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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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Neuron selection by relative importance for neural decoding of dexterous finger prosthesis control application

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BIOMEDICAL SIGNAL PROCESSING AND CONTROL 2012年第6期7卷 632-639页

作者： Kim, Hyoung-Nam Kim, Yong-Hee Shin, Hyun-Chool Aggarwal, Vikram Schieber, Marc H. Thakor, Nitish V. Johns Hopkins Sch Med Dept Biomed Engn Baltimore MD 21205 USA Pusan Natl Univ Dept Elect Engn Pusan 609735 South Korea Soongsil Univ Sch Elect Engn Seoul 156734 South Korea Univ Rochester Med Ctr Dept Neurol Neurobiol & Anat Rochester NY 14642 USA

Future generations of upper limb prosthesis will have dexterous hand with individual fingers and will be controlled directly by neural signals. Neurons from the primary motor (M1) cortex code for finger movements and provide the source for neural control of dexterous prosthesis. Each neuron's activation can be quantified by the change in firing rate before and after finger movement, and the quantified value is then represented by the neural activity over each trial for the intended movement. Since this neural activity varies with the intended movement, we define the relative importance of each neuron independent of specific intended movements. The relative importance of each neuron is determined by the inter-movement variance of the neural activities for respective intended movements. Neurons are ranked by the relative importance and then a subpopulation of rank-ordered neurons is selected for the neural decoding. The use of the proposed neuron selection method in individual finger movements improved decoding accuracy by 21.5% in the case of decoding with only 5 neurons and by 9.2% in the case of decoding with only 10 neurons. With only 15 highly ranked neurons, a decoding accuracy of 99.5% was achieved. The performance improvement is still maintained when combined movements of two fingers were included though the decoding accuracy fell to 95.7%. Since the proposed neuron selection method can achieve the targeting accuracy of decoding algorithms with less number of input neurons, it can be significant for developing brain-machine interfaces for direct neural control of hand prostheses. (C) 2012 Elsevier Ltd. All rights reserved.

关键词： Brain-machine interface (BMI) neural decoding Relative importance Neuron selection

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Towards in vivo neural decoding

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BIOMEDICAL ENGINEERING LETTERS 2022年第2期12卷 185-195页

作者： Valencia, Daniel Alimohammad, Amir San Diego State Univ Dept Elect & Comp Engn San Diego CA 92182 USA

Conventional spike sorting and motor intention decoding algorithms are mostly implemented on an external computing device, such as a personal computer. The innovation of high-resolution and high-density electrodes to record the brain's activity at the single neuron level may eliminate the need for spike sorting altogether while potentially enabling in vivo neural decoding. This article explores the feasibility and efficient realization of in vivo decoding, with and without spike sorting. The efficiency of neural network-based models for reliable motor decoding is presented and the performance of candidate neural decoding schemes on sorted single-unit activity and unsorted multi-unit activity are evaluated. A programmable processor with a custom instruction set architecture, for the first time to the best of our knowledge, is designed and implemented for executing neural network operations in a standard 180-nm CMOS process. The processor's layout is estimated to occupy 49 mm(2) of silicon area and to dissipate 12 mW of power from a 1.8 V supply, which is within the tissue-safe operation of the brain.

关键词： neural decoding Brain-machine interfaces Application-specific integrated circuits

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A neural decoding strategy based on convolutional neural network

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2020年第1期39卷 1033-1044页

作者： Hua, Shaoyang Wang, Congqing Wu, Xuewei Nanjing Univ Aeronaut & Astronaut Coll Automat Engn Nanjing Peoples R China

neural decoding is a technology to analyze intentions produced by neural activities, which has important applications in military, medical, entertainment and so on. As a typical application, decoding electromyogram (EMG) signals into corresponding gestures is an important content. In order to improve the accuracy of EMG signals recognition, researchers often extract effective features from EMG signals and classify gestures by constructing a reasonable classifier. However, because of the stochasticity of the signals, this method is not robust enough. This paper proposes a convolutional neural network (CNN) based on feature fusion, which can automatically learn and classify features from time-domain(TD) and frequency-domain(FD). To make full use of information, two fusion methods are used and compared. Experiments show that the proposed fusion methods are superior to the traditional algorithm for both normal people and amputees, and have better performance compared with CNN method using only one kind of information.

关键词： Convolutional neural network (CNN) gestures recognition neural decoding surface electromyogram (sEMG)

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Review: Human Intracortical Recording and neural decoding for Brain-Computer Interfaces

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IEEE TRANSACTIONS ON neural SYSTEMS AND REHABILITATION ENGINEERING 2017年第10期25卷 1687-1696页

作者： Brandman, David M. Cash, Sydney S. Hochberg, Leigh R. Brown Univ Brown Inst Brain Sci Neurosci Grad Program Providence RI 02912 USA Dalhousie Univ Dept Surg Neurosurg Halifax NS Canada Harvard Med Sch Massachusetts Gen Hosp Ctr Neurotechnol & Neurorecovery Neurol Boston MA USA Dept Vet Affairs Med Ctr Ctr Neurorestorat & Neurotechnol Rehabil R&D Serv Providence RI USA Brown Univ Brown Inst Brain Sci Sch Engn Providence RI 02912 USA

Brain-computer interfaces (BCIs) use neural information recorded from the brain for the voluntary control of external devices. The development of BCI systems has largely focused on improving functional independence for individuals with severe motor impairments, including providing tools for communication and mobility. In this review, we describe recent advances in intracortical BCI technology and provide potential directions for further research.

关键词： Brain computer interfaces ALS spinal cord injury neural decoding

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neural decoding SYSTEMS USING MARKOV DECISION PROCESSES

NEURAL DECODING SYSTEMS USING MARKOV DECISION PROCESSES

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

作者： Dantas, Henrique Mathews, V. John Wendelken, Suzanne M. Davis, Tyler S. Clark, Gregory A. Warren, David J. Oregon State Univ Sch Elect Engn & Comp Sci Corvallis OR 97331 USA Univ Utah Dept Bioengn Salt Lake City UT 84112 USA Univ Utah Dept Neurosurg Salt Lake City UT 84112 USA

ISBN: (纸本)9781509041176

This paper presents a framework for modeling neural decoding using electromyogram (EMG) and electrocorticogram (ECoG) signals to interpret human intent and control prosthetic arms. Specifically, the method of this paper employs Markov Decision Processes (MDP) for neural decoding, parameterizing the policy using an artificial neural network. The system is trained using a modification of the Dataset Aggregation (DAgger) algorithm. The results presented here suggest that the approach of the paper performs better than the state-of-the-art.

关键词： neural decoding Markov Decision Processes neural Network Dataset Aggregation Reinforcement Learning

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Neuron Selection Based on Deflection Coefficient Maximization for the neural decoding of Dexterous Finger Movements

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IEEE TRANSACTIONS ON neural SYSTEMS AND REHABILITATION ENGINEERING 2015年第3期23卷 374-384页

作者： Kim, Yong-Hee Thakor, Nitish V. Schieber, Marc H. Kim, Hyoung-Nam Pusan Natl Univ Dept Elect & Comp Engn Pusan 609735 South Korea Johns Hopkins Univ Dept Biomed Engn Baltimore MD 21218 USA Univ Rochester Dept Neurol Neurobiol & Anat Rochester NY 14642 USA Univ Rochester Dept Brain & Cognit Sci & Phys Med & Rehabil Rochester NY 14642 USA

Future generations of brain-machine interface (BMI) will require more dexterous motion control such as hand and finger movements. Since a population of neurons in the primary motor cortex (M1) area is correlated with finger movements, neural activities recorded in M1 area are used to reconstruct an intended finger movement. In a BMI system, decoding discrete finger movements from a large number of input neurons does not guarantee a higher decoding accuracy in spite of the increase in computational burden. Hence, we hypothesize that selecting neurons important for coding dexterous flexion/extension of finger movements would improve the BMI performance. In this paper, two metrics are presented to quantitatively measure the importance of each neuron based on Bayes risk minimization and deflection coefficient maximization in a statistical decision problem. Since motor cortical neurons are active with movements of several different fingers, the proposed method is more suitable for a discrete decoding of flexion-extension finger movements than the previous methods for decoding reaching movements. In particular, the proposed metrics yielded high decoding accuracies across all subjects and also in the case of including six combined two-finger movements. While our data acquisition and analysis was done off-line and post processing, our results point to the significance of highly coding neurons in improving BMI performance.

关键词： Brain-machine interfaces (BMI) neural decoding neural prosthesis neuron selection

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Fine-grained neural decoding with distributed word representations

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INFORMATION SCIENCES 2020年 507卷 256-272页

作者： Wang, Shaonan Zhang, Jiajun Wang, Haiyan Lin, Nan Zong, Chengqing CASIA Natl Lab Pattern Recognit Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China Chinese Acad Sci Inst Automat Brainnetome Ctr Beijing Peoples R China Inst Psychol CAS Key Lab Behav Sci Beijing Peoples R China Univ Chinese Acad Sci Dept Psychol Beijing Peoples R China CAS Ctr Excellence Brain Sci & Intelligence Techn Beijing Peoples R China

fMRI word decoding refers to decode what the human brain is thinking by interpreting functional Magnetic Resonance Imaging (fMRI) scans from people watching or listening to words, representing a sort of mind-reading technology. Existing works decoding words from imaging data have been largely limited to concrete nouns from a relatively small number of semantic categories. Moreover, such studies use different word-stimulus presentation paradigms and different computational models, lacking a comprehensive understanding of the influence of different factors on fMRI word decoding. In this paper, we present a large-scale evaluation of eight word embedding models and their combinations for decoding fine-grained fMRI data associated with three classes of words recorded from three stimulus-presentation paradigms. Specifically, we investigate the following research questions: (1) How does the brain-image decoder perform on different classes of words? (2) How does the brain-image decoder perform in different stimulus-presentation paradigms? (3) How well does each word embedding model allow us to decode neural activation patterns in the human brain? Furthermore, we analyze the most informative voxels associated with different classes of words, stimulus-presentation paradigms and word embedding models to explore their neural basis. The results have shown the following: (1) Different word classes can be decoded most effectively with different word embedding models. Concrete nouns and verbs are more easily distinguished than abstract nouns and verbs. (2) Among the three stimulus-presentation paradigms (picture, sentence and word clouds), the picture paradigm achieves the highest decoding accuracy, followed by the sentence paradigm. (3) Among the eight word embedding models, the model that encodes visual information obtains the best performance, followed by models that encode textual and contextual information. (4) Compared to concrete nouns, which activate mostly vision-related b

关键词： neural decoding fMRI word decoding Word class Stimuli paradigm Word embedding models Informative voxels

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A New Spike Sorting Algorithm Based on Continuous Wavelet Transform and Investigating Its Effect on Improving neural decoding Accuracy

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NEUROSCIENCE 2021年第0期468卷 139-148页

作者： Soleymankhani, Amir Shalchyan, Vahid Iran Univ Sci & Technol Neurosci & Neuroengn Res Lab Tehran Iran

Spike sorting is an essential step in extracting neuronal discharge patterns which help to decode different activities in the neural system. Therefore, improving the spike sorting accuracy can improve neural decoding performance subsequently. Although many methods are suggested for spike sorting, few studies have evaluated their effect on neural decoding performance. In this paper, a method of spike sorting based on an optimized selection of the parameters in the continuous wavelet transform (CWT) is proposed. The proposed algorithm was tested on a simulated dataset and two publicly available benchmark datasets to evaluate its performance in spike sorting. To evaluate the effect of utilizing different spike sorting algorithms on neural decoding performance, real data was used in which the aim was to decode the force applied by the rat's hand to a pedal continuously from the intra-cortical data of the primary motor area of the cortex. The extracted neuronal firing rates by the spike sorting algorithms were applied to a partial least squares regression to decode the force signal. In the simulation study, the proposed spike sorting algorithm based on optimized wavelet parameter selection outperformed both the WaveClus spike sorting and traditional PCA-based spike sorting algorithms. The results showed the superiority of the spike sorting algorithm based on optimal wavelet parameters compared to classical discrete wavelet transform (DWT) or PCA-based spike sorting methods in decoding real intracortical data. Overall, the results indicate that it is possible to improve neural decoding performance by improving the spike sorting accuracy. (c) 2021 IBRO. Published by Elsevier Ltd. All rights reserved.

关键词： continuous wavelet transform neural decoding optimization partial least squares spike sorting

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A neural decoding algorithm that generates language from visual activity evoked by natural images

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neural NETWORKS 2021年 144卷 90-100页

作者： Huang, Wei Yan, Hongmei Cheng, Kaiwen Wang, Chong Li, Jiyi Wang, Yuting Li, Chen Li, Chaorong Li, Yunhan Zuo, Zhentao Chen, Huafu Univ Elect Sci & Technol China Sch Life Sci & Technol MOE Key Lab Neuroinformat Clin HospChengdu Brain Sci Inst Chengdu 610054 Peoples R China Chinese Acad Sci Inst Biophys Beijing MR Ctr Brain Res State Key Lab Brain & Cognit Sci Beijing 100101 Peoples R China Sichuan Int Studies Univ Sch Language Intelligence Chongqing 400031 Peoples R China Sichuan Univ Dept Med Informat Engn Chengdu 610065 Peoples R China

Transforming neural activities into language is revolutionary for human-computer interaction as well as functional restoration of aphasia. Present rapid development of artificial intelligence makes it feasible to decode the neural signals of human visual activities. In this paper, a novel Progressive Transfer Language decoding Model (PT-LDM) is proposed to decode visual fMRI signals into phrases or sentences when natural images are being watched. The PT-LDM consists of an image-encoder, a fMRI encoder and a language-decoder. The results showed that phrases and sentences were successfully generated from visual activities. Similarity analysis showed that three often-used evaluation indexes BLEU, ROUGE and CIDEr reached 0.182, 0.197 and 0.680 averagely between the generated texts and the corresponding annotated texts in the testing set respectively, significantly higher than the baseline. Moreover, we found that higher visual areas usually had better performance than lower visual areas and the contribution curve of visual response patterns in language decoding varied at successively different time points. Our findings demonstrate that the neural representations elicited in visual cortices when scenes are being viewed have already contained semantic information that can be utilized to generate human language. Our study shows potential application of language-based brain- machine interfaces in the future, especially for assisting aphasics in communicating more efficiently with fMRI signals. (C) 2021 Elsevier Ltd. All rights reserved.

关键词： neural decoding Language decoding Visual activity Progressive transfer Human-computer interaction

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