Article
Computer Science, Artificial Intelligence
Siyang Li, Huanyu Wu, Lieyun Ding, Dongrui Wu
Summary: In this paper, a Multi-Domain Model-Agnostic Meta-Learning (MDMAML) approach is proposed to address challenging cross-subject, few-shot, and source-free classification tasks in EEG-based BCIs. The experiments demonstrated that MDMAML outperformed several classical and state-of-the-art approaches in both online and offline applications.
IEEE COMPUTATIONAL INTELLIGENCE MAGAZINE
(2022)
Article
Automation & Control Systems
Satoshi Miura, Junichi Takazawa, Yo Kobayashi, Masakatsu G. Fujie
Summary: This study investigates a brain-machine interface system that utilizes EEG and FES to detect motor commands and stimulate affected limbs in real-time. Validation experiments showed that the system significantly increased wrist accelerations, demonstrating its effectiveness in neurorehabilitation.
IEEE-ASME TRANSACTIONS ON MECHATRONICS
(2021)
Article
Engineering, Biomedical
Rui Wang, Tianyi Zhou, Zheng Li, Jing Zhao, Xiaoli Li
Summary: The study proposed an asynchronous method for steady-state visual evoked potential-based BCIs, which successfully identified idle states by fusing oscillatory and aperiodic features, leading to an improved classification performance.
JOURNAL OF NEURAL ENGINEERING
(2023)
Review
Chemistry, Analytical
Arrigo Palumbo, Vera Gramigna, Barbara Calabrese, Nicola Ielpo
Summary: Innovative aids, devices, and assistive technologies are needed to help individuals with severe disabilities live independently and improve overall health. Brain-Computer Interfaces using EEG data show potential for enhancing wheelchair control and movement in people with significant health challenges.
Article
Engineering, Biomedical
Wen Zhang, Ziwei Wang, Dongrui Wu
Summary: The paper proposes an offline unsupervised multi-source decentralized transfer (MSDT) approach, which achieves better classification performance while protecting privacy effectively.
IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING
(2022)
Article
Computer Science, Artificial Intelligence
Nedime Karakullukcu, Bulent Yilmaz
Summary: This study aims to initiate brain-computer interfaces (BCI) by identifying and characterizing movement intention using multichannel electroencephalography (EEG) signals. The Fourier-based synchrosqueezing transform (FSST) is proposed as a feature extractor to discriminate resting and motor imagery (MI) states. The study demonstrates high accuracy in distinguishing states and identifies certain channels and statistical features with statistical significance.
INTERNATIONAL JOURNAL OF NEURAL SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Tanaya Das, Lakhyajit Gohain, Nayan M. Kakoty, M. B. Malarvili, Prihartini Widiyanti, Gajendra Kumar
Summary: The brain is a unique organ that performs multiple processes simultaneously, and neurological diseases or trauma can affect limb movement. Researchers have developed a hierarchical approach using neural commands to predict and estimate finger movements, aiming to achieve anthropomorphic control for upper limb prostheses.
Article
Engineering, Biomedical
Gege Ming, Hui Zhong, Weihua Pei, Xiaorong Gao, Yijun Wang
Summary: This study proposed a new grid stimulation pattern with reduced stimulation area and low spatial contrast to achieve a balance between performance and comfort in SSVEP-based BCI systems. The optimized OFF-50% grid stimulus showed comparable online performance and improved user experience compared to the traditional Flicker-500% stimulus in multi-target BCI spellers.
JOURNAL OF NEURAL ENGINEERING
(2023)
Article
Engineering, Biomedical
Yue Zhang, Sheng Quan Xie, Chaoyang Shi, Jun Li, Zhi-Qiang Zhang
Summary: This study proposed an inter-subject transfer learning method for enhancing SSVEP recognition performance through transferred templates and transferred spatial filters. The spatial filters are trained via multiple covariance maximization to extract SSVEP-related information, and applied to templates to form new transferred templates. The transferred spatial filters are obtained via least-square regression. Experimental results validated the feasibility of the proposed method for improving SSVEP detection.
IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING
(2023)
Article
Automation & Control Systems
Sandra Cancino, Juan Manuel Lopez, Jaime F. Delgado Saa, Norelli Schettini
Summary: Brain-computer interfaces (BCIs) serve as an alternative solution for individuals with upper limb disabilities, facilitating communication between the brain and external devices. In this study, a novel technique for movement-related cortical potential estimation was introduced, and a time-frequency domain representation of the dataset was used for classification. By adopting a pretrained ConvNet AlexNet, the proposed method achieved a remarkable average accuracy of 76.0% across all five motor task categories, surpassing existing state-of-the-art techniques. Furthermore, an in-depth analysis of the convolutional layers enhanced the understanding of the classification process.
ADVANCED INTELLIGENT SYSTEMS
(2023)
Article
Engineering, Biomedical
Suayb S. Arslan, Pawan Sinha
Summary: This study aims to redefine and examine the information transmission rate (ITR) as an important metric for evaluating and comparing target identification algorithms in brain-computer interfaces (BCI). By modeling the visual pathway as a discrete memoryless channel and analyzing the asymmetry of transition statistics, the results suggest that the asymmetry of the DM channel has a significant impact on the perceived ITR. Additionally, individual input customizations can improve the perceived ITR performance.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2024)
Article
Engineering, Biomedical
Gege Ming, Weihua Pei, Hongda Chen, Xiaorong Gao, Yijun Wang
Summary: The study optimized a checkerboard-like visual stimulus for high-performance and user-friendly SSVEP-based BCI systems under low-frequency and high-frequency conditions, achieving comparable performance and enhanced visual comfort with the black-background checkerboard-like stimulus.
JOURNAL OF NEURAL ENGINEERING
(2021)
Article
Computer Science, Artificial Intelligence
Javier Fumanal-Idocin, Zdenko Takac, Javier Fernandez, Jose Antonio Sanz, Harkaitz Goyena, Ching-Teng Lin, Yu-Kai Wang, Humberto Bustince
Summary: This article introduces a method called moderate deviation functions to measure similarity and dissimilarity among interval-valued data and applies it to classify electroencephalography signals in brain-computer interfaces (BCI) systems. By using the notion of interval-valued moderate deviation function and fuzzy implication operators, the proposed method achieves better results compared to other numerical aggregation methods and interval-valued ordered weighted averaging operators in two BCI frameworks.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2022)
Article
Multidisciplinary Sciences
Jiahui Ying, Qingguo Wei, Xichen Zhou
Summary: The study introduces a transfer learning algorithm based on Riemannian geometry for c-VEP BCI, effectively reducing calibration time without sacrificing classification accuracy, resulting in improved performance. The algorithm processes data and selects subjects to enhance transfer learning performance, reducing training time while maintaining performance level for real world applications.
SCIENTIFIC REPORTS
(2022)
Article
Chemistry, Analytical
Alexander Craik, Juan Jose Gonzalez-Espana, Ayman Alamir, David Edquilang, Sarah Wong, Lianne Sanchez Rodriguez, Jeff Feng, Gerard E. Francisco, Jose L. Contreras-Vidal
Summary: We designed and validated a wireless, low-cost, easy-to-use, mobile, dry-electrode headset for scalp EEG recordings. The headset incorporates five EEG electrodes, three skin sensors, and an IMU for monitoring head movements. It operates with high SNR and CMRR and supports various coding languages. Extensive bench testing and human-subject pilot testing verify its usability and effectiveness for BCI research and neurorehabilitation.
Article
Radiology, Nuclear Medicine & Medical Imaging
Mohammad Daoud, Ayah F. Abu-Hani, Rami Alazrai
Article
Chemistry, Analytical
Mohammad I. Daoud, Abdullah Alhusseini, Mostafa Z. Ali, Rami Alazrai
Article
Chemistry, Analytical
Mohammad Daoud, Samir Abdel-Rahman, Tariq M. Bdair, Mahasen S. Al-Najar, Feras H. Al-Hawari, Rami Alazrai
Article
Computer Science, Artificial Intelligence
Feras Al-Hawari, Hala Barham, Omar Al-Sawaeer, Mai Alshawabkeh, Sahel Alouneh, Mohammad Daoud, Rami Alazrai
Summary: The study suggests design methods to enhance effective learning management capabilities within a university portal named MyGJU, which proves to be more engaging, user-friendly, and interactive compared to Moodie and in-house systems. System improvements are key to motivating both educators and students to actively engage in online learning.
PEERJ COMPUTER SCIENCE
(2021)
Article
Computer Science, Information Systems
Rami S. Al-Gharaibeh, Mostafa Z. Ali, Mohammad Daoud, Rami Alazrai, Heba Abdel-Nabi, Safaa Hriez, Ponnuthurai N. Suganthan
Summary: This paper introduces a simple yet powerful hybrid approach that combines a modified Cultural Algorithm with an Enhanced Levy Flight Search to achieve superior performance in a wide range of application-specific optimization problems. The algorithm utilizes a balanced search scheme and an updated selection function to enhance search efficiency and successful selection strategy.
INFORMATION SCIENCES
(2021)
Article
Chemistry, Multidisciplinary
Baha A. Alsaify, Mahmoud M. Almazari, Rami Alazrai, Sahel Alouneh, Mohammad I. Daoud
Summary: The study proposed a CSI-based human activity recognition system that observes changes in CSI values of wireless packets exchanged by OFDM subcarriers. By following a five-stage approach, including CSI processing, signal segmentation, feature extraction, feature selection, and SVM classifier training, it can accurately identify different activities. Experiments conducted in two different environments showed an average activity recognition accuracy of 91.27%.
APPLIED SCIENCES-BASEL
(2022)
Article
Chemistry, Analytical
Mohammad Daoud, Aamer Al-Ali, Rami Alazrai, Mahasen S. Al-Najar, Baha A. Alsaify, Mostafa Z. Ali, Sahel Alouneh
Summary: This study proposed an edge-based selection method to analyze the ROIs generated by different deep learning object-detection models with the goal of selecting the ROI that improves the localization of the tumor region.
Proceedings Paper
Engineering, Electrical & Electronic
Mohammad Daoud, Ayah Abuhani, Adnan R. Zayadeen, Rami Alazrai
Summary: This study introduces an effective method to accurately detect needles in ultrasound images based on beamformed radio frequency signals. The method is successfully applied to biopsy needles inserted in bovine liver tissue specimens, showing improved performance compared to other methods.
2021 8TH INTERNATIONAL CONFERENCE ON ELECTRICAL AND ELECTRONICS ENGINEERING (ICEEE 2021)
(2021)
Proceedings Paper
Engineering, Electrical & Electronic
Rami Alazrai, Mohammad Hababeh, Baha'A Alsaify, Mohammad Daoud
Summary: This paper proposes a method using 3D joint positions data captured by Kinect sensor to construct a view-invariant anatomical planes-based descriptor for recognizing two-person interactions from video sequences. Experimental results demonstrate the feasibility of the method in identifying interactions under different observation conditions.
2021 8TH INTERNATIONAL CONFERENCE ON ELECTRICAL AND ELECTRONICS ENGINEERING (ICEEE 2021)
(2021)
Proceedings Paper
Engineering, Biomedical
Dima Alsbeih, Mohammad Douad, Abdel-Karim Al-Tamimi, Mohammad A. Al-Jarrah
2020 IEEE 5TH MIDDLE EAST AND AFRICA CONFERENCE ON BIOMEDICAL ENGINEERING (MECBME)
(2020)
Article
Philosophy
Romina Cachia, Mohammed Aldaoud, Ayman M. Eldeib, Omar Hiari, Adiy Tweissi, Daniel Villar-Onrubia, Katherine Wimpenny, Isidro Maya Jariego
ARAUCARIA-REVISTA IBEROAMERICANA DE FILOSOFIA POLITICA Y HUMANIDADES
(2020)
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)