Article
Chemistry, Analytical
Vangelis P. P. Oikonomou
Summary: This study proposes a novel method that combines common spatial patterns and deep-learning neural networks to identify individuals based on the brain's responses to visual stimulation at specific frequencies. The method achieved a high correct recognition rate and showed its usefulness in person identification and usability.
Article
Biochemical Research Methods
Cristian Felipe Blanco-Diaz, Javier M. Antelis, Andres Felipe Ruiz-Olaya
Summary: This study compared different combinations of time segments and filter banks to decode hand motor imagery tasks in BCI research. The best configuration consisted of a filter bank with 3 filters, with accuracies of approximately 74% achieved in a time window of 1.5 seconds after the trigger.
JOURNAL OF NEUROSCIENCE METHODS
(2022)
Article
Geochemistry & Geophysics
Wenfei Gao, Fang Liu, Jia Liu, Liang Xiao, Xu Tang
Summary: In this article, a spatial-spectral adaptive learning with pixelwise filtering (SSALPF) method is proposed to fully consider the discriminative information of pixels in different spatial locations for hyperspectral image classification. It consists of a parallel spatial-spectral adaptive learning (SSAL) module and a pixelwise filtering (PF) module, which jointly obtain spatial-spectral discriminative features and implement pixel-level filtering on HSI for classification.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2023)
Article
Engineering, Biomedical
Zehui Wang, Chuangquan Chen, Junhua Li, Feng Wan, Yu Sun, Hongtao Wang
Summary: A brain-computer interface (BCI) has great potential in various applications, and the P300-based BCI speller is a typical example. However, the low recognition rate of the P300 speller is a challenge due to the complex spatio-temporal characteristics of EEG signals. In this study, we proposed a deep-learning framework named ST-CapsNet to improve P300 detection using a capsule network with spatial and temporal attention modules.
IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING
(2023)
Article
Multidisciplinary Sciences
Hirokatsu Shimizu, Ramesh Srinivasan
Summary: Decoding brain activity related to specific tasks, such as imagining something, is important for brain computer interface control. This study aimed to develop methods for improving training, performance, and interpretation of brain data. The results showed that brain activity during the visual task was present in the imagination task and could be used to improve classification of the imagined image. By using an attention module, spatial weights in each frequency band could be derived, allowing for spatial or frequency importance comparison between tasks. The combination of data sets in training also improved classification of the imagination task without significantly changing performance in the visual task.
Article
Multidisciplinary Sciences
Andres Jaramillo-Gonzalez, Shizhe Wu, Alessandro Tonin, Aygul Rana, Majid Khalili Ardali, Niels Birbaumer, Ujwal Chaudhary
Summary: The dataset includes EEG and EOG recordings from four ALS patients in locked-in state, who used eye movements to communicate, providing valuable insights for researching the progression of ALS.
Article
Computer Science, Information Systems
Mustapha Moufassih, Ousama Tarahi, Soukaina Hamou, Said Agounad, Hafida Idrissi Azami
Summary: This paper proposes a hybrid approach to improve the classification performance of motor imagery BCI. The proposed method combines two principal feature extraction approaches: Multi-Band common spatial patterns and boosted tangent space mapping. Experimental results show that the proposed hybrid approach significantly improves the classification performance of MI BCI.
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
Article
Engineering, Biomedical
V. N. Kiroy, O. M. Bakhtin, E. M. Krivko, D. M. Lazurenko, E. Aslanyan, D. G. Shaposhnikov, I. Shcherban
Summary: Research on EEG coherence values during spoken and imagined speech showed higher coherence levels during real verbalization, especially in the gamma-2 rhythm frequencies. Specific spatial coherence patterns in the left cerebral hemisphere were formed during imagined speech at gamma-2 frequencies. Machine learning and neural network classification demonstrated significant similarity between spatial coherent patterns of spoken and inner speech, with potential application in Brain-computer interfaces (BCIs).
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2022)
Article
Computer Science, Information Systems
Deyu Zhang, Siyu Liu, Jian Zhang, Guoqi Li, Dingjie Suo, Tiantian Liu, Jiawei Luo, Zhiyuan Ming, Jinglong Wu, Tianyi Yan
Summary: This research proposes a new form of brain-machine shared control strategy that quantifies brain commands in the form of a 2-D control vector stream, improving the flexibility, stability, and efficiency of BCIs. The proposed controller can be used in brain-controlled 2D navigation devices, such as wheelchairs and vehicles.
IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS
(2022)
Article
Mathematical & Computational Biology
Gan Wang, Moran Cerf
Summary: This study demonstrates the effectiveness of using deep learning neural networks to improve the performance of brain-computer interfaces in predicting imagined motor actions. By extracting temporal and spectral features from EEG signals, the algorithm achieved an average performance increase of 3.50% compared to benchmark algorithms. The results on public datasets also showed high accuracy rates.
FRONTIERS IN NEUROINFORMATICS
(2022)
Article
Physics, Multidisciplinary
Linzhi Jiang, Shuyu Liu, Zhengming Ma, Wenjie Lei, Cheng Chen
Summary: This research introduces an algorithm for classifying motion imagery signals using brain-computer interface technology and demonstrates its adaptability and effectiveness through experiments.
Article
Engineering, Biomedical
Yusong Zhou, Banghua Yang, Cuntai Guan
Summary: This study developed a method called TRCA-PCD for fast decoding of asymmetric visual evoked potentials (aVEPs) and compared its performance with other methods. The results demonstrated the effectiveness and superiority of the TRCA-PCD method in recognizing aVEPs and provided guidance for parameter selection.
IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING
(2022)
Article
Neurosciences
Natalia Browarska, Aleksandra Kawala-Sterniuk, Jaroslaw Zygarlicki, Michal Podpora, Mariusz Pelc, Radek Martinek, Edward Jacek Gorzelanczyk
Summary: Consumer-grade EEG equipment has become the first choice for recording brain waves for research purposes, with quality of recorded signals improving to levels comparable to more expensive clinical devices. A smoothing filter based on the Savitzky-Golay filter is proposed for EEG signal filtering in this paper, along with a summary and comparison to other filtering approaches. Signals analyzed were acquired from subjects performing visually involving high-concentration tasks with audio stimuli using Emotiv EPOC Flex equipment.
Article
Engineering, Biomedical
Shaohua Tang, Chuancai Liu, Qiankun Zhang, Heng Gu, Xiaoli Li, Zheng Li
Summary: The study demonstrates that utilizing Riemannian-processed covariance features is viable for mental workload level classification in realistic experimental scenarios.
JOURNAL OF NEURAL ENGINEERING
(2021)
Article
Computer Science, Artificial Intelligence
Ben McCartney, Barry Devereux, Jesus Martinez-del-Rincon
Summary: This paper proposes a deep learning based approach for image retrieval using EEG, which utilizes a multi-modal deep neural network and metric learning to map EEG signals and visual information. With the scalable metric learning approach, the system achieves zero-shot image retrieval with new images and demonstrates state-of-the-art results on standard EEG image-viewing datasets.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Engineering, Biomedical
Marianne Severens, Monica Perusquia-Hernandez, Bart Nienhuis, Jason Farquhar, Jacques Duysens
IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING
(2015)
Article
Clinical Neurology
R. J. Vlek, R. S. Schaefer, C. C. A. M. Gielen, J. D. R. Farquhar, P. Desain
CLINICAL NEUROPHYSIOLOGY
(2011)
Article
Engineering, Biomedical
R. J. Vlek, R. S. Schaefer, C. C. A. M. Gielen, J. D. R. Farquhar, P. Desain
JOURNAL OF NEURAL ENGINEERING
(2011)
Article
Engineering, Biomedical
A. Belitski, J. Farquhar, P. Desain
JOURNAL OF NEURAL ENGINEERING
(2011)
Article
Engineering, Biomedical
Jeroen Geuze, Jason D. R. Farquhar, Peter Desain
JOURNAL OF NEURAL ENGINEERING
(2012)
Article
Engineering, Biomedical
Marianne Severens, Jason Farquhar, Jacques Duysens, Peter Desain
JOURNAL OF NEURAL ENGINEERING
(2013)
Article
Computer Science, Artificial Intelligence
S. M. M. Martens, J. M. Mooij, N. J. Hill, J. Farquhar, B. Schoelkopf
NEURAL COMPUTATION
(2011)
Article
Neurosciences
Rebecca S. Schaefer, Jason Farquhar, Yvonne Blokland, Makiko Sadakata, Peter Desain
Article
Neurosciences
Rebecca S. Schaefer, Peter Desain, Jason Farquhar
Article
Computer Science, Interdisciplinary Applications
J. Farquhar, N. J. Hill
Article
Multidisciplinary Sciences
Yvonne M. Blokland, Jason D. R. Farquhar, Jo Mourisse, Gert J. Scheffer, Jos G. C. Lerou, Jorgen Bruhn
Article
Multidisciplinary Sciences
Karen Dijkstra, Jason Farquhar, Peter Desain
SCIENTIFIC REPORTS
(2019)
Article
Engineering, Biomedical
K. Dijkstra, J. D. R. Farquhar, P. W. M. Desain
JOURNAL OF NEURAL ENGINEERING
(2020)
Article
Behavioral Sciences
Marjolein van der Waal, Jason Farquhar, Luciano Fasotti, Peter Desain
BEHAVIOURAL BRAIN RESEARCH
(2017)
Proceedings Paper
Engineering, Biomedical
Yvonne Blokland, Rutger Vlek, Betul Karaman, Fatma Ozin, Dick Thijssen, Thijs Eijsvogels, Willy Colier, Marianne Floor-Westerdijk, Jorgen Bruhn, Jason Farquhar
2012 ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC)
(2012)
Article
Computer Science, Artificial Intelligence
Hamdan Abdellatef, Lina J. Karam
Summary: This paper proposes performing the learning and inference processes in the compressed domain to reduce computational complexity and improve speed of neural networks. Experimental results show that modified ResNet-50 in the compressed domain is 70% faster than traditional spatial-based ResNet-50 while maintaining similar accuracy. Additionally, a preprocessing step with partial encoding is suggested to improve resilience to distortions caused by low-quality encoded images. Training a network with highly compressed data can achieve good classification accuracy with significantly reduced storage requirements.
Article
Computer Science, Artificial Intelligence
Victor R. Barradas, Yasuharu Koike, Nicolas Schweighofer
Summary: Inverse models are essential for human motor learning as they map desired actions to motor commands. The shape of the error surface and the distribution of targets in a task play a crucial role in determining the speed of learning.
Article
Computer Science, Artificial Intelligence
Ting Zhou, Hanshu Yan, Jingfeng Zhang, Lei Liu, Bo Han
Summary: We propose a defense strategy that reduces the success rate of data poisoning attacks in downstream tasks by pre-training a robust foundation model.
Article
Computer Science, Artificial Intelligence
Hao Sun, Li Shen, Qihuang Zhong, Liang Ding, Shixiang Chen, Jingwei Sun, Jing Li, Guangzhong Sun, Dacheng Tao
Summary: In this paper, the convergence rate of AdaSAM in the stochastic non-convex setting is analyzed. Theoretical proof shows that AdaSAM has a linear speedup property and decouples the stochastic gradient steps with the adaptive learning rate and perturbed gradient. Experimental results demonstrate that AdaSAM outperforms other optimizers in terms of performance.
Article
Computer Science, Artificial Intelligence
Juntong Yun, Du Jiang, Li Huang, Bo Tao, Shangchun Liao, Ying Liu, Xin Liu, Gongfa Li, Disi Chen, Baojia Chen
Summary: In this study, a dual manipulator grasping detection model based on the Markov decision process is proposed. By parameterizing the grasping detection model of dual manipulators using a cross entropy convolutional neural network and a full convolutional neural network, stable grasping of complex multiple objects is achieved. Robot grasping experiments were conducted to verify the feasibility and superiority of this method.
Article
Computer Science, Artificial Intelligence
Miaohui Zhang, Kaifang Li, Jianxin Ma, Xile Wang
Summary: This paper proposes an unsupervised person re-identification (Re-ID) method that uses two asymmetric networks to generate pseudo-labels for each other by clustering and updates and optimizes the pseudo-labels through alternate training. It also designs similarity compensation and similarity suppression based on the camera ID of pedestrian images to optimize the similarity measure. Extensive experiments show that the proposed method achieves superior performance compared to state-of-the-art unsupervised person re-identification methods.
Article
Computer Science, Artificial Intelligence
Florian Bacho, Dominique Chu
Summary: This paper proposes a new approach called the Forward Direct Feedback Alignment algorithm for supervised learning in deep neural networks. By combining activity-perturbed forward gradients, direct feedback alignment, and momentum, this method achieves better performance and convergence speed compared to other local alternatives to backpropagation.
Article
Computer Science, Artificial Intelligence
Xiaojian Ding, Yi Li, Shilin Chen
Summary: This research paper addresses the limitations of recursive feature elimination (RFE) and its variants in high-dimensional feature selection tasks. The proposed algorithms, which introduce a novel feature ranking criterion and an optimal feature subset evaluation algorithm, outperform current state-of-the-art methods.
Article
Computer Science, Artificial Intelligence
Naoko Koide-Majima, Shinji Nishimoto, Kei Majima
Summary: Visual images observed by humans can be reconstructed from brain activity, and the visualization of arbitrary natural images from mental imagery has been achieved through an improved method. This study provides a unique tool for directly investigating the subjective contents of the brain.
Article
Computer Science, Artificial Intelligence
Huanjie Tao, Qianyue Duan
Summary: In this paper, a hierarchical attention network with progressive feature fusion is proposed for facial expression recognition (FER), addressing the challenges posed by pose variation, occlusions, and illumination variation. The model achieves enhanced performance by aggregating diverse features and progressively enhancing discriminative features.
Article
Computer Science, Artificial Intelligence
Zhenyi Wang, Pengfei Yang, Linwei Hu, Bowen Zhang, Chengmin Lin, Wenkai Lv, Quan Wang
Summary: In the face of the complex landscape of deep learning, we propose a novel subgraph-level performance prediction method called SLAPP, which combines graph and operator features through an innovative graph neural network called EAGAT, providing accurate performance predictions. In addition, we introduce a mixed loss design with dynamic weight adjustment to improve predictive accuracy.
Article
Computer Science, Artificial Intelligence
Yiyang Yin, Shuangling Luo, Jun Zhou, Liang Kang, Calvin Yu-Chian Chen
Summary: Medical image segmentation is crucial for modern healthcare systems, especially in reducing surgical risks and planning treatments. Transanal total mesorectal excision (TaTME) has become an important method for treating colon and rectum cancers. Real-time instance segmentation during TaTME surgeries can assist surgeons in minimizing risks. However, the dynamic variations in TaTME images pose challenges for accurate instance segmentation.
Article
Computer Science, Artificial Intelligence
Teng Cheng, Lei Sun, Junning Zhang, Jinling Wang, Zhanyang Wei
Summary: This study proposes a scheme that combines the start-stop point signal features for wideband multi-signal detection, called Fast Spectrum-Size Self-Training network (FSSNet). By utilizing start-stop points to build the signal model, this method successfully solves the difficulty of existing deep learning methods in detecting discontinuous signals and achieves satisfactory detection speed.
Article
Computer Science, Artificial Intelligence
Wenming Wu, Xiaoke Ma, Quan Wang, Maoguo Gong, Quanxue Gao
Summary: The layer-specific modules in multi-layer networks are critical for understanding the structure and function of the system. However, existing methods fail to accurately characterize and balance the connectivity and specificity of these modules. To address this issue, a joint learning graph clustering algorithm (DRDF) is proposed, which learns the deep representation and discriminative features of the multi-layer network, and balances the connectivity and specificity of the layer-specific modules through joint learning.
Article
Computer Science, Artificial Intelligence
Guanghui Yue, Guibin Zhuo, Weiqing Yan, Tianwei Zhou, Chang Tang, Peng Yang, Tianfu Wang
Summary: This paper proposes a novel boundary uncertainty aware network (BUNet) for precise and robust colorectal polyp segmentation. BUNet utilizes a pyramid vision transformer encoder to learn multi-scale features and incorporates a boundary exploration module (BEM) and a boundary uncertainty aware module (BUM) to handle boundary areas. Experimental results demonstrate that BUNet outperforms other methods in terms of performance and generalization ability.