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Classification of Electronic Devices and Software Processes via Unintentional Electronic Emissions With neural decoding Algorithms

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IEEE TRANSACTIONS ON ELECTROMAGNETIC COMPATIBILITY 2020年第2期62卷 470-477页

作者： Mariano, Laura J. Aubuchon, Alexander Lau, Troy Ozdemir, Onur Lazovich, Tomo Coakley, John Charles Stark Draper Lab Inc Cambridge MA 02139 USA ClimaCell Boston MA 02111 USA Northeastern Univ Coll Comp & Informat Sci Boston MA 02115 USA Lightmatter Boston MA 02115 USA

Electronic and electromechanical devices continuously emit electromagnetic (EM) signals while in use. These EM emissions (EMEs) contain unique spectral characteristics that can be leveraged for a variety of purposes, including identification of the device's unique EM "fingerprint," and characterization of software processes running on the device. In this study, we implemented a novel method for automatic identification and characterization of these EMEs inspired by a classification/decoding scheme used to extract neural correlates of brain state from magnetoencephalographic data. Utilizing a sparse bilinear formulation of logistic regression as our "neural" decoder, we extracted device- and software-specific spectrospatial patterns from five identical Arduino Uno prototyping boards as they cycled through five program states. In the device fingerprinting task, we were able to discriminate all five boards from each other with near-perfect accuracy using this method. For software characterization, within a single device, we detected all five programs with 90% accuracy, and across devices, we were able to identify 3/5 programs with 99% accuracy, and achieving 77% accuracy for the other two. Overall, this neural-decoding approach performed well in all scenarios tested, and the corresponding EME "maps" it provides quantify the differences between device and software-specific EMEs in an easily interpretable way.

关键词： Software Probes decoding Data collection Logistics Hardware Arrays Electromagnetic (EM) emissions logistic regression neural decoding pattern analysis

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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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On-FPGA Spiking neural Networks for End-to-End neural decoding

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IEEE ACCESS 2023年 11卷 41387-41399页

作者： Leone, Gianluca Raffo, Luigi Meloni, Paolo Univ Cagliari Dipartimento Ingn Elettr & Elettron I-09123 Cagliari Italy

In the last decades, deep learning neural decoding algorithms have gained momentum in the field of neural interfaces and neural processing systems. However, to be deployed on low-budget portable devices while maintaining real-time operability, these models must withstand strict computational and power limitations. This work presents a spike decoding system implemented on a low-end Zynq-7010 FPGA, which includes a multiplier-less spike detection pipeline and a spiking-neural-network-based decoder mapped in the programmable logic. We tested the system on two publicly available datasets and achieved comparable results with state-of-the-art neural decoders that use more complex deep learning models. The system required 7.36 times fewer parameters than the smallest architecture tested on the same dataset. Moreover, by exploiting the spike sparsity property of the neural signal, the total amount of computations is reduced by about 90% during a test carried out on real recorded data. The low computational complexity of the chosen spike detection setup, combined with the power efficiency of spiking neural networks, makes this prototype a well-suited choice for low-power real-time neural decoding at the edge.

关键词： decoding Task analysis Neurons Field programmable gate arrays Real-time systems Biological system modeling Hardware neural decoding spike detection spiking neural network FPGA low-power

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Graph-Enhanced Emotion neural decoding

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IEEE TRANSACTIONS ON MEDICAL IMAGING 2023年第8期42卷 2262-2273页

作者： Huang, Zhongyu Du, Changde Wang, Yingheng Fu, Kaicheng He, Huiguang Chinese Acad Sci Inst Automat Lab Brain Atlas & Brain Inspired Intelligence State Key Lab Multimodal Artificial Intelligence S Beijing 100190 Peoples R China Cornell Univ Dept Comp Sci Ithaca NY 14850 USA

Brain signal-based emotion recognition has recently attracted considerable attention since it has powerful potential to be applied in human-computer interaction. To realize the emotional interaction of intelligent systems with humans, researchers have made efforts to decode human emotions from brain imaging data. The majority of current efforts use emotion similarities (e.g., emotion graphs) or brain region similarities (e.g., brain networks) to learn emotion and brain representations. However, the relationships between emotions and brain regions are not explicitly incorporated into the representation learning process. As a result, the learned representations may not be informative enough to benefit specific tasks, e.g., emotion decoding. In this work, we propose a novel idea of graph-enhanced emotion neural decoding, which takes advantage of a bipartite graph structure to integrate the relationships between emotions and brain regions into the neural decoding process, thus helping learn better representations. Theoretical analyses conclude that the suggested emotion-brain bipartite graph inherits and generalizes the conventional emotion graphs and brain networks. Comprehensive experiments on visually evoked emotion datasets demonstrate the effectiveness and superiority of our approach.

关键词： Brain region emotion graph neural networks neural decoding representation

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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 force levels and gestures integrated multi-task strategy for neural decoding

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COMPLEX & INTELLIGENT SYSTEMS 2020年第3期6卷 469-478页

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

This paper discusses the problem of decoding gestures represented by surface electromyography (sEMG) signals in the presence of variable force levels. It is an attempt that multi-task learning (MTL) is proposed to recognize gestures and force levels synchronously. First, methods of gesture recognition with different force levels are investigated. Then, MTL framework is presented to improve the gesture recognition performance and give information about force levels. Last but not least, to solve the problem that using the greedy principle in MTL, a modified pseudo-task augmentation (PTA) trajectory is introduced. Experiments conducted on two representative datasets demonstrate that compared with other methods, frequency domain information with convolutional neural network (CNN) is more suitable for gesture recognition with variable force levels. Besides, the feasibility of extracting features that are closely related to both gestures and force levels is verified via MTL. By influencing learning dynamics, the proposed PTA method can improve the results of all tasks, and make it applicable to the case where the main tasks and auxiliary tasks are clear.

关键词： neural decoding Multi-task learning (MTL) Pseudo-task augmentation (PTA) Convolutional neural network (CNN)

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An application of formal concept analysis to semantic neural decoding

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ANNALS OF MATHEMATICS AND ARTIFICIAL INTELLIGENCE 2009年第3-4期57卷 233-248页

作者： Endres, Dominik Maria Foeldiak, Peter Priss, Uta Univ Clin Tubingen Sect Theoret Sensomotor Dept Cognit Neurol Hertie Inst Clin Brain Res Tubingen Germany Univ Clin Tubingen Ctr Integrat Neurosci Tubingen Germany Univ St Andrews Sch Psychol St Andrews KY16 9AJ Fife Scotland Edinburgh Napier Univ Sch Comp Edinburgh Midlothian Scotland

This paper proposes a novel application of Formal Concept Analysis (FCA) to neural decoding: the semantic relationships between the neural representations of large sets of stimuli are explored using concept lattices. In particular, the effects of neural code sparsity are modelled using the lattices. An exact Bayesian approach is employed to construct the formal context needed by FCA. This method is explained using an example of neurophysiological data from the high-level visual cortical area STSa. Prominent features of the resulting concept lattices are discussed, including indications for hierarchical face representation and a product-of-experts code in real neurons. The robustness of these features is illustrated by studying the effects of scaling the attributes.

关键词： Formal concept analysis FCA neural code Sparse coding High-level vision STS Bayesian classification Semantic neural decoding

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Revealing nonlinear neural decoding by analyzing choices

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NATURE COMMUNICATIONS 2021年第1期12卷 1-13页

作者： Yang, Qianli Walker, Edgar Cotton, R. James Tolias, Andreas S. Pitkow, Xaq Rice Univ Dept Elect & Comp Engn POB 1892 Houston TX 77251 USA Baylor Coll Med Dept Neurosci Houston TX 77030 USA Baylor Coll Med Ctr Neurosci & Artificial Intelligence Houston TX 77030 USA Shirley Ryan Abil Lab Chicago IL USA Northwestern Univ Dept Phys Med & Rehabil Evanston IL USA Changzhou Univ Aliyun Sch Big Data Changzhou Peoples R China Chinese Acad Sci CAS Ctr Excellence Brain Sci & Intelligence Techn Inst Neurosci Key Lab Primate Neurobiol Beijing Peoples R China

Sensory data about most natural task-relevant variables are entangled with task-irrelevant nuisance variables. The neurons that encode these relevant signals typically constitute a nonlinear population code. Here we present a theoretical framework for quantifying how the brain uses or decodes its nonlinear information. Our theory obeys fundamental mathematical limitations on information content inherited from the sensory periphery, describing redundant codes when there are many more cortical neurons than primary sensory neurons. The theory predicts that if the brain uses its nonlinear population codes optimally, then more informative patterns should be more correlated with choices. More specifically, the theory predicts a simple, easily computed quantitative relationship between fluctuating neural activity and behavioral choices that reveals the decoding efficiency. This relationship holds for optimal feedforward networks of modest complexity, when experiments are performed under natural nuisance variation. We analyze recordings from primary visual cortex of monkeys discriminating the distribution from which oriented stimuli were drawn, and find these data are consistent with the hypothesis of near-optimal nonlinear decoding. Sensory data about most natural task-relevant variables are entangled with task-irrelevant nuisance variables. Here, the authors present a theoretical framework for quantifying how the brain uses or decodes its nonlinear information which indicates near-optimal nonlinear decoding.

关键词： neural decoding Sensory processing

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EEG-based neural decoding of Gait in Developing Children

EEG-based Neural Decoding of Gait in Developing Children

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IEEE International Conference on Systems, Man and Cybernetics (SMC)

作者： Luu, Trieu Phat Eguren, David Cestari, Manuel Contreras-Vidal, Jose L. Univ Houston NSF IUCRC BRAIN Ctr Houston TX 77204 USA

ISBN: (纸本)9781728145693

neural decoding of human locomotion, including automated gait intention detection and continuous decoding of lower limb joint angles, has been of great interest in the field of Brain Machine Interface (BMI). However, neural decoding of gait in developing children has yet to be demonstrated. In this study, we collected physiological data (electroencephalography (EEG), electromyography (EMG)), and kinematic data from children performing different locomotion tasks. We also developed a state space estimation model to decode lower limb joint angles from scalp EEG. Fluctuations in the amplitude of slow cortical potentials of EEG in the delta band (0.1 - 3 Hz) were used for prediction. The decoding accuracies (Pearson's r values) were promising (Hip: 0.71;Knee: 0.59;Ankle: 0 51). Our results demonstrate the feasibility of neural decoding of children walking and have implications for the development of a real-time closed-loop BMI system for the control of a pediatric exoskeleton.

关键词： Brain-computer interface neural decoding children walking EEG EMG gait

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AIoT-based neural decoding and Neurofeedback for Accelerated Cognitive Training: Vision, Directions and Preliminary Results 22

AIoT-based Neural Decoding and Neurofeedback for Accelerated...

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22nd IEEE Statistical Signal Processing Workshop (SSP)

作者： Van-Tam Nguyen Tartaglione, Enzo Tuan Dinh Inst Polytech Paris Telecom Paris LTCI Paris France TechTree Ventures Berkeley CA USA

ISBN: (纸本)9781665452458

Attention and working memory, which are two fundamental components of cognitive basis, can be improved through cognitive training. In addition, thanks to neuroplasticity, neurons are able to adapt quickly to the demands placed on them. By developing new neural networks and strengthening important connections, a cognitive training program can measurably and permanently improve brain activity. In this paper, we present a concept of AIoT based neural decoding and neurofeedback to accelerate cognitive training, the preliminary results and research directions. The proposed concept is to design adequate tiny machine learning to extract the relevant features and characteristics from physiological signals. A tiny ML performs classification or recognition of relevant patterns, based on which the neurofeedback system is appropriately designed for more effective cognitive training.

关键词： AIoT Tiny ML Embedded AI neural decoding Neurofeedback Cognitive Training Attention Memorization EQ

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