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Electroencephalogram and Event-Related Potential in Mild cognitive Impairment: Recent Developments in Signal Processing, Machine Learning, and Deep Learning

IEEE Journal of Selected Areas in Sensors

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IEEE Journal of Selected Areas in Sensors 2025年 2卷 162-184页

作者： Azami, Hamed Mirjalili, Mina Rajji, Tarek K. Wu, Chien-Te Humeau-Heurtier, Anne Jung, Tzyy-Ping Wei, Chun-Shu Trinh, Thanh-Tung Liu, Yi-Hung University of Toronto Centre for Addiction and Mental Health TorontoONM5S 1A1 Canada University of Texas Southwestern Medical Center DallasTX75390 United States University of Florida Department of Occupational Therapy GainesvilleFL32611 United States University of Florida Center for Cognitive Aging and Memory McKnight Brain Institute GainesvilleFL32611 United States University of Angers LARIS SFR MATHSTIC AngersF-49000 France University of California Institute for Neural Computation Institute of Engineering in Medicine La Jolla CA92093 United States National Yang Ming Chiao Tung University Department of Computer Science Hsinchu City30010 Taiwan National Taipei University of Technology Graduate Institute of Manufacturing Technology College of Mechanical and Electrical Engineering Taipei10608 Taiwan Vietnam National University Department of Information and Communication Technology Faculty of Nontraditional Security HSB Hanoi720325 Viet Nam National Yang Ming Chiao Tung University Institute of Electrical and Control Engineering Hsinchu City30010 Taiwan

Mild cognitive impairment (MCI) is an early stage of non-age-related cognitive decline with an increased risk of progressing to dementia. Early detection of MCI is essential for implementing preventative strategies that can delay or prevent the onset of dementia, ultimately improving patient outcomes and reducing healthcare costs. Electroencephalograms (EEGs) and event-related potentials (ERPs) have shown significant promise in detecting MCI due to their affordability, real-time monitoring capabilities, and noninvasiveness. EEG provides continuous brain activity data, while ERPs offer insights into specific cognitive processes by analyzing brain responses to stimuli. These methods can complement each other in MCI diagnosis by providing a comprehensive view of overall brain function and detailed information on specific cognitive processes. However, EEG and ERP are susceptible to noise and interindividual variability, which can hinder their reliability. In addition, applying machine learning models on EEG or ERP for MCI detection presents challenges such as the risk of overfitting and difficulties in interpreting the underlying decision-making process. This review emphasizes recent advancements in signal processing and feature extraction methods applied to EEG and ERP data and explores the use of machine learning and deep learning techniques to enhance diagnostic accuracy and interpretative depth. By integrating these methodologies, the review highlights how EEG and ERP can contribute to a more effective understanding and monitoring of cognitive changes associated with MCI, underscoring the importance of early diagnosis for timely intervention and improved patient care. Finally, the review focuses on future research directions, including the development of advanced analytical techniques and multimodal integration approaches involving EEG and ERP to further improve diagnostic accuracy and clinical application. © 2024 IEEE.

关键词： Deep learning

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From EEG to Eye Movements: Cross-modal Emotion Recognition Using Constrained Adversarial Network with Dual Attention

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IEEE Transactions on Affective Computing 2024年

作者： Wang, Yiting Liu, Jia-Wen Lu, Bao-Liang Zheng, Wei-Long Karolinska Institutet Department of Clinical Neuroscience Stockholm171 77 Sweden Shanghai Jiao Tong University China Shanghai Jiao Tong University Center for Brain-Like Computing and Machine Intelligence Department of Computer Science and Engineering Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai200240 China Shanghai Jiao Tong University School of Medicine RuiJin-Mihoyo Laboratory Clinical Neuroscience Center RuiJin Hospital Shanghai200020 China Shanghai Emotionhelper Technology Co. Ltd. Shanghai China

Emotion recognition is a fundamental part of affective computing, obtaining performance gain from multimodal methods. Electroencephalography (EEG) and eye movements are extensively used as they contain complementary information. However, the inconvenient acquisition of EEG is hindering the extensive adoption of multimodal emotion recognition in daily applications while eye movements are more convenient to collect but with lower performance. To tackle this issue, we propose a Constrained Adversarial Network with Dual Attention (CANDA), exploiting the complementary information from multiple modalities during training to improve the test-time performance of single easily acquired modality, i.e., transferring knowledge from a stronger modality to a weaker modality. During training, a common joint space is learned to diminish the distribution discrepancy among different modalities and incorporate the multimodal representations. During test, single modality is converted to the common space achieving comparable performance to multiple modalities. Extensive experiments demonstrate that our model achieves the state-of-the-art performance for cross-modal emotion recognition. Specifically, the mean accuracy increases around 15% on SEED, 15% on SEED-IV, and 2% on SEED-V compared to the latest baseline for emotion recognition. Visualization of features in the joint space illustrates that the distribution of different modalities aligns together with the discriminative ability regarding various emotions. © 2010-2012 IEEE.

关键词： Electroencephalography

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Diagnosis of Pediatrics Epilepsy Based on Graph Analysis of Scalp EEG Applying Mutual Information

Diagnosis of Pediatrics Epilepsy Based on Graph Analysis of ...

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International Conference on Computational science and Computational Intelligence (CSCI)

作者： Elaheh Zarafshan Parisa Forouzannezhad Hoda Rajaei Ulyana Morar Mercedes Cabrerizo Ilker Yaylali Alberto Pinzon Malek Adjouadi Department of Electrical and Computer Engineering Florida International University FL USA Dept. of Radiation Oncology School of Medicine University of Washington Seattle WA Department of Brain and Cognitive Science Massachusetts Institute of Technology MA USA Department of Neurology Oregon Health and Science University Portland OR USA Dept. of Neurology Neuroscience Center Baptist Hospital of Miami FL USA

Epilepsy is a brain disorder that causes seizures, affecting nearly half a million children in the US alone. In this study, we aimed to use a nonlinear driven method to characterize scalp EEG recordings of pediatric epilepsy patients (PE: $\mathrm{n}=7$ ) compared to pediatric control subjects (PC: $\mathrm{n}=7$ ) in a clinical environment. A time-varying approach was used to construct functional connectivity networks (FCNs) of all subjects. Next, the FCNs are mapped into the form of undirected graphs that are subjected to the extraction of graph theory-based features. An unsupervised clustering technique based on K-mean is used to delineate the PE from the PC group. Our findings show a statistically significant difference in the mean FCNs between PC and PE groups $(t(340)=- 15.9899, p < < 0.0001)$ . Performance results showed an accuracy of 92.5% with a sensitivity of 90% and a specificity of 95.3%. This approach can help improve and validate the early diagnosis of PE by applying non-invasive scalp EEG signals.

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Syllablebased Neural Thai Word Segmentation 28

Syllablebased Neural Thai Word Segmentation

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28th International Conference on Computational Linguistics, COLING 2020

作者： Chormai, Pattarawat Prasertsom, Ponrawee Cheevaprawatdomrong, Jin Rutherford, Attapol T. Department of Linguistics Chulalongkorn University Bangkok Thailand Computer Science and Electrical Engineering Technische Universität Berlin Germany Max Planck School of Cognition MPI for Human Cognitive and Brain Sciences Leipzig Germany

ISBN: (纸本)9781952148279

Word segmentation is a challenging preprocessing step for Thai Natural Language Processing due to the lack of explicit word boundaries. The previous systems rely on powerful neural network architecture alone and ignore linguistic substructures of Thai words. We utilize the linguistic observation that Thai strings can be segmented into syllables, which should narrow down the search space for the word boundaries and provide helpful features. Here, we propose a neural Thai Word Segmenter that uses syllable embeddings to capture linguistic constraints and uses dilated CNN filters to capture the environment of each character. Within this goal, we develop the first MLbased Thai orthographical syllable segmenter, which yields syllable embeddings to be used as features by the word segmenter. Our word segmentation system outperforms the previous state-of-the-art system in both speed and accuracy on both indomain and outdomain datasets. © 2020 COLING 2020 - 28th International Conference on Computational Linguistics, Proceedings of the Conference. All rights reserved.

关键词： Embeddings

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Towards decoupling frontend enhancement and backend recognition in monaural robust ASR

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computer Speech & Language 2026年 95卷

作者： Yufeng Yang Ashutosh Pandey DeLiang Wang Department of Computer Science and Engineering The Ohio State University 2015 Neil Avenue Columbus 43210 OH United States Center for Cognitive and Brain Science The Ohio State University 1835 Neil Ave Columbus 43210 OH United States

It has been shown that the intelligibility of noisy speech can be improved by speech enhancement (SE) algorithms. However, monaural SE has not been established as an effective frontend for automatic speech recognition (ASR) in noisy conditions compared to an ASR model trained on noisy speech directly. The divide between SE and ASR impedes the progress of robust ASR systems, especially as SE has made major advances in recent years. This paper focuses on eliminating this divide with an ARN (attentive recurrent network) time-domain, a TF-CrossNet time–frequency domain, and an MP-SENet magnitude-phase based enhancement model. The proposed systems decouple frontend enhancement and backend ASR, with the latter trained only on clean speech. Results on the WSJ, CHiME-2, LibriSpeech, and CHiME-4 corpora demonstrate that ARN, TF-CrossNet, and MP-SENet enhanced speech all translate to improved ASR results in noisy and reverberant environments, and generalize well to real acoustic scenarios. The proposed system outperforms the baselines trained on corrupted speech directly. Furthermore, it cuts the previous best word error rate (WER) on CHiME-2 by 28.4% relatively with a 5.6% WER, and achieves 3 . 3 / 4 . 4 % WER on single-channel CHiME-4 simulated/real test data without training on CHiME-4. We also observe consistent improvements using noise-robust Whisper as the backend ASR model.

关键词： CHiME-2 CHiME-4 Robust ASR Speech distortion Speech enhancement

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Automated Detection of Epileptic Spikes and Seizures Incorporating a Novel Spatial Clustering Prior

arXiv

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arXiv 2025年

作者： Dong, Hanyang Sheng, Shurong Wang, Xiongfei Gao, Jia-Hong Sun, Yi Yang, Wanli Xiao, Kuntao Teng, Pengfei Luan, Guoming Lv, Zhao School of Artificial Intelligence Anhui University Hefei230601 China Anhui Province Key Laboratory of Biomedical Imaging and Intelligent Processing Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei230088 China Department of Neurosurgery Beijing Key Laboratory of Epilepsy Sanbo Brain Hospital Capital Medical University Beijing China McGovern Institute for Brain Research Peking University Beijing China Center for MRI Research Academy for Advanced Interdisciplinary Studies Peking University Beijing China Beijing City Key Lab for Medical Physics and Engineering Institute of Heavy Ion Physics School of Physics Peking University Beijing China Anhui Province Key Laboratory of Multimodal Cognitive Computation School of Computer Science and Technology Anhui University Hefei230601 China

A Magnetoencephalography (MEG) time-series recording consists of multi-channel signals collected by superconducting sensors, with each signal’s intensity reflecting magnetic field changes over time at the sensor location. Automating epileptic MEG spike detection significantly reduces manual assessment time and effort, yielding substantial clinical benefits. Existing research addresses MEG spike detection by encoding neural network inputs with signals from all channel within a time segment, followed by classification. However, these methods overlook simultaneous spiking occurred from nearby sensors. We introduce a simple yet effective paradigm that first clusters MEG channels based on their sensor’s spatial position. Next, a novel convolutional input module is designed to integrate the spatial clustering and temporal changes of the signals. This module is fed into a custom MEEG-ResNet3D developed by the authors, which learns to extract relevant features and classify the input as a spike clip or not. Our method achieves an F1 score of 94.73% on a large real-world MEG dataset Sanbo-CMR collected from two centers, outperforming state-of-the-art approaches by 1.85%. Moreover, it demonstrates efficacy and stability in the Electroencephalographic (EEG) seizure detection task, yielding an improved weighted F1 score of 1.4% compared to current state-of-the-art techniques evaluated on TUSZ, whch is the largest EEG seizure dataset. Copyright © 2025, The Authors. All rights reserved.

关键词： Magnetoencephalography

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Semi-supervised Sequential Generative Models

arXiv

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arXiv 2020年

作者： Teng, Michael Le, Tuan Anh Scibior, Adam Wood, Frank Department of Engineering Science University of Oxford Department of Brain and Cognitive Sciences MIT Department of Computer Science University of British Columbia

We introduce a novel objective for training deep generative time-series models with discrete latent variables for which supervision is only sparsely available. This instance of semi-supervised learning is challenging for existing methods, because the exponential number of possible discrete latent configurations results in high variance gradient estimators. We first overcome this problem by extending the standard semi-supervised generative modeling objective with reweighted wake-sleep. However, we find that this approach still suffers when the frequency of available labels varies between training sequences. Finally, we introduce a unified objective inspired by teacher-forcing and show that this approach is robust to variable length supervision. We call the resulting method caffeinated wake-sleep (CWS) to emphasize its additional dependence on real data. We demonstrate its effectiveness with experiments on MNIST, handwriting, and fruit fly trajectory data. Copyright © 2020, The Authors. All rights reserved.

关键词： Wakes

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Learning hierarchical integration of foveal and peripheral vision for vergence control by active efficient coding

arXiv

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arXiv 2021年

作者： Zhao, Zhetuo Triesch, Jochen Shi, Bertram E. Dept. of Brain and Cognitive Science University of Rochester RochesterNY United States Frankfurt Institute for Advanced Studies Frankfurt am Main Germany Dept. of Electronic and Computer Engineering Hong Kong University of Science and Technology Kowloon Hong Kong

The active efficient coding (AEC) framework parsimoniously explains the joint development of visual processing and eye movements, e.g., the emergence of binocular disparity selective neurons and fusional vergence, the disjunctive eye movements that align left and right eye images. Vergence can be driven by information in both the fovea and periphery, which play complementary roles. The high resolution fovea can drive precise short range movements. The lower resolution periphery supports coarser long range movements. The fovea and periphery may also contain conflicting information, e.g. due to objects at different depths. While past AEC models did integrate peripheral and foveal information, they did not explicitly take into account these characteristics. We propose here a two-level hierarchical approach that does. The bottom level generates different vergence actions from foveal and peripheral regions. The top level selects one. We demonstrate that the hierarchical approach performs better than prior approaches in realistic environments, exhibiting better alignment and less oscillation. © 2021, CC BY.

关键词： Eye movements

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3D Shape Perception Integrates Intuitive Physics and Analysis-by-Synthesis

arXiv

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arXiv 2023年

作者： Yildirim, Ilker Siegel, Max H. Soltani, Amir A. Chaudhari, Shraman Ray Tenenbaum, Joshua B. Department of Psychology Yale University New HavenCT United States Department of Statistics & Data Science Yale University New HavenCT United States Wu-Tsai Institute Yale University New HavenCT United States Department of Brain & Cognitive Sciences MIT CambridgeMA United States The Center for Brains Minds and Machines MIT CambridgeMA United States Department of Psychology Boston College Chestnut HillMA United States Department of Electrical Engineering & Computer Science MIT CambridgeMA United States

Many surface cues support three-dimensional shape perception, but people can sometimes still see shape when these features are missing – in extreme cases, even when an object is completely occluded, as when covered with a draped cloth. We propose a framework for 3D shape perception that explains perception in both typical and atypical cases as analysis-by-synthesis, or inference in a generative model of image formation: the model integrates intuitive physics to explain how shape can be inferred from deformations it causes to other objects, as in cloth-draping. Behavioral and computational studies comparing this account with several alternatives show that it best matches human observers in both accuracy and response times, and is the only model that correlates significantly with human performance on difficult discriminations. Our results suggest that bottom-up deep neural network models are not fully adequate accounts of human shape perception, and point to how machine vision systems might achieve more human-like robustness. Copyright © 2023, The Authors. All rights reserved.

关键词： Deep neural networks

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SUMMARY ON THE ICASSP 2022 MULTI-CHANNEL MULTI-PARTY MEETING TRANSCRIPTION GRAND CHALLENGE

arXiv

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arXiv 2022年

作者： Yu, Fan Zhang, Shiliang Guo, Pengcheng Fu, Yihui Du, Zhihao Zheng, Siqi Huang, Weilong Xie, Lei Tan, Zheng-Hua Wang, DeLiang Qian, Yanmin Lee, Kong Aik Yan, Zhijie Ma, Bin Xu, Xin Bu, Hui Speech Lab Alibaba Group China Speech Lab Alibaba Group Singapore AISHELL Foundation China Beijing Shell Shell Technology Co. Ltd. Beijing China Department of Electronic Systems Aalborg University Aalborg Denmark Department of Computer Science and Engineering Center for Cognitive and Brain Sciences The Ohio State University United States SpeechLab Department of Computer Science and Engineering Shanghai Jiao Tong University China Aural and Language Intelligence Department Institute for Infocomm Research A*STAR Singapore

The ICASSP 2022 Multi-channel Multi-party Meeting Transcription Grand Challenge (M2MeT) focuses on one of the most valuable and the most challenging scenarios of speech technologies. The M2MeT challenge has particularly set up two tracks, speaker diarization (track 1) and multi-speaker automatic speech recognition (ASR) (track 2). Along with the challenge, we released 120 hours of real-recorded Mandarin meeting speech data with manual annotation, including far-field data collected by 8-channel microphone array as well as near-field data collected by each participants’ headset microphone. We briefly describe the released dataset, track setups, baselines and summarize the challenge results and major techniques used in the submissions. © 2022, CC BY-SA.

关键词： Speech recognition

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