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32nd IEEE Signal Processing and Communications Applications Conference (SIU)

作者： Cakar, Tuna Girisken, Yener Tuna, Esin Filiz, Gozde Drias, Yassine MEF Univ Bilgisayar Muhendisligi Istanbul Turkiye Uluslararasi Final Univ Iktisadi & Idari Bilimler Fak Istanbul Turkiye MEF Univ Psikol Dept Istanbul Turkiye MEF Univ Fen Bilimleri Enstitusu Istanbul Turkiye

ISBN: (纸本)9798350388978;9798350388961

This research examines the link between consumer brand perceptions and neural activity by employing Functional Near-Infrared Spectroscopy (fNIRS) and machine learning techniques. The study analyzes the neural projections of participants' reactions to brand-associated adjectives, processing data collected from 168 individuals through machine learning algorithms. The findings underscore the significance of the lateral regions of the prefrontal cortex in the decision- making process related to brand perceptions. The aim is to understand how brands are perceived when associated with various adjectives and to develop this understanding through neural patterns using machine learning models. This study demonstrates the potential of integrating neural data with machine learning methods in the field of applied neuroscience.

关键词： Functional near-infrared spectroscopy neural decoding Brand Perception Machine Learning Neuromarketing

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neural decoding of continuous upper limb movements: a meta-analysis

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DISABILITY AND REHABILITATION-ASSISTIVE TECHNOLOGY 2022年第7期17卷 731-737页

作者： Fard, Mahdie Khaliq Fallah, Ali Maleki, Ali Amirkabir Univ Technol Dept Biomed Engn Tehran Iran Semnan Univ Semnan Iran

Objective EEG-based motion trajectory decoding makes a promising approach for neurotechnology which can be used for neural control of motion reconstruction and neurorehabilitation tools. However, the feasibility and validity of continuous motion decoding by non-invasive brain activity are not clear. The main aim of this study was to perform a meta-analysis across studies that examined the ability of EEG-based continuous motion decoding of upper limb movements. Approach Pearson's correlation coefficient (CC) was used to evaluate the model performance of the studies and considered as an effect size. To estimate the overall effect size of neural decoding of motion trajectory across studies, characteristics of included studies were addressed and the random effect model was applied to the heterogeneous studies which estimated overall effect size distribution. Furthermore, the significant difference between the two subgroups of imagined and executed movements was analysed. Main results The mean of the overall effect size was computed 0.46 across the nonhomogeneous studies. The results showed no significant difference between imagined and executed movements (Chi(2)=0.28, df = 1, p = 0.60). Significance Meta-analysis results confirm that imagination like execution movements can be used for neural decoding of motion trajectory in neural motor control systems. Also, nonlinear compare with linear model statistically confirmed to be more beneficial for complex movements. Furthermore, a new approach of synergy-based motion decoding can be significantly effective to increase model performance and more research needs to evaluate this method for different levels of complexity of movements.

关键词： neural decoding continuous movements upper limb meta-analysis

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neural decoding of movements: From linear to nonlinear trajectory models

Neural decoding of movements: From linear to nonlinear traje...

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14th International Conference on neural Information Processing (ICONIP 2007)

作者： Yu, Byron M. Cunningham, John P. Shenoy, Krishna V. Sahani, Maneesh Stanford Univ Dept Elect Engn & Neurosci Stanford CA 94305 USA UCL Gatsby Computat Neurosci Unit London England

ISBN: (纸本)9783540691549

To date, the neural decoding of time-evolving physical state for example, the path of a foraging rat or arm movements - has been largely carried out using linear trajectory models, primarily due to their computational efficiency. The possibility of better capturing the statistics of the movements using nonlinear trajectory models, thereby yielding more accurate decoded trajectories, is enticing. However, nonlinear decoding usually carries a higher computational cost, which is an important consideration in real-time settings. In this paper, we present techniques for nonlinear decoding employing modal Gaussian approximations, expectatation propagation, and Gaussian quadrature. We compare their decoding accuracy versus computation time tradeoffs based on high-dimensional simulated neural spike counts.

关键词： nonlinear dynamical models nonlinear state estimation neural decoding neural prosthetics expectation-propagation Gaussian quadrature

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neural decoding USING A NONLINEAR GENERATIVE MODEL FOR BRAIN-COMPUTER INTERFACE

NEURAL DECODING USING A NONLINEAR GENERATIVE MODEL FOR BRAIN...

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IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

作者： Dantas, Henrique Kellis, Spencer Mathews, V. John Greger, Bradley Univ Fed Pernambuco Recife PE Brazil CALTECH Biol & Biol Engn Div Pasadena CA 91125 USA Univ Utah Dept Elect & Comp Engn Pasadena 84109 CA USA Arizona State Univ Sch Sch Biologicool Biol & Hlth Tempe AZ 85287 USA

ISBN: (纸本)9781479928934

Kalman filters have been used to decode neural signals and estimate hand kinematics in many studies. However, most prior work assumes a linear system model, an assumption that is almost certainly violated by neural systems. In this paper, we show that adding nonlinearities to the decoding algorithm improves the accuracy of tracking hand movements using neural signal acquired via a 32-channel micro-electrocorticographic (mu ECoG) grid placed over the arm and hand representations in the motor cortex. Experimental comparisons indicate that a Kalman filter with a fifth order polynomial generative model relating the hand kinematics signals to the neural signals improved the mean-square tracking performance in the hand movements over a conventional Kalman filter employing a linear system model. This finding is in accord with the current neurophysiological understanding of the decoded signals.

关键词： neural decoding Brain-Computer Interface Nonlinear Kalman Filter

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neural decoding for Location of Macaque's Moving Finger Using Generative Adversarial Networks

Neural Decoding for Location of Macaque's Moving Finger Usin...

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IEEE International Conference of Intelligent Robotics and Control Engineering (IRCE)

作者： Feng, Jingyi Wu, Haifeng Zeng, Yu Yunnan Minzu Univ Sch Elect & Informat Technol Kunming Yunnan Peoples R China

ISBN: (纸本)9781538674161

In this paper, we will study the problem that the location of a macaque's moving finger is decoded through neuron spike signals in its motor cortex. In traditional neural decoding methods, supervised learning is more popular because their decoding accuracy could be guaranteed. However, this paper proposes a weak supervised learning (WSL) method to decode the location of a macaque's moving finger and an unsupervised deep learning model, generative adversarial network (GAN) is introduced to optimize a training model and ultimately achieve an accurate estimation of the finger movement trajectory. In experiments, we use public data to evaluate the decoding performance of the proposed method. The experimental results show that this WSL method has higher accuracy of the location decoding.

关键词： neural decoding WSL GAN

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Jointly Fusing Multi-Scale Spatial-Logical Brain Networks: A neural decoding Method

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IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS 2023年第1期27卷 445-456页

作者： Li, Ziyu Zhu, Zhiyuan Li, Qing Wu, Xia Beijing Normal Univ Engn Res Ctr Intelligent Technol & Educ Applicat Sch Artificial Intelligence Minist Educ Beijing 100875 Peoples R China Beijing Normal Univ State Key Lab Cognit Neurosci & Learning Beijing 100875 Peoples R China

Functional magnetic resonance imaging (fMRI) is a methodology for measuring human brain activities. It has become more and more popular in neural decoding due to its noninvasive. neural decoding aims to establishing models to reconstruct external stimuli or features of stimuli from known brain responses, so that we can understand the principles of brain functions such as emotion, cognition and language. neural decoding based on fMRI is of great significance for further understanding the mechanism of brain operation. Most existing studies take multi-scale topology information of brain networks obtained from fMRI into account in neural decoding. However, they always ignore the simultaneous modeling of network structure and hemodynamic response, thus leading to information loss. In addition, current multi-scale methods usually only utilize spatial or logical reasoning relationship of brain networks, which brings challenge to precise neural decoding. In this work, we present a novel and robust multi-scale spatial and logical reasoning learning framework (MSLR) for fMRI-based neural decoding. Specifically, we first design graph signal wavelet generation module to combine brain network topology and node information to construct multi-scale representation of brain networks in a local to global manner. Then, we develop multi-scale information fusion module that can simultaneously model the spatial and logical reasoning relationship of brain networks, it can also learn discriminative multi-scale features with brain state transition. Finally, we construct a neural decoding module to predict the brain states. We evaluated the framework on the public Human Connectome Project (HCP) dataset that included 986 participants. The experimental results with support vector machine (SVM) outperform current state-of-the-art methods on four evaluation metrics (accuracy: 91.58, kappa coefficient: 0.883, macro F1: 0.865 and hamming distance: 0.105) under 19 different stimuli spanning 7 diffe

关键词： neural decoding multi-scale brain network hemodynamic response spatial and logical reasoning

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Multi-Scale Spatio-Temporal Fusion With Adaptive Brain Topology Learning for fMRI Based neural decoding

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IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS 2024年第1期28卷 262-272页

作者： Li, Ziyu Li, Qing Zhu, Zhiyuan Hu, Zhongyi Wu, Xia Beijing Normal Univ Sch Artificial Intelligence Beijing 100875 Peoples R China Beijing Normal Univ State Key Lab Cognit Neurosci & Learning Beijing 100875 Peoples R China Wenzhou Univ Coll Comp Sci & Artificial Intelligence Wenzhou 325035 Peoples R China

neural decoding aims to extract information from neurons' activities to reveal how the brain functions. Due to the inherent spatial and temporal characteristics of brain signals, spatio-temporal computing has become a hot topic for neural decoding. However, the extant spatio-temporal decoding methods usually use static brain topology, ignoring the dynamic patterns of the interaction between brain regions. Further, they do not identify the hierarchical organization of brain topology, leading to only superficial insight into brain spatio-temporal interactions. Therefore, here we propose a novel framework, the Multi-Scale Spatio-Temporal framework with Adaptive Brain Topology Learning (MSST-ABTL), for neural decoding. It includes two new capabilities to enhance spatio-temporal decoding: i) ABTL module, which learns dynamic brain topology while updating specific patterns of brain regions, ii) MSST module, which captures the association of spatial pattern and temporal evolution, and further enhances the interpretability of the learned dynamic topology from multi-scale perspective. We evaluated the framework on the public Human Connectome Project (HCP) dataset (resting-state and task-related fMRI data). The extensive experiments show that the proposed MSST-ABTL outperforms state-of-the-art methods on four evaluation metrics, and also can renew the neuroscientific discoveries in the brain's hierarchical patterns.

关键词： neural decoding adaptive brain topology multi-scale spatio-temporal

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neural decoding OF MOVEMENT TARGETS BY UNSORTED SPIKE TRAINS

NEURAL DECODING OF MOVEMENT TARGETS BY UNSORTED SPIKE TRAINS

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IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Xu, Zhiming Ang, Kai Keng Guan, Cuntai ASTAR Inst Infocomm Res Singapore 138632 Singapore

ISBN: (纸本)9781479903566

decoding movement targets from neural activity in motor cortex using invasive brain-computer interface (BCI) has potential application to help disabled patients. Most works employed spike sorting to obtain the single units (SUs) for decoding from the extracellular electrode recordings. However, spike sorting is difficult, computational demanding, and is often limited by the spike waveform variability especially in low SNR and high neuronal density conditions. To address these issues, we proposed a decoding method using unsorted spike trains from recording electrodes based on the maximal likelihood (ML) estimation approach. An experiment was performed to test neuronal data recorded from a rhesus monkey performing the center-out movement task of eight targets. The results showed that the proposed method yielded average correct decoding rate of 98.5% compared to the SU based method that yielded correct decoding rate of 96.3%. The results also showed that the proposed method yielded improved computational efficiency. Thus the proposed method showed potential for real time BCI applications with large scale of neuronal recordings.

关键词： Brain computer interface maximal likelihood motor cortex neural decoding Poisson distribution

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A Study on Combining Local Field Potential and Single Unit Activity for Better neural decoding

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INTERNATIONAL JOURNAL OF IMAGING SYSTEMS AND TECHNOLOGY 2011年第2期21卷 165-172页

作者： Zhang, Shaomin Jiang, Bo Zhu, Junming Zhang, Qiaosheng Chen, Weidong Zheng, Xiaoxiang Zhao, Ting Zhejiang Univ Qiushi Acad Adv Studies Hangzhou 310027 Peoples R China Zhejiang Univ Dept Biomed Engn Hangzhou 310027 Peoples R China Zhejiang Univ Coll Comp Sci Hangzhou 310027 Peoples R China

Recent studies showed that the local field potential (LFP) in motor cortex carries information about parameters of limb movements and could be used as a candidate neural signal in brain-machine interfaces to control external devices. However, it is yet to be clear how much information LFP can offer and how it can be effectively used in BMIs. In this article, we systematically evaluated the decoding performance of combining LFP and single-unit activity (SUA) from the primary motor cortex of rats performing the lever-pressing task. The results showed that the decoding power could be significantly improved by combining SUA and LFP in the time-frequency mode, which is based on the separation of LFP into multiple frequency bands. Furthermore, we found that using all frequency bands might be the best choice because it yielded better or no significantly worse results than using low or high frequency bands only. This implies that different frequency components of LFP carry different information. Moreover, we demonstrated that the combination could stabilize the decoding performance even if SUA disappears over time. These results suggest that the different frequency components in the LFP play different roles in kinematics decoding and the combination of LFPs and SUA could be a promising strategy for improving neural decoding in brain-machine interfaces. (C) 2011 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 21, 165-172, 2011;Published online in Wiley Online Library (***). DOI 10.1002/ima.20287

关键词： brain-machine interface local field potential single-unit activity neural decoding time-frequency domain

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Voxel selection and neural decoding of fMRI data based on robust sparse programming with multi-dimensional derivative constraints

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MULTIDIMENSIONAL SYSTEMS AND SIGNAL PROCESSING 2015年第1期26卷 225-241页

作者： Yu, Zhuliang Feng, Bao Gu, Zhenghui Xue, Zhenxia Li, Yuanqing Wang, Cong S China Univ Technol Ctr Brain Comp Interfaces & Brain Informat Proc Guangzhou 510641 Guangdong Peoples R China S China Univ Technol Guangzhou 510641 Guangdong Peoples R China Henan Univ Sci & Technol Luoyang 471023 Peoples R China

Recently, sparse representation has received a great deal of attention in voxel selection and decoding analysis of functional magnetic resonance imaging (fMRI) data. Due to contamination of large noise in fMRI data, the conventional sparse representation methods may not get stable results. Moreover, the selected activated brain regions may lose clustering effects and are less biologically interpretable. In order to overcome the above mentioned problems, we exploit the error-tolerant formulation of sparse representation and introduce multi-dimensional derivative constraints (smoothness constraints) in optimization. Two new methods are proposed in this paper. One is robust voxel selection with multi-dimensional constraint (RVSMDC). With the error-tolerant formulation and smoothness constraints on regression coefficients, RVSMDC is robust against noise/error and achieves clustering effects. To directly decode neural activities from fMRI data, we also proposed robust sparse decoding with multi-dimensional constraints (RSDMDC), which minimize the regression error of fMRI data to the task function with sparsity and smoothness constraints on regression coefficients. Numerical results validate the effectiveness of the two proposed methods.

关键词： Voxel selection neural decoding fMRI Sparse programming Multi-dimensional derivative constraints

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