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Efficient brain decoding Based on Adaptive EEG Channel Selection and Transformation

IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE 2022年第6期6卷 1314-1323页

作者： Wang, Jiaxing Shi, Lei Wang, Weiqun Hou, Zeng-Guang Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Meituan Inc Beijing 100102 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China CAS Ctr Excellence Brain Sci & Intelligence Tech Beijing 100190 Peoples R China Macau Univ Sci & Technol Inst Syst Engn MUST CASIA Joint Lab Intelligence Sci & Technol Macau Peoples R China

Electroencephalography (EEG) based brain-computer interface (BCI) has a wide range of applications in neuro-rehabilitation and motor assistance. However, brain activities, acquired from a large number of EEG channels, are highly inter-correlated or irrelevant to the brain decoding task, thus reducing the decoding efficiency and accuracy. How to adaptively select the optimal channel number depend on different trials remains a big challenge. To solve this problem, an efficient end-to-end brain decoding model named AdaEEGNet, is proposed in this study. It can reduce the computational cost by adaptively controlling the number of input channels and improve the classification accuracy by reducing over-fitting. Specifically, a lightweight policy module is designed to analyze which channel is needed for decoding current EEG trial. Due to the channel selection process is indifferentiable, we propose to use the Gumbel-Estimator to back-propagate the gradient to train the whole framework. Additionally, a weight coefficient is designed to make a trade-off between brain decoding accuracy and efficiency. To validate the proposed AdaEEGNet feasibility in improving decoding efficiency and accuracy, extensive experiments were conducted on BCI competition IV dataset. The results show that our methods can improve the decoding accuracy by 2% with only 65% computational cost significantly compared with the baseline method.

关键词： Electroencephalography decoding brain modeling Computational modeling Optimized production technology Feature extraction Data models Channel selection channel transformation brain decoding computational cost classification accuracy

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Bayesian hypothesis testing for pattern discrimination in brain decoding

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PATTERN RECOGNITION 2012年第6期45卷 2075-2084页

作者： Olivetti, Emanuele Veeramachaneni, Sriharsha Nowakowska, Ewa Bruno Kessler Fdn NeuroInformat Lab NILab I-38121 Trento Italy Univ Trento Ctr Mind & Brain Sci CIMeC I-38121 Trento Italy WindLogics Inc St Paul MN USA GIK Polonia Multivariate Stat Grp Warsaw Poland

Research in cognitive neuroscience and in brain-computer interfaces (BCI) is frequently concerned with finding evidence that a given brain area processes, or encodes, given stimuli. Experiments based on neuroimaging techniques consist of a stimulation protocol presented to a subject while his or her brain activity is being recorded. The question is then whether there is enough evidence of brain activity related to the stimuli within the recorded data. Finding a link between brain activity and stimuli has recently been proposed as a classification task, called brain decoding. A classifier that can accurately predict which stimuli were presented to the subject provides support for a positive answer to the question. However, it is only the answer for a given data set and the question still remains whether it is a general rule that will apply also to new data. In this paper we try to reliably answer the neuroscientific question about the presence of a significant link between brain activity and stimuli once we have the classification results. The proposed method is based on a Beta-Binomial model for the population of generalization errors of classifiers from multi-subject studies within the Bayesian hypothesis testing framework. We present an application on nine brain decoding investigations from a real functional magnetic resonance imaging (fMRI) experiment about the relation between mental calculation and eye movements. (C) 2011 Elsevier Ltd. All rights reserved.

关键词： Interpretability and validation brain decoding Generalization error Hypothesis testing

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An Adaptively Weighted Averaging Method for Regional Time Series Extraction of fMRI-Based brain decoding

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IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS 2024年第10期28卷 5984-5995页

作者： Zhu, Jianfei Wei, Baichun Tian, Jiaru Jiang, Feng Yi, Chunzhi Harbin Inst Technol Fac Comp Harbin 150001 Peoples R China Harbin Inst Technol Sch Med & Hlth Harbin 150001 Peoples R China Harbin Inst Technol Sch Int Studies Harbin 150001 Peoples R China Nanjing Univ Informat Sci & Technol Sch Artificial Intelligence Nanjing 210044 Peoples R China

brain decoding that classifies cognitive states using the functional fluctuations of the brain can provide insightful information for understanding the brain mechanisms of cognitive functions. Among the common procedures of decoding the brain cognitive states with functional magnetic resonance imaging (fMRI), extracting the time series of each brain region after brain parcellation traditionally averages across the voxels within a brain region. This neglects the spatial information among the voxels and the requirement of extracting information for the downstream tasks. In this study, we propose to use a fully connected neural network that is jointly trained with the brain decoder to perform an adaptively weighted average across the voxels within each brain region. We perform extensive evaluations by cognitive state decoding, manifold learning, and interpretability analysis on the Human Connectome Project (HCP) dataset. The performance comparison of the cognitive state decoding presents an accuracy increase of up to 5% and stable accuracy improvement under different time window sizes, resampling sizes, and training data sizes. The results of manifold learning show that our method presents a considerable separability among cognitive states and basically excludes subject-specific information. The interpretability analysis shows that our method can identify reasonable brain regions corresponding to each cognitive state. Our study would aid the improvement of the basic pipeline of fMRI processing.

关键词： Task analysis decoding Functional magnetic resonance imaging Time series analysis Vectors Feature extraction Data mining brain decoding deep learning regional time series task fMRI

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brain decoding OF FMRI CONNECTIVITY GRAPHS USING DECISION TREE ENSEMBLES

BRAIN DECODING OF FMRI CONNECTIVITY GRAPHS USING DECISION TR...

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7th IEEE International Symposium on Biomedical Imaging: From Nano to Macro

作者： Richiardi, Jonas Eryilmaz, Hamdi Schwartz, Sophie Vuilleumier, Patrik Van De Ville, Dimitri Ecole Polytech Fed Lausanne Med Image Proc Lab CH-1015 Lausanne Switzerland Univ Geneva Med Image Proc Lab CH-1211 Geneva Switzerland Univ Geneva Lab Neurol & Imaging Cognit CH-1211 Geneva Switzerland

ISBN: (纸本)9781424441266

Functional connectivity analysis of fMRI data can reveal synchronized activity between anatomically distinct brain regions. Here, we exploit the characteristic connectivity graphs of task and resting epochs to perform classification between these conditions. Our approach is based on ensembles of decision trees, which combine powerful discriminative ability with interpretability of results. This makes it possible to extract discriminative graphs that represent a subset of the connections that distinguish best between the experimental conditions. Our experimental results also show that the method can be applied for group-level brain decoding.

关键词： fMRI brain decoding functional connectivity graphs decision tree

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A Bayesian probit model with spatially varying coefficients for brain decoding using fMRI data

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STATISTICS IN MEDICINE 2016年第24期35卷 4380-4397页

作者： Zhang, Fengqing Jiang, Wenxin Wong, Patrick Wang, Ji-Ping Northwestern Univ Dept Stat Evanston IL 60208 USA Drexel Univ Dept Psychol Philadelphia PA 19104 USA Chinese Univ Hong Kong Dept Linguist & Modern Languages Shatin Hong Kong Peoples R China

Recent advances in human neuroimaging have shown that it is possible to accurately decode how the brain perceives information based only on non-invasive functional magnetic resonance imaging measurements of brain activity. Two commonly used statistical approaches, namely, univariate analysis and multivariate pattern analysis often lead to distinct patterns of selected voxels. One current debate in brain decoding concerns whether the brain's representation of sound categories is localized or distributed. We hypothesize that the distributed pattern of voxels selected by most multivariate pattern analysis models can be an artifact due to the spatial correlation among voxels. Here, we propose a Bayesian spatially varying coefficient model, where the spatial correlation is modeled through the variance-covariance matrix of the model coefficients. Combined with a proposed region selection strategy, we demonstrate that our approach is effective in identifying the truly localized patterns of the voxels while maintaining robustness to discover truly distributed pattern. In addition, we show that localized or clustered patterns can be artificially identified as distributed if without proper usage of the spatial correlation information in fMRI data. Copyright (c) 2016 John Wiley & Sons, Ltd.

关键词： brain decoding multivariate pattern analysis fMRI variable selection classification

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Explainable fMRI-based brain decoding via spatial temporal-pyramid graph convolutional network

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HUMAN brain MAPPING 2023年第7期44卷 2921-2935页

作者： Ye, Ziyuan Qu, Youzhi Liang, Zhichao Wang, Mo Liu, Quanying Southern Univ Sci & Technol Dept Biomed Engn Shenzhen Guangdong Peoples R China Southern Univ Sci & Technol Dept Biomed Engn Shenzhen Key Lab Smart Healthcare Engn 1088 Xueyuan Rd Shenzhen 518055 Guangdong Peoples R China

brain decoding, aiming to identify the brain states using neural activity, is important for cognitive neuroscience and neural engineering. However, existing machine learning methods for fMRI-based brain decoding either suffer from low classification performance or poor explainability. Here, we address this issue by proposing a biologically inspired architecture, Spatial Temporal-pyramid Graph Convolutional Network (STpGCN), to capture the spatial-temporal graph representation of functional brain activities. By designing multi-scale spatial-temporal pathways and bottom-up pathways that mimic the information process and temporal integration in the brain, STpGCN is capable of explicitly utilizing the multi-scale temporal dependency of brain activities via graph, thereby achieving high brain decoding performance. Additionally, we propose a sensitivity analysis method called brainNetX to better explain the decoding results by automatically annotating task-related brain regions from the brain-network standpoint. We conduct extensive experiments on fMRI data under 23 cognitive tasks from Human Connectome Project (HCP) S1200. The results show that STpGCN significantly improves brain-decoding performance compared to competing baseline models;brainNetX successfully annotates task-relevant brain regions. Post hoc analysis based on these regions further validates that the hierarchical structure in STpGCN significantly contributes to the explainability, robustness and generalization of the model. Our methods not only provide insights into information representation in the brain under multiple cognitive tasks but also indicate a bright future for fMRI-based brain decoding.

关键词： brain decoding brain-inspired models cognitive tasks fMRI graph neural networks human connectome project model explainability

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Hierarchical multi-resolution mesh networks for brain decoding

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brain IMAGING AND BEHAVIOR 2018年第4期12卷 1067-1083页

作者： Ertugrul, Itir Onal Ozay, Mete Vural, Fatos T. Yarman Middle East Tech Univ Dept Comp Engn Ankara Turkey Tohoku Univ Grad Sch Informat Sci Sendai Miyagi Japan

Human brain is supposed to process information in multiple frequency bands. Therefore, we can extract diverse information from functional Magnetic Resonance Imaging (fMRI) data by processing it at multiple resolutions. We propose a framework, called Hierarchical Multi-resolution Mesh Networks (HMMNs), which establishes a set of brain networks at multiple resolutions of fMRI signal to represent the underlying cognitive process. Our framework, first, decomposes the fMRI signal into various frequency subbands using wavelet transform. Then, a brain network is formed at each subband by ensembling a set of local meshes. Arc weights of each local mesh are estimated by ridge regression. Finally, adjacency matrices of mesh networks obtained at different subbands are used to train classifiers in an ensemble learning architecture, called fuzzy stacked generalization (FSG). Our decoding performances on Human Connectome Project task-fMRI dataset reflect that HMMNs can successfully discriminate tasks with 99% accuracy, across 808 subjects. Diversity of information embedded in mesh networks of multiple subbands enables the ensemble of classifiers to collaborate with each other for brain decoding. The suggested HMMNs decode the cognitive tasks better than a single classifier applied to any subband. Also mesh networks have a better representation power compared to pairwise correlations or average voxel time series. Moreover, fusion of diverse information using FSG outperforms fusion with majority voting. We conclude that, fMRI data, recorded during a cognitive task, provide diverse information in multi-resolution mesh networks. Our framework fuses this complementary information and boosts the brain decoding performances obtained at individual subbands.

关键词： fMRI data analysis brain decoding Wavelet decomposition Mesh networks Hierarchical models Ensemble models

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Real-Life Self-Control is Predicted by Parietal Activity During Preference Decision Making: A brain decoding Analysis

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COGNITIVE AFFECTIVE & BEHAVIORAL NEUROSCIENCE 2021年第5期21卷 936-947页

作者： Kroenke, Klaus-Martin Mohr, Holger Wolff, Max Kraeplin, Anja Smolka, Michael N. Buehringer, Gerhard Ruge, Hannes Goschke, Thomas Tech Univ Dresden Fac Psychol D-01062 Dresden Germany Tech Univ Dresden Dept Psychiat & Psychotherapy Dresden Germany Tech Univ Dresden Neuroimaging Ctr Dresden Germany Univ Southern Denmark Fac Hlth Dept Clin Res Odense Denmark

Despite its relevance for health and education, the neurocognitive mechanism of real-life self-control is largely unknown. While recent research revealed a prominent role of the ventromedial prefrontal cortex in the computation of an integrative value signal, the contribution and relevance of other brain regions for real-life self-control remains unclear. To investigate neural correlates of decisions in line with long-term consequences and to assess the potential of brain decoding methods for the individual prediction of real-life self-control, we combined functional magnetic resonance imaging during preference decision making with ecological momentary assessment of daily self-control in a large community sample (N = 266). Decisions in line with long-term consequences were associated with increased activity in bilateral angular gyrus and precuneus, regions involved in different forms of perspective taking, such as imagining one's own future and the perspective of others. Applying multivariate pattern analysis to the same clusters revealed that individual patterns of activity predicted the probability of real-life self-control. brain activations are discussed in relation to episodic future thinking and mentalizing as potential mechanisms mediating real-life self-control.

关键词： Self-control Value-based decision making Ecological momentary assessment Ecological validity brain decoding

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Visual Representation Model for fMRI-based brain decoding 2019

Visual Representation Model for fMRI-based Brain Decoding

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2nd International Conference on Electronics, Communications and Control Engineering (ICECC)

作者： Saengpetch, Piyawat Pipanmemekaporn, Luepol Kamolsantiroj, Suwatchai King Mongkuts Univ Technol North Bangkok Dept Comp & Informat Sci Bangkok 10800 Thailand

ISBN: (纸本)9781450362634

Classification of brain activity patterns has enabled to infer what a person in mind. Among brain activity signals, functional Magnetic Resonance Imaging (fMRI) is considered as the most reliable source to decode the neural activity patterns. While fMRI-based brain decoders exist, the accuracy is often limited by lacking of training samples. Most of the existing fMRI-based brain decoders overcome the limitation by designing semantic features for encoding numerous classes and training a model to predict these encoded features from fMRI patterns. However, what kind of features for accurately decoding the fMRI patterns remain unclear. In this work, a novel fMRI-based brain decoding model is present. Unlike traditional approaches that use text-derived features, we show that visual features derived from images can be well predicted for decoding fMRI patterns. Moreover, we propose a multi-task method that effectively learns a compact but discriminative model for characterizing neural coding. Experimental results conducted on fMRI datasets demonstrated that our proposed models achieve encouraging performance compared to state-of-the-art fMRI-based brain decoders.

关键词： brain decoding fMRI Multi-task learning Machine Learning

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IMPROVING brain decoding THROUGH CONSTRAINED AND PARAMETRIZED TEMPORAL SMOOTHING 11

IMPROVING BRAIN DECODING THROUGH CONSTRAINED AND PARAMETRIZE...

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11th IEEE International Symposium on Biomedical Imaging (ISBI)

作者： Markides, Loizos Gillies, Duncan F. Imperial Coll London Dept Comp London SW7 2AZ England

ISBN: (纸本)9781467319614

decoding mental states from task-related fMRI data has recently been the focus of much research. Nevertheless, high levels of acquisition and physiological noise still makes inter-subject decoding a difficult and quite unstable process. Since all of the existing decoding approaches are applied on a volume-by-volume basis, it would be sensible to ensure that sudden signal changes reflect a true change of cognitive state rather than noise artefacts. Correction of the temporal signal can be achieved through temporal smoothing, which over the years has always been a debatable fMRI preprocessing step among the neuroscience community. In this paper, we present two methods for improving decoding accuracy by correcting the temporal dynamics of a number of functional regions, using parametrized temporal smoothing. We test our methods on a real fMRI dataset and we show that when temporal smoothing is applied separately in multiple scales and is both properly constrained and conditioned, it can remove sudden artefact-driven peaks and drops from the fMRI signal and thus improve the prediction accuracy of different tasks. Moreover, since our methods are performed independently from the decoding operations, they can be used in conjunction with any feature selection and classification algorithm.

关键词： fMRI brain decoding temporal smoothing window autocorrelation

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