Article
Chemistry, Multidisciplinary
Shiyu Luo, Miguel Angrick, Christopher Coogan, Daniel N. Candrea, Kimberley Wyse-Sookoo, Samyak Shah, Qinwan Rabbani, Griffin W. Milsap, Alexander R. Weiss, William S. Anderson, Donna C. Tippett, Nicholas J. Maragakis, Lora L. Clawson, Mariska J. Vansteensel, Brock A. Wester, Francesco V. Tenore, Hynek Hermansky, Matthew S. Fifer, Nick F. Ramsey, Nathan E. Crone
Summary: Brain-computer interfaces (BCIs) can accurately and reliably control assistive devices for patients with neurological disorders. This study demonstrates that a chronically implanted ECoG-based speech BCI can accurately detect and decode speech commands without the need for retraining or recalibration, supporting its feasibility for long-term use.
Article
Mathematics
Zhen Peng, Hongyi Li, Di Zhao, Chengwei Pan
Summary: In this study, a non-linear dimensionality reduction algorithm based on neural networks is proposed to construct a more discriminative low-dimensional symmetric positive definite (SPD) covariance matrix manifold. The proposed manifold network is implemented using a Siamese architecture and the effectiveness of the method is verified through numerical experiments.
Article
Chemistry, Multidisciplinary
Arnau Dillen, Fakhreddine Ghaffari, Olivier Romain, Bram Vanderborght, Uros Marusic, Sidney Grospretre, Ann Nowe, Romain Meeusen, Kevin De Pauw
Summary: Brain-computer interfaces (BCIs) can enable individuals to interact with devices based on their brain activity. However, the high costs associated with research-grade electroencephalogram (EEG) acquisition devices make them impractical for everyday use. This study demonstrates that decoding movement intention from a limited number of sensors is feasible, opening up the possibility of using commercial sensor devices for BCI control.
APPLIED SCIENCES-BASEL
(2023)
Article
Multidisciplinary Sciences
Sean L. Metzger, Kaylo T. Littlejohn, Alexander B. Silva, David A. Moses, Margaret P. Seaton, Ran Wang, Maximilian E. Dougherty, Jessie R. Liu, Peter Wu, Michael A. Berger, Inga Zhuravleva, Adelyn Tu-Chan, Karunesh Ganguly, Gopala K. Anumanchipalli, Edward F. Chang
Summary: This research uses high-density surface recordings of the speech cortex to achieve real-time decoding of brain activity into text, speech sounds, and facial movements. Deep learning models are trained to accurately and rapidly convert neural data into various outputs. This multimodal speech neuroprosthetic approach has substantial potential to restore full and embodied communication for individuals with severe paralysis.
Article
Biology
Davide Borra, Valeria Mondini, Elisa Magosso, Gernot R. Mueller-Putz
Summary: This study aims to decode hand kinematics (position and velocity) in Brain-Computer Interfaces (BCIs) using an interpretable convolutional neural network (ICNN). The ICNN outperformed other decoders, balancing performance, size, and training time. It also allowed interpretation of the most relevant spectral and spatial features.
COMPUTERS IN BIOLOGY AND MEDICINE
(2023)
Article
Computer Science, Artificial Intelligence
Hanwen Wang, Yu Qi, Lin Yao, Yueming Wang, Dario Farina, Gang Pan
Summary: Brain-computer interfaces (BCIs) provide a direct connection between the brain and external devices, showing great potential for assistive and rehabilitation technologies. In this study, a human-machine joint learning framework is proposed to accelerate the learning process in BCIs by guiding users to generate brain signals towards an optimal distribution. Experimental results demonstrated that the proposed joint learning process outperformed traditional approaches in terms of learning efficiency and effectiveness.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Imam Mustafa Kamal, Hyerim Bae
Summary: Dimensionality reduction plays a crucial role in classification, object detection, and pattern recognition tasks. Autoencoder, as a state-of-the-art non-linear dimensionality reduction method, can be problematic in preserving distinctive information. In this study, we propose a supervised and cooperative super-encoder (SE) network to tackle this issue and achieve effective extraction of separable latent code.
PATTERN RECOGNITION
(2022)
Article
Computer Science, Information Systems
Sung-Jin Kim, Dae-Hyeok Lee, Seong-Whan Lee
Summary: This paper discusses the potential problems of the ShallowConvNet method in EEG-based BCI technology and proposes a novel model called M-ShallowConvNet to solve these problems. Experimental results demonstrate the improved performance of the proposed model.
Review
Neurosciences
Tianwei Wang, Yun Chen, He Cui
Summary: This review discusses the traditional representational perspectives and the emerging dynamical system perspective in motor cortex research, examining their explanatory power and controversies from empirical and computational perspectives. The goal is to reconcile these perspectives and evaluate their theoretical impact and potential applications in brain-machine interfaces.
NEUROSCIENCE BULLETIN
(2022)
Article
Engineering, Biomedical
Brian M. Dekleva, Jeffrey M. Weiss, Michael L. Boninger, Jennifer L. Collinger
Summary: This study aims to improve cursor click decoding for point-and-click and click-and-drag control using iBCI technology. By identifying prominent neural responses related to hand grasp, a new approach based on transient responses was developed, which outperformed the standard binary state classification method. This transient-based approach provides high degree of cursor click control, marking an important step towards high-performance cursor control and clinical translation of iBCI technology.
JOURNAL OF NEURAL ENGINEERING
(2021)
Article
Computer Science, Artificial Intelligence
Donglin Li, Jianhui Wang, Jiacan Xu, Xiaoke Fang, Ying Ji
Summary: A cross-channel specific-mutual feature transfer learning (CCSM-FT) network model is proposed in this paper to address the issue of extracting specific and mutual features from multiregion signals in the brain. Effective training tricks are used to maximize the distinction between these two types of features and improve algorithm effectiveness.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Engineering, Biomedical
Kuan-Jung Chiang, Chun-Shu Wei, Masaki Nakanishi, Tzyy-Ping Jung
Summary: This study aimed to enhance the performance of SSVEP-based BCIs through a transfer-learning framework leveraging LST, which achieved significantly higher decoding accuracy compared to standard TRCA and non-LST methods. The research demonstrated the effectiveness of LST in obviating SSVEP variability across multiple domains and its potential for practical applications in BCI systems.
JOURNAL OF NEURAL ENGINEERING
(2021)
Article
Engineering, Biomedical
Miyoung Chung, Taehyung Kim, Eunju Jeong, Chun Kee Chung, June Sic Kim, Oh-Sang Kwon, Sung-Phil Kim
Summary: This study evaluated the feasibility of decoding pitch imagery directly from human EEG and achieved the best classification performance for seven pitches using support vector machine. It demonstrated for the first time the potential of decoding imagined musical pitch from human EEG.
IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING
(2023)
Article
Engineering, Biomedical
Hongyi Zhi, Zhuliang Yu, Tianyou Yu, Zhenghui Gu, Jian Yang
Summary: The proposed multi-domain temporal-spatial-frequency convolutional neural network (TSFCNet) enhances the decoding performance of Motor Imagery (MI) by effectively utilizing different EEG feature domains. By performing multiple independent convolution operations in the spatial, frequency, and time-frequency domains and aggregating features using average pooling and variance layers, TSFCNet outperforms other models in terms of classification accuracy and kappa values, promising to improve the decoding performance of MI BCIs.
IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING
(2023)
Article
Computer Science, Artificial Intelligence
Imam Mustafa Kamal, Hyerim Bae
Summary: This paper proposes an auto-classifier that automatically utilizes dimensionality reduction to improve the generalization accuracy. It contains classifier and generator networks and uses a cooperative learning mechanism to achieve the objectives of classification and data reconstruction. Experimental results show that the accuracy of this classifier is highly competitive.
PATTERN RECOGNITION LETTERS
(2022)
Article
Biology
Seyyed Bahram Borgheai, Alyssa Hillary Zisk, John McLinden, James Mcintyre, Reza Sadjadi, Yalda Shahriari
Summary: This study proposed a novel personalized scheme using fNIRS and EEG as the main tools to predict and compensate for the variability in BCI systems, especially for individuals with severe motor deficits. By establishing predictive models, it was found that there were significant associations between the predicted performances and the actual performances.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Hongliang Guo, Hanbo Liu, Ahong Zhu, Mingyang Li, Helong Yu, Yun Zhu, Xiaoxiao Chen, Yujia Xu, Lianxing Gao, Qiongying Zhang, Yangping Shentu
Summary: In this paper, a BDSMA-based image segmentation method is proposed, which improves the limitations of the original algorithm by combining SMA with DE and introducing a cooperative mixing model. The experimental results demonstrate the superiority of this method in terms of convergence speed and precision compared to other methods, and its successful application to brain tumor medical images.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Jingfei Hu, Linwei Qiu, Hua Wang, Jicong Zhang
Summary: This study proposes a novel semi-supervised point consistency network (SPC-Net) for retinal artery/vein (A/V) classification, addressing the challenges of specific tubular structures and limited well-labeled data in CNN-based approaches. The SPC-Net combines an AVC module and an MPC module, and introduces point set representations and consistency regularization to improve the accuracy of A/V classification.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Omair Ali, Muhammad Saif-ur-Rehman, Tobias Glasmachers, Ioannis Iossifidis, Christian Klaes
Summary: This study introduces a novel hybrid model called ConTraNet, which combines the strengths of CNN and Transformer neural networks, and achieves significant improvement in classification performance with limited training data.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Juan Antonio Valera-Calero, Dario Lopez-Zanoni, Sandra Sanchez-Jorge, Cesar Fernandez-de-las-Penas, Marcos Jose Navarro-Santana, Sofia Olivia Calvo-Moreno, Gustavo Plaza-Manzano
Summary: This study developed an easy-to-use application for assessing the diagnostic accuracy of digital pain drawings (PDs) compared to the classic paper-and-pencil method. The results demonstrated that digital PDs have higher reliability and accuracy compared to paper-and-pencil PDs, and there were no significant differences in assessing pain extent between the two methods. The PAIN EXTENT app showed good convergent validity.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Biao Qu, Jialue Zhang, Taishan Kang, Jianzhong Lin, Meijin Lin, Huajun She, Qingxia Wu, Meiyun Wang, Gaofeng Zheng
Summary: This study proposes a deep unrolled neural network, pFISTA-DR, for radial MRI image reconstruction, which successfully preserves image details using a preprocessing module, learnable convolution filters, and adaptive threshold.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Alireza Rafiei, Milad Ghiasi Rad, Andrea Sikora, Rishikesan Kamaleswaran
Summary: This study aimed to improve machine learning model prediction of fluid overload by integrating synthetic data, which could be translated to other clinical outcomes.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Jinlian Ma, Dexing Kong, Fa Wu, Lingyun Bao, Jing Yuan, Yusheng Liu
Summary: In this study, a new method based on MDenseNet is proposed for automatic segmentation of nodular lesions from ultrasound images. Experimental results demonstrate that the proposed method can accurately extract multiple nodules from thyroid and breast ultrasound images, with good accuracy and reproducibility, and it shows great potential in other clinical segmentation tasks.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Jiabao Sheng, SaiKit Lam, Jiang Zhang, Yuanpeng Zhang, Jing Cai
Summary: Omics fusion is an important preprocessing approach in medical image processing that assists in various studies. This study aims to develop a fusion methodology for predicting distant metastasis in nasopharyngeal carcinoma by mitigating the disparities in omics data and utilizing a label-softening technique and a multi-kernel-based neural network.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Zhenxiang Xiao, Liang He, Boyu Zhao, Mingxin Jiang, Wei Mao, Yuzhong Chen, Tuo Zhang, Xintao Hu, Tianming Liu, Xi Jiang
Summary: This study systematically investigates the functional connectivity characteristics between gyri and sulci in the human brain under naturalistic stimulus, and identifies unique features in these connections. This research provides novel insights into the functional brain mechanism under naturalistic stimulus and lays a solid foundation for accurately mapping the brain anatomy-function relationship.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Qianqian Wang, Mingyu Zhang, Aohan Li, Xiaojun Yao, Yingqing Chen
Summary: The development of PARP-1 inhibitors is crucial for the treatment of various cancers. This study investigates the structural regulation of PARP-1 by different allosteric inhibitors, revealing the basis of allosteric inhibition and providing guidance for the discovery of more innovative PARP-1 inhibitors.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Qing Xu, Wenting Duan
Summary: In this paper, a dual attention supervised module, named DualAttNet, is proposed for multi-label lesion detection in chest radiographs. By efficiently fusing global and local lesion classification information, the module is able to recognize targets with different sizes. Experimental results show that DualAttNet outperforms baselines in terms of mAP and AP50 with different detection architectures.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Kaja Gutowska, Piotr Formanowicz
Summary: The primary aim of this research is to propose algorithms for identifying significant reactions and subprocesses within biological system models constructed using classical Petri nets. These solutions enable two analysis methods: importance analysis for identifying critical individual reactions to the model's functionality and occurrence analysis for finding essential subprocesses. The utility of these methods has been demonstrated through analyses of an example model related to the DNA damage response mechanism. It should be noted that these proposed analyses can be applied to any biological phenomenon represented using the Petri net formalism. The presented analysis methods extend classical Petri net-based analyses, enhancing our comprehension of the investigated biological phenomena and aiding in the identification of potential molecular targets for drugs.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Hansle Gwon, Imjin Ahn, Yunha Kim, Hee Jun Kang, Hyeram Seo, Heejung Choi, Ha Na Cho, Minkyoung Kim, Jiye Han, Gaeun Kee, Seohyun Park, Kye Hwa Lee, Tae Joon Jun, Young-Hak Kim
Summary: Electronic medical records have potential in advancing healthcare technologies, but privacy issues hinder their full utilization. Deep learning-based generative models can mitigate this problem by creating synthetic data similar to real patient data. However, the risk of data leakage due to malicious attacks poses a challenge to traditional generative models. To address this, we propose a method that employs local differential privacy (LDP) to protect the model from attacks and preserve the privacy of training data, while generating medical data with reasonable performance.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Siwei Tao, Zonghan Tian, Ling Bai, Yueshu Xu, Cuifang Kuang, Xu Liu
Summary: This study proposes a transfer learning-based method to address the phase retrieval problem in grating-based X-ray phase contrast imaging. By generating a training dataset and using deep learning techniques, this method improves image quality and can be applied to X-ray 2D and 3D imaging.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)