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Attention Detection Using EEG Signals and Machine Learning: A Review

Machine Intelligence Research 2025年第2期22卷 219-238页

作者： Qianru Sun Yueying Zhou Peiliang Gong Daoqiang Zhang Key Laboratory of Brain-Machine Intelligence Technology Ministry of EducationNanjing University of Aeronautics and AstronauticsNanjing 211106China College of Computer Science and Technology Nanjing University of Aeronautics and AstronauticsNanjing 211106China

Attention detection using electroencephalogram(EEG)signals has become a popular ***,there seems to be a notable gap in the literature regarding comprehensive and systematic reviews of machine learning methods for attention detection using EEG ***,this survey outlines recent advances in EEG-based attention detection within the past five years,with a primary focus on auditory attention detection(AAD)and attention level ***,we provide a brief overview of commonly used paradigms,preprocessing techniques,and artifact-handling methods,as well as listing accessible datasets used in these ***,we summarize the machine learning methods for classification in this field and divide them into two categories:traditional machine learning methods and deep learning *** also analyse the most frequently used methods and discuss the factors influencing each technique′s performance and ***,we discuss the existing challenges and future trends in this field.

关键词： Attention detection electroencephalogram(EEG) machine learning deep learning brain-computer interface.

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Machine learning assisted screening of binary alloys for metal-based anode materials

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Journal of Energy Chemistry 2025年第5期104卷 62-68页

作者： Xingyue Shi Linming Zhou Yuhui Huang Chaohui Wu Yongjun Wu Juan Li Zijian Hong State Key Laboratory of Silicon and Advanced Semiconductor Materials School of Materials Science and Engineering Zhejiang University Nanhu Brain-Computer Interface Institute Zhejiang Key Laboratory of Advanced Solid State Energy Storage Technology and Applications Research Institute of Zhejiang University-Taizhou Inner Mongolia Electric Power Economic and Technological Research Institute Institute of Fundamental and Transdisciplinary Research Zhejiang University College of Materials Science and Engineering Zhejiang University of Technology School of Engineering Hangzhou City University

Metal alloy anode materials with high specific capacity and low voltage have recently gained significant attention due to their excellent electrochemical performance and the ability to suppress dendrite ***, experimental investigations of metal alloys can be time-consuming and expensive, often requiring extensive experimental design and effort. In this study, we developed a machine learning model based on the Crystal Graph Convolutional Neural Network(CGCNN) to screen alloy anode materials for seven battery systems, including lithium(Li), sodium(Na), potassium(K), zinc(Zn), magnesium(Mg), calcium(Ca), and aluminum(Al). We utilized data with tens of thousands of alloy materials from the Materials Project(MP) and Automatic FLOW for Materials Discovery(AFLOW) databases. Without any experimental voltage input, we identified over 30 alloy systems that have been experimentally validated with good precision. Additionally, we predicted over 100 alloy anodes with low potential and high specific capacity. We hope this work to spur further interest in employing advanced machine learning models for the design of battery materials.

关键词： Alloy anodes CGCNN Machine learning Metal-based batteries

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A Wireless Fully Implantable DBS Device for Closed-Loop Neuromodulation Applications

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IEEE Sensors Journal 2025年第9期25卷 15331-15344页

作者： Su, Kangyu Qiu, Zhang Xu, Kedi Xu, Jian Wang, Yueming Zhejiang University College of Information Science and Electronic Engineering Hangzhou310027 China Zhejiang University MOE Frontier Science Center for Brain Science and Brain-Machine Integration Hangzhou310027 China Nanhu Brain-Computer Interface Institute Hangzhou311100 China Zhejiang University College of Biomedical Engineering and Instrument Science Hangzhou310027 China Zhejiang University School of Medicine Affiliated Mental Health Center Hangzhou Seventh People’s Hospital Hangzhou310013 China Zhejiang University State Key Laboratory of Brain-Machine Intelligence Hangzhou310027 China Zhejiang University College of Computer Science and Technology Hangzhou310027 China

Closed-loop deep brain stimulation (DBS) is an emerging technology in the field of neuromodulation, which can provide effective treatment methods for intractable neurological disorders. To address the faced challenges, this article presents a high-precision closed-loop DBS device based on a custom-designed neuromodulation integrated circuit (CNIC) fabricated in a 180-nm bipolar-CMOS-DMOS (BCD) process. Also, three new techniques have been developed to optimize system performance. First, to improve system stability, a frequency shaping & quick blanking amplifier (FSQBA)-based analog front-end is proposed to extend system dynamic range (DR). Second, both stimulation artifacts (SAs) suppression scheme and closed-loop control algorithm are developed to achieve real simultaneous recording and stimulation. Third, a redundant-crossfire (RXF) technique is designed to enhance current precision without consuming much extra area. Bench-top measurements show that the designed system features a wide DR of 112 dB, a short saturation recovery time of 100 μs, and a high stimulation current resolution of 13.1-bit. Besides, animal experiments demonstrate that the designed system is highly suitable for long-term implantation applications, thus providing accurate and reliable closed-loop control for neuromodulation therapies. © 2001-2012 IEEE.

关键词： CMOS integrated circuits

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Electrophysiological Abnormalities Associated with Sustained Attention in Children with Attention Deficit Hyperactivity Disorder and Autism Spectrum Disorder

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IEEE Transactions on Neural Systems and Rehabilitation Engineering 2025年 33卷 1785-1795页

作者： Zhao, Jiajing Bao, Mengyi Ruan, Wencong Kuang, Rui Li, Haifeng Wang, Yueming Yao, Lin Affiliated Mental Health Center Hangzhou Seventh People’s Hospital Zhejiang University School of Medicine Department of Neurobiology China Nanhu Brain-Computer Interface Institute Hangzhou 311100 China Zhejiang University College of Computer Science and Technology MOE Frontiers Science Center for Brain and Brain-Machine Integration Zhejiang University China Children’s Hospital of Zhejiang University School of Medicine Rehabilitation Department Hangzhou China Zhejiang University College of Computer Science and Technology China State Key Laboratory of Brain-Machine Intelligence Zhejiang University MOE Frontiers Science Center for Brain and Brain-Machine Integration China and the College of Biomedical Engineering and Instrument Science Zhejiang University College of Computer Science and Technology China

Objective: This study investigates electrophysiological abnormalities in children with Attention-Deficit/Hyperactivity Disorder (ADHD) and Autism Spectrum Disorder (ASD) during sustained attention tasks, focusing on vigilance and inhibitory control, and explores associations between neural markers and attentional performance. Methods: Children with ADHD (n = 30), ASD (n = 23), and typically developing (TD) children (n = 31) completed a Test of Variables of Attention (TOVA) task while electroencephalography (EEG) was recorded. Event-related potentials (ERPs: P1, N2, P3) and event-related desynchronization/synchronization (ERD/ERS: theta ERS, alpha ERD, beta ERS) were measured and compared across groups. Correlations between electrophysiological features and behavioral performance were analyzed. Results: Both ADHD and ASD groups demonstrated attenuated P1 amplitudes during vigilance task and reduced prefrontal theta ERS during inhibitory control. The ASD group exhibited additional impairments, including attenuated N2 amplitudes in inhibitory control, reduced P3 amplitudes, and weaker alpha ERD across conditions. The ADHD group showed additional deficits in theta ERS. Notably, N2 amplitude and theta ERS during vigilance state significantly correlated with response time measures. Conclusions: Children with ADHD and ASD share deficits in primary visual stimulus processing and inhibitory attention allocation. ASD-specific impairments involve top-down processing and inhibition, while ADHD-specific challenges involve attentional allocation and modulation. Significance: These findings enhance the electrophysiological understanding of sustained attention in ADHD and ASD, offering insights that may inform future diagnostic and intervention strategies. © 2001-2011 IEEE.

关键词： ADHD ASD Electroencephalography dynamics inhibition sustained attention vigilance

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Learned distributed image compression with decoder side information

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Digital Communications and Networks 2025年第2期11卷 349-358页

作者： Yin, Yankai Sun, Zhe Ruan, Peiying Li, Ruidong Duan, Feng Tianjin Key Laboratory of Interventional Brain-Computer Interface and Intelligent Rehabilitation Nankai University Tianjin300350 China Institute of Natural Sciences Kanazawa University Ishikawa9201164 Japan Faculty of Health Data Science and Graduate School of Medicine Juntendo University Chiba2790013 Japan NVIDIA AI Technology Center NVIDIA Japan Tokyo1070052 Japan

With the rapid development of digital communication and the widespread use of the Internet of Things, multi-view image compression has attracted increasing attention as a fundamental technology for image data communication. Multi-view image compression aims to improve compression efficiency by leveraging correlations between images. However, the requirement of synchronization and inter-image communication at the encoder side poses significant challenges, especially for constrained devices. In this study, we introduce a novel distributed image compression model based on the attention mechanism to address the challenges associated with the availability of side information only during decoding. Our model integrates an encoder network, a quantization module, and a decoder network, to ensure both high compression performance and high-quality image reconstruction. The encoder uses a deep Convolutional Neural Network (CNN) to extract high-level features from the input image, which then pass through the quantization module for further compression before undergoing lossless entropy coding. The decoder of our model consists of three main components that allow us to fully exploit the information within and between images on the decoder side. Specifically, we first introduce a channel-spatial attention module to capture and refine information within individual image feature maps. Second, we employ a semi-coupled convolution module to extract both shared and specific information in images. Finally, a cross-attention module is employed to fuse mutual information extracted from side information. The effectiveness of our model is validated on various datasets, including KITTI Stereo and Cityscapes. The results highlight the superior compression capabilities of our method, surpassing state-of-the-art techniques. © 2024 Chongqing University of Posts and Telecommunications

关键词： Decoding

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Multimodal brain-computer interfaces: AI-powered Decoding Methodologies

arXiv

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arXiv 2025年

作者： Li, Siyang Wang, Hongbin Chen, Xiaoqing Wu, Dongrui The Ministry of Education Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China The Henan Key Laboratory of Brain Science and Brain Computer Interface Technology School of Electrical and Information Engineering Zhengzhou University China

brain-computer interfaces (BCIs) enable direct communication between the brain and external devices. This review highlights the core decoding algorithms that enable multimodal BCIs, including a dissection of the elements, a unified view of diversified approaches, and a comprehensive analysis of the present state of the field. We emphasize algorithmic advancements in cross-modality mapping, sequential modeling, besides classic multi-modality fusion, illustrating how these novel AI approaches enhance decoding of brain data. The current literature of BCI applications on visual, speech, and affective decoding are comprehensively explored. Looking forward, we draw attention on the impact of emerging architectures like multimodal Transformers, and discuss challenges such as brain data heterogeneity and common errors. This review also serves as a bridge in this interdisciplinary field for experts with neuroscience background and experts that study AI, aiming to provide a comprehensive understanding for AI-powered multimodal BCIs. © 2025, CC BY-NC-ND.

关键词： brain computer interface

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Self-Attentive Spatio-Temporal Calibration for Precise Intermediate Layer Matching in ANN-to-SNN Distillation

arXiv

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arXiv 2025年

作者： Hong, Di Wang, Yueming The College of Computer Science and Technology Zhejiang University China Nanhu Brain-computer Interface Institute Hangzhou China The State Key Laboratory of Brain-Machine Intelligence Zhejiang University China

Spiking Neural Networks (SNNs) are promising for low-power computation due to their event-driven mechanism but often suffer from lower accuracy compared to Artificial Neural Networks (ANNs). ANN-to-SNN knowledge distillation can improve SNN performance, but previous methods either focus solely on label information, missing valuable intermediate layer features, or use a layer-wise approach that neglects spatial and temporal semantic inconsistencies, leading to performance degradation. To address these limitations, we propose a novel method called self-attentive spatio-temporal calibration (SASTC). SASTC uses self-attention to identify semantically aligned layer pairs between ANN and SNN, both spatially and temporally. This enables the autonomous transfer of relevant semantic information. Extensive experiments show that SASTC outperforms existing methods, effectively solving the mismatching problem. Superior accuracy results include 95.12% on CIFAR-10, 79.40% on CIFAR-100 with 2 time steps, and 68.69% on ImageNet with 4 time steps for static datasets, and 97.92% on DVS-Gesture and 83.60% on DVS-CIFAR10 for neuromorphic datasets. This marks the first time SNNs have outperformed ANNs on both CIFAR-10 and CIFAR-100, shedding the new light on the potential applications of SNNs. Copyright © 2025, The Authors. All rights reserved.

关键词： Spatio-temporal data

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Lack of social interaction advantage: A domain-general cognitive alteration in schizophrenia

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Journal of Psychiatric Research 2025年 186卷 434-444页

作者： Tang, Enze Li, Jian Liu, Huiying Peng, Chihao Zhou, Dongsheng Hu, Shaohua Chen, Hui The First Affiliated Hospital Zhejiang University School of Medicine Hangzhou China Department of Psychology and Behavioral Sciences Zhejiang University Hangzhou China Department of Psychiatry Affiliated Kangning Hospital of Ningbo University (Ningbo Kangning Hospital) Zhejiang Ningbo 315201 China Department of Psychiatry The First Affiliated Hospital Zhejiang University School of Medicine Hangzhou 310003 China Nanhu Brain-computer Interface Institute Hangzhou 311100 China The Zhejiang Key Laboratory of Precision Psychiatry Hangzhou 310003 China MOE Frontier Science Center for Brain Science and Brain-Machine Integration Zhejiang University School of Medicine Hangzhou 310003 China Brain Research Institute of Zhejiang University Hangzhou 310003 China The State Key Lab of Brain-Machine Intelligence Zhejiang University Hangzhou China Zhejiang Engineering Center for Mathematical Mental Health Hangzhou 310003 China

People with schizophrenia (PSZ) showed preserved ability to unconsciously process simple social information (e.g., face and gaze), but not in higher-order cognition (e.g., memory). It is yet unknown how PSZ process social interactions across different cognitive domains. This study systematically investigated the cognitive characteristics of PSZ during social interaction processing from bottom-up perception to top-down memory, and established correlations with schizophrenic symptoms. In two experiments, social interactions were consistently displayed by face-to-face or back-to-back dyads. Experiment 1 enrolled 30 PSZ and 30 healthy control subjects (HCS) with a breaking continuous flash suppression (b-CFS) paradigm. Experiment 2 recruited 36 PSZ and 36 HCS for two memory tasks, wherein participants restored the between-model distance (working memory task) and recalled the socially bound pairs (long-term memory task). Results indicated that HCS showed advantageous processing of socially interactive stimuli against non-interactive stimuli throughout two experiments, including faster access to visual consciousness, closer spatial distance held in working memory and higher recollection accuracy in long-term memory. However, PSZ did not show any of these advantages, with significant interaction effects for all three tasks (task one: p = .018, η_p² = .092;task two: p = .021, η_p² = .074;task three: p = .015, η_p² = .082). Moreover, correlation analyses indicated that PSZ with more severe negative symptoms (r = −.344, p = .040) or higher medication dosages (r = −.334, p = .046) showed fewer advantages in memorizing socially interactive information. Therefore, social interaction is not prioritized in schizophrenia from bottom-up perception to top-down memory, and the magnitude of such a domain-general cognitive alteration is clinically relevant to symptom severity and medication. © 2025 Elsevier Ltd

关键词： Continuous flash suppression Memory Perception Schizophrenia Social interaction

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A survey of motor rehabilitation for hemiplegic upper limbs based on the brain–apparatus interaction

Intelligent Sports and Health

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Intelligent Sports and Health 2025年第1期1卷 4-10页

作者： Yangsong Zhang Xinrui Bian Fali Li Yufeng Tang Ting Zeng Jing Zhao Dezhong Yao Peng Xu School of Computer Science and Technology Laboratory for Brain Science and Artificial Intelligence Southwest University of Science and Technology Mianyang 621010 China Clinical Hospital of Chengdu Brain Science Institute MOE Key Laboratory for Neuroinformation School of Life Science and Technology University of Electronic Scienceand Technology of China Chengdu 611731 China Mianyang Central Hospital Mianyang 621000 China Radiation Oncology Key Laboratory of Sichuan Province Chengdu 610041 China

Stroke remains one of the leading causes of disability among adults, and the demand for stroke rehabilitation services is increasing. Many studies in the literature have provided various rehabilitation training strategies for stroke patients. In this work, we focus on motor rehabilitation for hemiplegic upper limbs with the assistance of a brain–computer interface and an exoskeleton. We reviewed the progress from multiple perspectives, i.e., the rehabilitation training mode, the rehabilitation movement paradigm, decoding algorithms for physiological signals, motion-assisted rehabilitation robots, evaluation strategies and brain mechanisms. Furthermore, we briefly discuss some challenges and list several future research directions. We hope that this paper will provide insights into future studies on upper limb rehabilitation for hemiplegic patients.

关键词： Hemiplegic upper limbs Smart rehabilitation EMG EEG brain–apparatus interaction

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A Habenula Neural Biomarker Simultaneously Tracks Weekly and Daily Symptom Variations during Deep brain Stimulation Therapy for Depression

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IEEE Journal of Biomedical and Health Informatics 2025年 PP卷 PP页

作者： Liu, Shi Qi, Yu Hu, Shaohua Wei, Ning Zhang, Jianmin Zhu, Junming Wu, Hemmings Hu, Hailan Yang, Yuxiao Wang, Yueming Zhejiang University College of Computer Science and Technology Hangzhou 310058 China Zhejiang University School of Medicine First Affiliated Hospital Hangzhou 310003 China Zhejiang University School of Medicine Second Affiliated Hospital Hangzhou 310058 China Zhejiang University School of Brain Science and Brain Medicine Hangzhou 310058 China Nanhu Brain-computer Interface Institute MOE Frontier Science Center for Brain Science and Brain-machine Integration Hangzhou China Zhejiang University State Key Laboratory of Brain-machine Intelligence Department of Neurosurgery Second Affiliated Hospital School of Medicine College of Computer Science and Technology Hangzhou 310058 China

Objective: Deep brain stimulation (DBS) targeting the lateral habenula (LHb) is a promising therapy for treatment-resistant depression (TRD) but its clinical effect has been variable, which can be improved by adaptive DBS (aDBS) guided by a neural biomarker of depression symptoms. Existing neural biomarkers, however, cannot simultaneously track slow and fast symptom dynamics, do not sufficiently respond to stimulation parameters, and lack neurobiological interpretability, which hinder their use in developing aDBS. Methods: We conducted a study on one TRD patient who achieved remission following a 41-week LHb DBS treatment, during which we assessed slow symptom variations using weekly clinical ratings and fast variations using daily self-reports. We recorded daily LHb local field potentials (LFP) concurrently with the reports during the entire treatment process. We then used machine learning methods to identify a personalized depression neural biomarker from spectral and temporal LFP features. Results: The neural biomarker was identified from classification of high and low depression symptom states with a cross-validated accuracy of 0.97. It further simultaneously tracked both weekly (slow) and daily (fast) depression symptom variation dynamics, achieving test data explained variance of 0.74 and 0.63 respectively and responded to DBS frequency alterations. Finally, it can be neurobiologically interpreted as indicating LHb excitatory and inhibitory balance changes during DBS treatment. Conclusion: By collecting and analyzing a unique personalized dataset of weekly and daily LFP recordings and symptom evaluations, we identified a high-performance neural biomarker for depression during LHb DBS. Significance: Our results hold promise to facilitate future aDBS for treating TRD. © 2013 IEEE.

关键词： biomarker Deep brain stimulation depression habenula local field potential machine learning

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