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19th International Symposium on Applied Reconfigurable Computing (ARC) - Architectures, Tools, and Applications

作者： Leone, Gianluca Martis, Luca Raffo, Luigi Meloni, Paolo Univ Cagliari Cagliari Italy

ISBN: (纸本)9783031429200;9783031429217

In the field of brain-machine interface (BMI), deep learning algorithms have been steadily advancing as the go-to instrument for the key task of neural decoding. However, to function in real-time on portable devices, these algorithms must adhere to stringent limitations on computational power and memory. In this work, we exploit spiking neural networks (SNNs) within a real-time neural decoding system deployed on a low-end Artix-7 FPGA. The system is capable of decoding the spike activity in intracortical neural signals, recorded by a 96-channels microelectrode array, to continuously and concurrently track five target variables in a reach-to-grasp experiment. We have assessed our approach on a widely used reference dataset, achieving a decoding accuracy comparable with alternatives in literature, which exploit more complex deep learning models on the same dataset to decode a single target variables. Our system uses around 20 times less parameters than other non-SNN approaches and consumes 56.4 mW.

关键词： neural decoding SNN FPGA Low power

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On neural decoding for some cyclic codes

On neural decoding for some cyclic codes

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1995 IEEE International Symposium on Information Theory

作者： Yu, JP Zhao, YB Wang, XM XIDIAN UNIV NATL KEY LAB ISNXIAN 710071PEOPLES R CHINA

ISBN: (纸本)0780324536

Based on analysis of the property of a class of cyclic codes, an algorithm for neural soft decision decoding of those codes is presented. The complexity of the new algorithm is much less than that of the available algorithms for decoding general linear block codes, and its performance is approached to that of the maximum likelihood decoding.

关键词： cyclic codes neural decoding cyclic codes soft decision decoding complexity maximum likelihood decoding error correcting codes linear block codes

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Online Learning in neural decoding Using Incremental Linear Discriminant Analysis

Online Learning in Neural Decoding Using Incremental Linear ...

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IEEE International Conference on Cyborg and Bionic Systems (CBS)

作者： Lee, Yaesop Madayambath, Sreenuj Chellath Liu, Yanzhou Lin, Da-Ting Chen, Rong Bhattacharyya, Shuvra S. Univ Maryland Dept Elect & Comp Engn College Pk MD 20742 USA NIDA NIH Baltimore MD USA Univ Maryland Dept Diagnost Radiol & Nucl Med Baltimore MD 21201 USA Tampere Univ Technol Dept Pervas Comp Tampere Finland

ISBN: (纸本)9781538631942

neural decoding focuses on predicting behavior variables based on neural activities. Linear discriminant analysis (LDA) has been successfully used in pattern recognition and machine learning to find the set of discriminant vectors to characterize two or more classes of objects. However, LDA cannot be directly used for real-time neural decoding problems. In this paper, we propose an incremental LDA with online learning method to overcome this limitation. The dataflow techniques are implemented in the LIDE (LIghtweight Dataflow Environment), which provides capabilities to systematically optimize and integrate embedded software components for signal and information processing. Using these techniques along with online learning, an efficient real-time neural decoding system can be attained.

关键词： neural decoding online learning incremental learning ILDA dataflow

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Foreground-attention in neural decoding: Guiding Loop-Enc-Dec to reconstruct visual stimulus images from fMRI

Foreground-attention in neural decoding: Guiding Loop-Enc-De...

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IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) / IEEE World Congress on Computational Intelligence (IEEE WCCI) / International Joint Conference on neural Networks (IJCNN) / IEEE Congress on Evolutionary Computation (IEEE CEC)

作者： Chen, Kai Ma, Yongqiang Sheng, Mingyang Zheng, Nanning Xi An Jiao Tong Univ Coll Artificial Intelligence Xian 710049 Peoples R China

ISBN: (数字)9781728186719

ISBN: (纸本)9781728186719

The reconstruction of visual stimulus images from functional Magnetic Resonance Imaging (fMRI) has received extensive attention in recent years, which provides a possibility to interpret the human brain. Due to the high-dimensional and high-noise characteristics of fMRI data, how to extract stable, reliable and useful information from fMRI data for image reconstruction has become a challenging problem. Inspired by the mechanism of human visual attention, in this paper, we propose a novel method of reconstructing visual stimulus images, which first decodes human visual salient region from fMRI, we define human visual salient region as foreground attention (F-attention), and then reconstructs the visual images guided by F-attention. Because the human brain is strongly wound into sulci and gyri, some spatially adjacent voxels are not connected in practice. Therefore, it is necessary to consider the global information when decoding fMRI, so we introduce the self-attention module for capturing global information into the process of decoding F-attention. In addition, in order to obtain more loss constraints in the training process of encoder-decoder, we also propose a new training strategy called Loop-Enc-Dec. The experimental results show that the F-attention decoder decodes the visual attention from fMRI successfully, and the Loop-Enc-Dec guided by F-attention can also well reconstruct the visual stimulus images.

关键词： visual reconstruction Foreground-attention fMRI neural decoding

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Real-Time Calcium Imaging Based neural decoding with a Support Vector Machine

Real-Time Calcium Imaging Based Neural Decoding with a Suppo...

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IEEE Biomedical Circuits and Systems Conference (BioCAS)

作者： Lee, Kyunghun Lee, Yaesop Lin, Da-Ting Bhattacharyya, Shuvra S. Chen, Rong Univ Maryland Sch Med Baltimore MD 21201 USA Univ Maryland Dept Elect & Comp Engn College Pk MD 20742 USA Univ Maryland Inst Adv Comp Studies College Pk MD 20742 USA NIDA NIH Baltimore MD USA

ISBN: (纸本)9781509006175

We present a novel real-time neural decoding system for calcium imaging data. Miniature calcium imaging is of great utility for examining neural activity in populations of animals. Our real-time neural decoding system is developed using a carefully-designed support vector machine (SVM) subsystem together with dataflow-based techniques for system design, which capture the high-level structure of the application and enable powerful system-level analysis and optimization. Through extensive experiments, we have evaluated the proposed system using calcium imaging datasets in which neural activities of D1 medium spiny neurons (MSNs) in the dorsal striatum were recorded. The results show that the performance of the proposed system is significantly better than that of previously developed neural decoding systems for calcium imaging.

关键词： neural decoding dataflow real-time signal processing support vector machine

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Multi-finger Motion Inference Using M1 neural decoding 32

Multi-finger Motion Inference Using M1 Neural Decoding

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32nd International Conference on Information Networking (ICOIN)

作者： Choi, Hwayoung You, Kyung-Jin Shin, Hyun-chool Soongsil Univ Dept Elect Engn Seoul South Korea

ISBN: (纸本)9781538622902

This paper proposed a novel decoding method, which is the decoding technique for inferring multi-finger motions trained by the neural activities acquired in single finger motions. We have exploited the relationship between single and multi-finger motions. Then the model for multi-finger motions was built. The proposed method decoded multi-finger motions with 100% accuracy without a priori training information of multi-finger. These results suggest that only with the neural activities on single finger motions we can infer multi-finger motions.

关键词： neural decoding motion decoding semi-blind decoding finger digit

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Deep Learning for Real-Time neural decoding of Grasp

Deep Learning for Real-Time Neural Decoding of Grasp

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European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD)

作者： Viviani, Paolo Gesmundo, Ilaria Ghinato, Elios Agudelo-Toro, Andres Vercellino, Chiara Vitali, Giacomo Bergamasco, Letizia Scionti, Alberto Ghislieri, Marco Agostini, Valentina Terzo, Olivier Scherberger, Hansjoerg LINKS Fdn Turin Italy Polytech Torino Turin Italy Deutsch Primatenzentrum GmbH DPZ Gottingen Germany

ISBN: (纸本)9783031434266;9783031434273

neural decoding involves correlating signals acquired from the brain to variables in the physical world like limb movement or robot control in Brain Machine Interfaces. In this context, this work starts from a specific pre-existing dataset of neural recordings from monkey motor cortex and presents a Deep Learning-based approach to the decoding of neural signals for grasp type classification. Specifically, we propose here an approach that exploits LSTM networks to classify time series containing neural data (i.e., spike trains) into classes representing the object being grasped. The main goal of the presented approach is to improve over state-of-the-art decoding accuracy without relying on any prior neuroscience knowledge, and leveraging only the capability of deep learning models to extract correlations from data. The paper presents the results achieved for the considered dataset and compares them with previous works on the same dataset, showing a significant improvement in classification accuracy, even if considering simulated real-time decoding.

关键词： Machine Learning neural decoding neural networks brain machine interface

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Benchmarking of hardware-efficient real-time neural decoding in brain-computer interfaces

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NEUROMORPHIC COMPUTING AND ENGINEERING 2024年第2期4卷 024008-024008页

作者： Hueber, Paul Tang, Guangzhi Sifalakis, Manolis Liaw, Hua-Peng Micheli, Aurora Tomen, Nergis Liu, Yao-Hong Imec Netherlands Eindhoven Netherlands Delft Univ Technol Delft Netherlands

Designing processors for implantable closed-loop neuromodulation systems presents a formidable challenge owing to the constrained operational environment, which requires low latency and high energy efficacy. Previous benchmarks have provided limited insights into power consumption and latency. However, this study introduces algorithmic metrics that capture the potential and limitations of neural decoders for closed-loop intra-cortical brain-computer interfaces in the context of energy and hardware constraints. This study benchmarks common decoding methods for predicting a primate's finger kinematics from the motor cortex and explores their suitability for low latency and high energy efficient neural decoding. The study found that ANN-based decoders provide superior decoding accuracy, requiring high latency and many operations to effectively decode neural signals. Spiking neural networks (SNNs) have emerged as a solution, bridging this gap by achieving competitive decoding performance within sub-10 ms while utilizing a fraction of computational resources. These distinctive advantages of neuromorphic SNNs make them highly suitable for the challenging closed-loop neural modulation environment. Their capacity to balance decoding accuracy and operational efficiency offers immense potential in reshaping the landscape of neural decoders, fostering greater understanding, and opening new frontiers in closed-loop intra-cortical human-machine interaction.

关键词： neural decoding brain computer interfaces closed-loop neuromodulation spiking neural networks neuromorphic computing

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neural decoding reveals specialized kinematic tuning after an abrupt cortical transition

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CELL REPORTS 2023年第9期42卷 113119页

作者： Glanz, Ryan M. Sokoloff, Greta Blumberg, Mark S. Univ Iowa Dept Psychol & Brain Sci Iowa City IA 52242 USA Univ Iowa Iowa Neurosci Inst Iowa City IA 52242 USA

The primary motor cortex (M1) exhibits a protracted period of development, including the development of a sensory representation long before motor outflow emerges. In rats, this representation is present by post-natal day (P) 8, when M1 activity is "discontinuous."Here, we ask how the representation changes upon the transition to "continuous"activity at P12. We use neural decoding to predict forelimb movements from M1 activity and show that a linear decoder effectively predicts limb movements at P8 but not at P12;instead, a nonlinear decoder better predicts limb movements at P12. The altered decoder performance reflects increased complexity and uniqueness of kinematic information in M1. We next show that M1's representation at P12 is more susceptible to "lesioning"of inputs and "transplanting"of M1's encoding scheme from one pup to another. Thus, the emergence of continuous M1 activity signals the developmental onset of more complex, informationally sparse, and individualized sensory representations.

关键词： development rat REM sleep twitching motor cortex neural decoding sensorimotor sparse coding LSTM mutual information Research topic(s)CP: Neuroscience

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Robust neural decoding by kernel regression with Siamese representation learning

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JOURNAL OF neural ENGINEERING 2021年第5期18卷

作者： Li, Yangang Qi, Yu Wang, Yiwen Wang, Yueming Xu, Kedi Pan, Gang Zhejiang Univ Qiushi Acad Adv Studies Hangzhou Peoples R China Zhejiang Univ Coll Comp Sci & Technol Hangzhou Peoples R China Zhejiang Univ MOE Frontier Sci Ctr Brain Sci & Brain Machine In Hangzhou Peoples R China Hong Kong Univ Sci & Technol Dept Chem & Biol Engn Hong Kong Peoples R China Hong Kong Univ Sci & Technol Dept Elect & Comp Engn Hong Kong Peoples R China Zhejiang Univ Minist Educ Key Lab Biomed Engn Hangzhou Peoples R China Zhejiang Univ Zhejiang Prov Key Lab Cardiocerebral Vasc Detect Hangzhou Peoples R China

Objective. Brain-machine interfaces (BMIs) provide a direct pathway between the brain and external devices such as computer cursors and prosthetics, which have great potential in motor function restoration. One critical limitation of current BMI systems is the unstable performance, partly due to the variability of neural signals. Studies showed that neural activities exhibit trial-to-trial variability, and the preferred direction of neurons frequently changes under different conditions. Therefore, a fixed decoding function does not work well. Approach. To deal with the problems, we propose a novel kernel regression framework. The nonparametric kernel regression is used to fit diverse decoding functions by finding similar neural patterns to handle neural variations caused by varying tuning functions. Further, the representations of raw neural signals are learned by Siamese networks and constrained by kinematic parameters, which can alleviate neural variations caused by intrinsic noises and task-irrelevant information. The representations are jointly learned with the kernel regression framework in an end-to-end manner so that neural variations can be tackled effectively. Main results. Experiments on two datasets demonstrate that our approach outperforms most existing methods and significantly improves the robustness in challenging situations such as limited samples and missing channels. Significance. The proposed approach demonstrates robust performance with different conditions and provides a new and inspiring perspective toward robust BMI control.

关键词： brain-machine interfaces neural decoding kernel regression representation learning Siamese networks

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