 12-week period, we observed a clear drop in the rate of brain activity in the exercise groups, especially in the frontal and temporal regions in the DG and the frontal and occipital regions in the BG, indicating that exercise might suppress hyperactivity and that different exercise types have distinct impacts on brain networks. Importantly, both exercise groups demonstrated enhancements in brain flexibility and network connectivity entropy, particularly after the acute intervention. Besides, a significantly negative correlation was found between Δattentional bias and Δbrain flexibility after acute intervention in both DG and BG. Analysis strongly suggested that exercise programs can reshape patient brains into a highly energy-efficient state with a lower activity rate but higher information communication capacity and more plasticity for potential cognitive functions. These results may shed light on the potential therapeutic effects of exercise interventions for MA users.</p><p><strong>Supplementary information: </strong>The online version contains supplementary material available at 10.1007/s11571-022-09848-5.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":" ","pages":"3209-3225"},"PeriodicalIF":3.1,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11655724/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43833994","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}

{"title":"Multiple-timescale dynamics, mixed mode oscillations and mixed affective states in a model of bipolar disorder.","authors":"Efstathios Pavlidis, Fabien Campillo, Albert Goldbeter, Mathieu Desroches","doi":"10.1007/s11571-022-09900-4","DOIUrl":"10.1007/s11571-022-09900-4","url":null,"abstract":"<p><p>Mixed affective states in bipolar disorder (BD) is a common psychiatric condition that occurs when symptoms of the two opposite poles coexist during an episode of mania or depression. A four-dimensional model by Goldbeter (Progr Biophys Mol Biol 105:119-127, 2011; Pharmacopsychiatry 46:S44-S52, 2013) rests upon the notion that manic and depressive symptoms are produced by two competing and auto-inhibited neural networks. Some of the rich dynamics that this model can produce, include complex rhythms formed by both small-amplitude (subthreshold) and large-amplitude (suprathreshold) oscillations and could correspond to mixed bipolar states. These rhythms are commonly referred to as mixed mode oscillations (MMOs) and they have already been studied in many different contexts by Bertram (Mathematical analysis of complex cellular activity, Springer, Cham, 2015), (Petrov et al. in J Chem Phys 97:6191-6198, 1992). In order to accurately explain these dynamics one has to apply a mathematical apparatus that makes full use of the timescale separation between variables. Here we apply the framework of multiple-timescale dynamics to the model of BD in order to understand the mathematical mechanisms underpinning the observed dynamics of changing mood. We show that the observed complex oscillations can be understood as MMOs due to a so-called <i>folded-node singularity</i>. Moreover, we explore the bifurcation structure of the system and we provide possible biological interpretations of our findings. Finally, we show the robustness of the MMOs regime to stochastic noise and we propose a minimal three-dimensional model which, with the addition of noise, exhibits similar yet purely noise-driven dynamics. The broader significance of this work is to introduce mathematical tools that could be used to analyse and potentially control future, more biologically grounded models of BD.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"18 6","pages":"3239-3257"},"PeriodicalIF":3.1,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11655942/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142876472","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}

{"title":"Pupil dilation and behavior as complementary measures of fear response in Mice.","authors":"Jing Sun, Lin Zhu, Xiaojing Fang, Yong Tang, Yuci Xiao, Shaolei Jiang, Jianbang Lin, Yuantao Li","doi":"10.1007/s11571-024-10180-3","DOIUrl":"10.1007/s11571-024-10180-3","url":null,"abstract":"<p><p>The precise assessment of emotional states in animals under the combined influence of multiple stimuli remains a challenge in neuroscience research. In this study, multi-dimensional assessments, including high-precision pupil tracking and behavioral analysis, were conducted to investigate the combined effects of fear stimuli and drug manipulation on emotional responses in mice. Mice exposed to foot shocks showed typical freezing and flight behaviors, but neither of these measures could effectively distinguish between dexmedetomidine, isoflurane, and saline groups. In contrast, the change in pupil diameter clearly distinguished the groups. Our results showed that fear stimulation could induce significant pupil dilation, and dexmedetomidine-isoflurane combined stimulation could significantly inhibit this response, but isoflurane anesthesia alone could not achieve good inhibitory effect. This further demonstrates the superiority of pupil data in resolving the effects of combined stimuli on emotional states and the potential of multidimensional assessments to refine animal disease models and drug evaluations.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"18 6","pages":"4047-4054"},"PeriodicalIF":3.1,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11655993/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142876517","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}