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

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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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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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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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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s-TBN: A New neural decoding Model to Identify Stimulus Categories From Brain Activity Patterns

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IEEE TRANSACTIONS ON neural SYSTEMS AND REHABILITATION ENGINEERING 2024年 32卷 1934-1943页

作者： Liu, Chunyu Cao, Bokai Zhang, Jiacai North China Elect Power Univ Sch Control & Comp Engn Beijing 102206 Peoples R China Meta Inc Menlo Pk CA 94025 USA Beijing Normal Univ Sch Artificial Intelligence Beijing 100875 Peoples R China

neural decoding is still a challenging and a hot topic in neurocomputing science. Recently, many studies have shown that brain network patterns containing rich spatiotemporal structural information represent the brain's activation information under external stimuli. In the traditional method, brain network features are directly obtained using the standard machine learning method and provide to a classifier, subsequently decoding external stimuli. However, this method cannot effectively extract the multidimensional structural information hidden in the brain network. Furthermore, studies on tensors have show that the tensor decomposition model can fully mine unique spatiotemporal structural characteristics of a spatiotemporal structure in data with a multidimensional structure. This research proposed a stimulus-constrained Tensor Brain Network (s-TBN) model that involves the tensor decomposition and stimulus category-constraint information. The model was verified on real neuroimaging data obtained via magnetoencephalograph and functional mangetic resonance imaging). Experimental results show that the s-TBN model achieve accuracy matrices of greater than 11.06% and 18.46% on the accuracy matrix compared with other methods on two modal datasets. These results prove the superiority of extracting discriminative characteristics using the STN model, especially for decoding object stimuli with semantic information.

关键词： Tensors Brain modeling Data models Visualization Matrix decomposition decoding Analytical models neural decoding brain network tensor decomposition tensor brain network

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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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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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Structured neural decoding With Multitask Transfer Learning of Deep neural Network Representations

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IEEE TRANSACTIONS ON neural NETWORKS AND LEARNING SYSTEMS 2022年第2期33卷 600-614页

作者： Du, Changde Du, Changying Huang, Lijie Wang, Haibao He, Huiguang Chinese Acad Sci Res Ctr Brain Inspired Intelligence Natl Lab Pattern Recognit Inst Automat Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China Huawei Cloud BU EI Innovat Lab Beijing 100085 Peoples R China Huawei Noahs Ark Lab Beijing 100085 Peoples R China Chinese Acad Sci Ctr Excellence Brain Sci & Intelligence Technol Beijing 100190 Peoples R China

The reconstruction of visual information from human brain activity is a very important research topic in brain decoding. Existing methods ignore the structural information underlying the brain activities and the visual features, which severely limits their performance and interpretability. Here, we propose a hierarchically structured neural decoding framework by using multitask transfer learning of deep neural network (DNN) representations and a matrix-variate Gaussian prior. Our framework consists of two stages, Voxel2Unit and Unit2Pixel. In Voxel2Unit, we decode the functional magnetic resonance imaging (fMRI) data to the intermediate features of a pretrained convolutional neural network (CNN). In Unit2Pixel, we further invert the predicted CNN features back to the visual images. Matrix-variate Gaussian prior allows us to take into account the structures between feature dimensions and between regression tasks, which are useful for improving decoding effectiveness and interpretability. This is in contrast with the existing single-output regression models that usually ignore these structures. We conduct extensive experiments on two real-world fMRI data sets, and the results show that our method can predict CNN features more accurately and reconstruct the perceived natural images and faces with higher quality.

关键词： decoding Image reconstruction Functional magnetic resonance imaging Visualization Task analysis Brain Correlation Deep neural network (DNN) functional magnetic resonance imaging (fMRI) image reconstruction multioutput regression neural decoding

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