Article
Chemistry, Analytical
Nayab Usama, Imran Khan Niazi, Kim Dremstrup, Mads Jochumsen
Summary: This study aimed to classify Error-related potentials (ErrPs) in individuals with stroke and compare different classification methods and calibration schemes. The results showed that using waveform features can improve classifier performance and interpretability, but specific calibration is needed for optimal decoding of ErrPs.
Article
Engineering, Biomedical
Quan Liu, Wenhao Zheng, Kun Chen, Li Ma, Qingsong Ai
Summary: The study proposed a novel method for ErrPs feature extraction based on AAR-CSP fusion and adaptive threshold-based SRDA classification to improve detection accuracy and reduce false positives. Experimental results demonstrated that the proposed algorithm outperforms other methods in terms of accuracy, F1-score, and false positive rate.
JOURNAL OF NEURAL ENGINEERING
(2021)
Article
Engineering, Biomedical
Susan Aliakbaryhosseinabadi, Strahinja Dosen, Andrej M. Savic, Jakob Blicher, Dario Farina, Natalie Mrachacz-Kersting
Summary: The study aimed to investigate the possibility of detecting MRCPs associated with movement intention in ALS patients at different stages of the disease. The results demonstrated high detection performance in all patients, but the classification parameters varied greatly across patients, highlighting the importance of tuning the classification pipeline to each patient individually.
JOURNAL OF NEURAL ENGINEERING
(2021)
Article
Engineering, Biomedical
Praveen K. Parashiva, A. P. Vinod
Summary: This study proposes a method of decoding two-directional hand movement using EEG data and improves the performance of BCI system through corrective step using ErrP. The cascaded scheme of direction decoding model and ErrP detection model achieves a higher decoding accuracy in online experiments.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2022)
Article
Multidisciplinary Sciences
Navneet Tibrewal, Nikki Leeuwis, Maryam Alimardani
Summary: This study evaluated the effectiveness of deep learning models in capturing motor imagery features in motor imagery brain-computer interfaces (MI-BCIs), particularly in inefficient users. The results showed that the convolutional neural network (CNN) model improved the classification accuracy for all subjects, with a significantly larger improvement for low performers. These findings suggest promise for the employment of deep learning models in future MI-BCI systems for users who are unable to produce desired sensorimotor patterns for conventional machine learning approaches.
Article
Engineering, Biomedical
Haotian Xu, Anmin Gong, Jiangong Luo, Fan Wang, Peng Ding, Yunfa Fu
Summary: This paper proposes an online neurofeedback (NF) closed-loop system to verify the utility of error-related potentials (ErrPs) in a visual-motor imagery-based brain-computer interface (BCI) system.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2023)
Article
Engineering, Biomedical
Joana Pereira, Reinmar Kobler, Patrick Ofner, Andreas Schwarz, Gernot R. Mueller-Putz
Summary: The study aimed to develop a novel BCI paradigm for asynchronous online detection of movement based on low-frequency time-domain EEG features, specifically movement-related cortical potentials. Results showed that about 54.1% of movements were correctly identified when participants initiated the tasks themselves, with all participants performing above chance-level.
JOURNAL OF NEURAL ENGINEERING
(2021)
Article
Engineering, Biomedical
Jiansheng Niu, Ning Jiang
Summary: This study analyzed the detection and classification of upper-limb movement volitions in a pseudo-online fashion. The results showed that the ensemble model achieved good performance in both detection and classification tasks, providing a promising design for movement decoding in brain-computer interfaces.
JOURNAL OF NEURAL ENGINEERING
(2022)
Article
Engineering, Biomedical
Mine Yasemin, Aniana Cruz, Urbano J. Nunes, Gabriel Pires
Summary: This study compares the impact of different classification pipelines on the detection accuracy of Error-related potentials (ErrPs), and identifies the most robust classification method and optimal parameters. The experimental results show that classification accuracy is highly dependent on user tasks and signal quality, providing important guidelines for the design of ErrP-based brain-computer interface (BCI) tasks.
JOURNAL OF NEURAL ENGINEERING
(2023)
Article
Computer Science, Cybernetics
Xiaowei Zhang, Zhongyi Zhou, Qiqi Zhao, Kechen Hou, Xiangyu Wei, Sipo Zhang, Yikun Yang, Yanmeng Cui
Summary: In this study, a domain adaptation method named DJKT is proposed for EEG-based emotion recognition tasks. The method utilizes an online updating mechanism and precise calculation of discriminative information to improve the performance and stability of the model.
IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS
(2023)
Article
Engineering, Biomedical
Catarina Lopes-Dias, Andreea Sburlea, Katharina Breitegger, Daniela Wyss, Harald Drescher, Renate Wildburger, Gernot R. Mueller-Putz
Summary: This study demonstrates the feasibility of transferring an ErrP classifier from able-bodied participants to those with SCI, allowing for asynchronous detection of ErrPs in an online setting without offline calibration and providing immediate feedback to users.
JOURNAL OF NEURAL ENGINEERING
(2021)
Article
Multidisciplinary Sciences
Yanghao Lei, Dong Wang, Weizhen Wang, Hao Qu, Jing Wang, Bin Shi
Summary: This study designed a hybrid BCI paradigm based on error-related potentials (ErrP) and motor imagery (MI), and proposed a strategy to improve the classification accuracy of single-hand open/close MI tasks by utilizing ErrP information.
Article
Engineering, Biomedical
Leisi Pei, Guang Ouyang
Summary: This study investigated the precise mapping between scalp-level electrophysiological signals and linguistic information conveyed by handwriting, offering a novel approach to developing brain-computer interfaces focusing on semantic communication.
JOURNAL OF NEURAL ENGINEERING
(2021)
Article
Computer Science, Artificial Intelligence
Zhihua Huang, Minghong Li, Wenming Zheng, Yingjie Wu, Kun Jiang, Huiru Zheng
Summary: Under certain task conditions, error-related potentials (ErrP) are elicited, representing the subject's perception of error or engagement in cognitive reinforcement learning. The detection of ErrP on a single trial basis is important for improving brain-computer interfaces (BCIs). In this paper, a novel method called window-adjusted common spatial pattern (WACSP) is proposed to detect ErrP in P300 BCI by measuring the difference in EEG signals and capturing stable spatial patterns. Testing on EEG datasets from P300 BCI experiments, WACSP outperformed commonly used methods in terms of accuracy, AUC, and F-measure.
NEURAL PROCESSING LETTERS
(2023)
Article
Engineering, Biomedical
Sebastian Olsen, Jianwei Zhang, Ken-Fu Liang, Michelle Lam, Usama Riaz, Jonathan C. Kao
Summary: The study introduces a new BCI architecture that incorporates external artificial intelligence to improve control performance. Testing with human subjects showed that the AI-BCI achieved higher information communication rates, quicker movement trajectories, improved precision control, and more efficient movement paths, across a spectrum of control quality from poor to proficient.
JOURNAL OF NEURAL ENGINEERING
(2021)
Article
Neurosciences
Song Xue, Feng Kong, Yiying Song, Jia Liu
Summary: This study used resting-state functional magnetic resonance imaging to explore the relationship between individual's spontaneous neural activity and social interaction anxiety in a nonclinical population. The results showed that social interaction anxiety was correlated with the fractional amplitude of low-frequency fluctuations in several brain regions, and that emotional intelligence partially mediated this relationship. This study provides evidence for the neural basis of social interaction anxiety in the normal population and highlights the role of emotional intelligence in this anxiety.
NEUROSCIENCE LETTERS
(2024)
Article
Neurosciences
Katsuyuki Yamaguchi, Takuya Yazawa
Summary: This study provides morphometric data on the development of the human medullary arcuate nucleus (AN) by examining the brains of preterm and perinatal infants. The results show that AN morphology demonstrates asymmetry and individual variability during the fetal period. The volume and neuronal number of AN increase exponentially with age, while neuronal density decreases exponentially. The AN may undergo neuron death and neuroblasts production after mid-gestation.
NEUROSCIENCE LETTERS
(2024)
Article
Neurosciences
Zhan Zhou, Weixin Dai, Tianxiao Liu, Min Shi, Yi Wei, Lifei Chen, Yubo Xie
Summary: Studies have shown that propofol-induced neurotoxicity is caused by disruption of mitochondrial fission and fusion, leading to an energy supply imbalance for developing neurons. Healthy mitochondria released by astrocytes can migrate to compromised neurons to mitigate propofol-induced neurotoxicity, but the exact mechanisms involved still need further clarification.
NEUROSCIENCE LETTERS
(2024)
Article
Neurosciences
An Chen, Song Hao, Yongpeng Han, Yang Fang, Yibei Miao
Summary: This study explores the efficacy of two forms of BCI attention training games and finds that physical games may be more effective than video games. The research also offers valuable insights for future game design from a neuroscience perspective.
NEUROSCIENCE LETTERS
(2024)
Article
Neurosciences
Lina Liu, Luran Liu, Yunting Lu, Tianyuan Zhang, Wenting Zhao
Summary: This study reveals that GDI1 serves as a potential diagnostic biomarker for AD and inhibition of GDI1 can attenuate Aβ-induced neurotoxicity. The findings offer new insights for the treatment of AD.
NEUROSCIENCE LETTERS
(2024)
Article
Neurosciences
Zahra Gholami, Ava Soltani Hekmat, Ali Abbasi, Kazem Javanmardi
Summary: This study investigated the effects of alamandine on allodynia in a rat model and found the presence of MrgD receptors in the vlPAG and RVM regions. Microinjection of alamandine resulted in a significant increase in paw withdrawal threshold and could be blocked by an MrgD receptor antagonist. Upregulation of MrgD receptor expression following allodynia induction suggests a potential compensatory mechanism in response to pain.
NEUROSCIENCE LETTERS
(2024)
Article
Neurosciences
Mingliang Xu, Lei Xia, Junjie Li, Yehong Du, Zhifang Dong
Summary: This study found that DHF effectively alleviates sevoflurane-induced cognitive impairment in developing mice by restoring the balance between tau O-GlcNAcylation and phosphorylation. Therefore, DHF has the potential to be a therapeutic agent for treating cognitive impairment associated with anesthetics, such as sevoflurane.
NEUROSCIENCE LETTERS
(2024)
Article
Neurosciences
Tsubasa Mitsutake, Hisato Nakazono, Takanori Taniguchi, Hisayoshi Yoshizuka, Maiko Sakamoto
Summary: The posterior parietal cortex plays a crucial role in postural stability, and transcranial electrical stimulation of this region can modulate physical control responses. This study found that cathodal stimulation significantly decreased joint angular velocity in multiple directions, while there were no significant differences with transcranial random noise stimulation.
NEUROSCIENCE LETTERS
(2024)
Article
Neurosciences
Xishuai Yang, Wei Zhang, Xueli Chang, Zuopeng Li, Runquan Du, Junhong Guo
Summary: This study aims to evaluate the efficacy of low-dose rituximab (RTX) in patients with muscle-specific kinase antibody positive myasthenia gravis (MuSK-MG). The results showed that low-dose RTX treatment led to significant improvements in clinical symptoms and quality of life for patients with MuSK-MG.
NEUROSCIENCE LETTERS
(2024)
Article
Neurosciences
Jian Zhang, Shunyuan Guo, Rong Tao, Fan Wang, Yihong Xie, Huizi Wang, Lan Ding, Yuejian Shen, Xiaoli Zhou, Junli Feng, Qing Shen
Summary: This study established an Alzheimer's disease (AD) model of zebrafish induced by AlCl3 and found that marine-derived plasmalogens (Pls) could alleviate cognitive impairments of AD zebrafish by reversing athletic impairment and altering the expression levels of genes related to oxidative stress, ferroptosis, synaptic dysfunction, and apoptosis.
NEUROSCIENCE LETTERS
(2024)
Article
Neurosciences
Lu Li, Jiaqi Ren, Qi Fang, Liqiang Yu, Jintao Wang
Summary: ICU-AW is a common and severe neuromuscular complication in critically ill patients. Electrophysiological examination is essential for accurate diagnosis and early prediction of the disease. This study aimed to establish and validate an ICU-AW predictive model in SIRS patients, providing a practical tool for early clinical prediction.
NEUROSCIENCE LETTERS
(2024)
Article
Neurosciences
Ahmad Alipour, Roghayeh Mohammadi
Summary: The present study aimed to investigate the separate and combined effects of anodal transcranial direct current stimulation (tDCS) over the primary motor cortex (M1) and left dorsolateral prefrontal cortex (F3) regions on pain relief in patients with type-2 diabetes suffering from neuropathic pain (NP). The results showed that tDCS had the potential to induce pain relief in patients with type-2 diabetes suffering from NP. The mean perceived pain intensity in the posttest was lower in the M1 stimulation group than in the F3 stimulation group. However, more trials with larger sample sizes are necessary to define clinically relevant effects.
NEUROSCIENCE LETTERS
(2024)
Article
Neurosciences
Eduardo J. Fusse, Franciele F. Scarante, Maria A. Vicente, Mariana M. Marrubia, Flavia Turcato, Davi S. Scomparin, Melissa A. Ribeiro, Maria J. Figueiredo, Tamires A. V. Brigante, Francisco S. Guimaraes, Alline C. Campos
Summary: Repeated exposure to psychosocial stress alters the endocannabinoid system and affects brain regions associated with emotional distress. Enhancing the effects of endocannabinoids through pharmacological inhibition induces an anti-stress behavioral effect, possibly mediated by the mTOR signaling pathway.
NEUROSCIENCE LETTERS
(2024)
Article
Neurosciences
Giulia Agostoni, Luca Bischetti, Federica Repaci, Margherita Bechi, Marco Spangaro, Irene Ceccato, Elena Cavallini, Luca Fiorentino, Francesca Martini, Jacopo Sapienza, Mariachiara Buonocore, Michele Francesco D'Incalci, Federica Cocchi, Carmelo Guglielmino, Roberto Cavallaro, Marta Bosia, Valentina Bambini
Summary: This study found a general impairment in humor comprehension in individuals with schizophrenia, with mental jokes being more difficult for both patients and controls. Humor comprehension was closely associated with the patients' overall pragmatic and linguistic profile, while the association with Theory of Mind (ToM) was minimal. Another notable finding was the increased appreciation of humor in individuals with schizophrenia, who rated jokes as funnier than controls did, regardless of whether they were correctly or incorrectly completed. The funniness ratings were not predicted by any measure, suggesting a dimension of humor untied to cognition or psychopathology.
NEUROSCIENCE LETTERS
(2024)
Article
Neurosciences
Xiuping Gong, Qi Li, Yang Liu
Summary: This study demonstrates that Sev targets CREBBP to inhibit ALG13 transcription, leading to hippocampal damage and cognitive impairment.
NEUROSCIENCE LETTERS
(2024)