Cognitive Neurodynamics最新文献

{"title":"EEG-based schizophrenia detection: integrating discrete wavelet transform and deep learning.","authors":"Dayanand Dhongade, Kamal Captain, Suresh Dahiya","doi":"10.1007/s11571-025-10248-8","DOIUrl":"https://doi.org/10.1007/s11571-025-10248-8","url":null,"abstract":"<p><p>Millions of people worldwide are afflicted with the psychological disease Schizophrenia (SZ). Symptoms of SZ include delusions, hallucinations, disoriented speech, and confused thinking. This disorder is manually diagnosed by a skilled medical practitioner. Nowadays, machine learning and deep learning techniques based on electroencephalogram (EEG) signals have been proposed to support medical practitioners. This paper proposes a deep learning system and a wavelet transform-based computer-aided detection method for detecting SZ disorder. The proposed technique aims to present a highly accurate EEG signal-based SZ detection technique. In this work, we first separate the EEG signal into sub-bands and extract the features for each sub-band using the Discrete Wavelet Transform (DWT). We have explored different mother wavelets and decomposition levels for the DWT setting; it is found that the Daubechies (db4) wavelet with 7-level decomposition performs the best for SZ detection. After obtaining the gathered features, the multilayer perceptron neural network (MLP) applies them to differentiate between SZ patients and healthy controls (HC). We validate our proposed automated SZ detection method using two publicly available datasets, Dataset-1 (DS1) with 81 records (32-HC and 49-SZ) and Dataset-2 (DS2) with 28 records (14-HC and 14-SZ), respectively. Compared with previous work, our proposed model surpasses the state-of-the-art technique for SZ detection. Our classification accuracy has increased, achieving an accuracy of 99.61% and 99.12% for DS1 and DS2. Our proposed method for identifying SZ using EEG signals is more reliable and accurate and is ready to support physicians in diagnosing SZ.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"19 1","pages":"62"},"PeriodicalIF":3.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12006578/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143981893","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":"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":"Review of directional leads, stimulation patterns and programming strategies for deep brain stimulation.","authors":"Yijie Zhou, Yibo Song, Xizi Song, Feng He, Minpeng Xu, Dong Ming","doi":"10.1007/s11571-024-10210-0","DOIUrl":"10.1007/s11571-024-10210-0","url":null,"abstract":"<p><p>Deep brain stimulation (DBS) is a well-established treatment for both neurological and psychiatric disorders. Directional DBS has the potential to minimize stimulation-induced side effects and maximize clinical benefits. Many new directional leads, stimulation patterns and programming strategies have been developed in recent years. Therefore, it is necessary to review new progress in directional DBS. This paper summarizes progress for directional DBS from the perspective of directional DBS leads, stimulation patterns, and programming strategies which are three key elements of DBS systems. Directional DBS leads are reviewed in electrode design and volume of tissue activated visualization strategies. Stimulation patterns are reviewed in stimulation parameters and advances in stimulation patterns. Programming strategies are reviewed in computational modeling, monopolar review, direction indicators and adaptive DBS. This review will provide a comprehensive overview of primary directional DBS leads, stimulation patterns and programming strategies, making it helpful for those who are developing DBS systems.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"19 1","pages":"33"},"PeriodicalIF":3.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11757656/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143045804","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":"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":"Dynamics of structured complex-valued Hopfield neural networks.","authors":"Rama Murthy Garimella, Marcos Eduardo Valle, Guilherme Vieira, Anil Rayala, Dileep Munugoti","doi":"10.1007/s11571-025-10257-7","DOIUrl":"10.1007/s11571-025-10257-7","url":null,"abstract":"<p><p>In this paper, we explore the dynamics of structured complex-valued Hopfield neural networks (CvHNNs), which arise when the synaptic weight matrix possesses specific structural properties. We begin by analyzing CvHNNs with a Hermitian synaptic weight matrix and establish the existence of four-cycle dynamics in CvHNNs with skew-Hermitian weight matrices operating synchronously. Furthermore, we introduce two new classes of complex-valued matrices: braided Hermitian and braided skew-Hermitian matrices. We demonstrate that CvHNNs utilizing these matrix types exhibit cycles of length eight when operating in full parallel update mode. Finally, we conduct extensive computational experiments on synchronous CvHNNs, exploring other synaptic weight matrix structures. The findings provide a comprehensive overview of the dynamics of structured CvHNNs, offering insights that may contribute to developing improved associative memory models when integrated with suitable learning rules.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"19 1","pages":"74"},"PeriodicalIF":3.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12089572/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144119116","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":"The mechanism of a 16-Week Baduanjin intervention in altering neural inhibition responses to food cues in healthy adults - an ERP study.","authors":"Chenping Zhang, Xiawen Li, Liyan Wang, Hongbiao Wang","doi":"10.1007/s11571-025-10270-w","DOIUrl":"10.1007/s11571-025-10270-w","url":null,"abstract":"<p><p>The positive impact of exercise on inhibitory control has been validated in numerous studies; however, few studies have explored the effect of long-term exercise on food-related inhibitory control. The aim of the present study was to explore the effects of long-term exercise on the inhibitory response to food cues and the underlying neural mechanism. We recruited 51 healthy participants who were pseudo randomly divided into an exercise group and a non-exercise group, body mass index (BMI), age and sex. The exercise group underwent 16 weeks of Baduanjin intervention at a frequency of 3 days/week for 60 min/session. The assessment consisted of a personal information questionnaire, a hunger questionnaire and Go/NoGo tasks. The electroencephalography (EEG) data were recorded as the participants performed the Go/NoGo task. Only EEG data for the NoGo stimuli were analysed. The participants were asked to complete the entire procedure at baseline and within 1 week after the end of the exercise. There were 26 participants in the exercise group (age: 18.90 ± 0.49; number of females (%): 23 (88.46%); BMI: 21.79 ± 4.40) and 25 participants in the control group (age: 19.19 ± 0.63; number of females (%):20 (80.00%); BMI: 21.26 ± 3.36).A significant interaction effect of group and time on NoGo accuracy and N2/P2/P3 amplitudes was observed. Specifically, 16 weeks of Baduanjin exercise significantly increased NoGo accuracy, decreased the N2 amplitude and increased the P2/P3 amplitudes for food-related NoGo stimuli. We speculated that exercise may improve inhibitory control by reasonably regulating the allocation of attentional resources and improving the strategic orientation of attention.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"19 1","pages":"82"},"PeriodicalIF":3.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12106255/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144173273","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":"Brain-region specific epileptic seizure detection through EEG dynamics: integrating spectral features, SMOTE and long short-term memory networks.","authors":"Indu Dokare, Sudha Gupta","doi":"10.1007/s11571-025-10250-0","DOIUrl":"https://doi.org/10.1007/s11571-025-10250-0","url":null,"abstract":"<p><p>Investigating neural dynamics through EEG signals offers valuable insights into brain activity, especially for automated seizure detection. The identification of epileptogenic zones is crucial for effective epilepsy treatment, particularly in surgical planning. This work introduces a novel method for seizure detection using EEG signals, designed to benefit clinicians by integrating spectral features with Long Short-Term Memory (LSTM) networks, enhanced by brain region-specific analysis. This research work captures critical frequency domain characteristics by extracting pivotal spectral features from EEG data, thereby improving the signal representation for LSTM networks. Additionally, this proposed work has employed the Synthetic Minority Over-sampling Technique (SMOTE) to handle the class imbalance problem. Furthermore, a comprehensive spatial analysis of EEG signals is performed to evaluate performance variations across distinct brain regions, enabling targeted region-wise analysis. This strategy effectively reduces the number of channels required, minimizing the need to process all 22 channels specified in the CHB-MIT dataset, thus significantly decreasing computational complexity while preserving high seizure detection performance. This work has obtained a mean value of accuracy of 95.43%, precision of 95.46%, sensitivity of 95.59%, F1-score of 95.48%, and specificity of 95.25% for the brain region providing the best performance for seizure discrimination. The results demonstrate that integrating spectral features and LSTM, augmented by spatial insights, enhances seizure detection performance and hence assists in identifying epileptogenic regions. This tool enhances clinical applications by improving diagnostic precision, personalized treatment strategies, and supporting precise surgical planning for epilepsy, ensuring safer resection and better outcomes.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"19 1","pages":"67"},"PeriodicalIF":3.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12049356/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143983265","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":"Characteristics analysis of a single electromechanical arm driven by a functional neural circuit.","authors":"Xinlin Song, Ya Wang, Zhenhua Yu, Feifei Yang","doi":"10.1007/s11571-025-10218-0","DOIUrl":"https://doi.org/10.1007/s11571-025-10218-0","url":null,"abstract":"<p><p>From a biological viewpoint, the muscle tissue produces efficient gait behavior that can be adjusted by neural signals. From the physical viewpoint, the limb movement can be simulated by applying a neural circuit to control the artificial electromechanical arm (EA). In this paper, a functional neural circuit is used to excite a single EA, the load circuit attached to the moving beam is driven by a neural circuit, and the Ampere force is activated by the load circuit to control the artificial EA. The dynamic equations of the neural circuit are derived using Kirchhoff's theorem, while the energy and motion equations of the beam are computed through the application of mechanics and related theoretical principles. Furthermore, the dynamic characteristics of the functional neural circuit forced EA are analyzed. The results indicate that the beam movement can be controlled by the electrical activity of this functional neural circuit. This work will provide theoretical guidance to build the electromechanical device for complex gait behaviors.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"19 1","pages":"65"},"PeriodicalIF":3.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12011675/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143980560","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}