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检索条件"主题词=neural decoding"

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A mobile embedded platform for high performance neural signal computation and communication 11

A mobile embedded platform for high performance neural signa...

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11th IEEE Annual Biomedical Circuits and Systems Conference (BioCAS)

作者： Heelan, Christopher Komar, Jacob Vargas-Irwin, Carlos E. Simeral, John D. Nurmikko, Arto V. Brown Univ Sch Engn Providence RI 02912 USA Brown Univ Dept Neurosci Providence RI 02912 USA Brown Univ Inst Brain Sci Providence RI 02912 USA Dept VA Med Ctr Ctr Neurorestorat & Neurotechnol Rehab R&D Serv Providence RI USA

ISBN: (纸本)9781479972340

We have designed and implemented a new compact, high performance neural signal processing system for wearable neurotechnology platforms. The low-power embedded system (referred to hereafter as ESPA) prototype wirelessly receives, records, and processes 200 channels of broadband neural data, demonstrated here in a non-human primate. The subject is implanted with intracortical micro-electrode arrays (MEAs) with a head-mounted wireless neural signal transmitting device. The fully integrated embedded system manages and processes neural data using state-of-the art all-programmable system on a Chip (SoC) technology. The utilized SoC provides substantial digital signal processing hardware in the form of a field programmable gate array (FPGA) subsystem. This programmable logic (PL) subsystem connects through highbandwidth busses to the SoC's mobile processor system (PS) which provides SoC management and communication capabilities through the use of a real-time operating system. Next, in an application demonstration, a rhesus macaque performed a dexterous manual grasp task. Wirelessly received broadband neural data was communicated by the prototype embedded system to a backend server for real-time file storage and off-line spike feature extraction and hand-grip classification. Successful grip classification was demonstrated by achieving comparable classification accuracy to a conventional rackmounted commercial neural signal *** performance of the system was first benchtop tested using 200 channels of 30ksps simulated neural data which underwent neural signal pre-processing, filtering, and feature extraction implemented on the PL subsystem. The raw and processed data were streamed to the PS where it underwent further processing before being communicated wirelessly to a backend server via Wi-Fi. The SoC only required an estimated 2.01W of power during this test.

关键词： neural prosthetics wireless embedded system mobile multichannel FPGA Xilinx real-time QNX multielectrode neural decoding paralysis ALS medical device

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Motor Cortical Adaptation Induced by Closed-Loop BCI 7

Motor Cortical Adaptation Induced by Closed-Loop BCI

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7th Annual International IEEE EMBS Conference on neural Engineering (NER)

作者： Xu, Zhiming Guan, Cuntai So, Rosa Q. Ang, Kai Keng Toe, Kyaw Kyar ASTAR Singapore Dept Neural & Biomed Technol Inst Infocomm Res Singapore 138632 Singapore

ISBN: (纸本)9781467363891

Brain-computer interface (BCI) could help disabled patients with a broken neural pathway from brain to limbs restore movements by directly exploiting brain signals. Current laboratory BCIs on nonhuman primates (NHPs) were usually started from open-loop hand control (HC) setup for calibration and training, and then progressed to closed-loop brain control (BC) setup without using natural limbs. Successful transition from HC to BC necessitated motor leaning and neural plasticity which might involve the cortical adaptation induced by learning BCI. One useful strategy is to design neural feedback procedure to assist such adaptation and learning. We present an intracortical BCI on NHP with our designed feedback training procedure. In particular, we showed the motor cortical adaptation in terms of single neuron spiking activity in vivo during the closed-loop motor learning induced by our designed feedback training procedure. This experimental work can complement the existing theoretical modeling works on such closed-loop learning process.

关键词： Brain computer interface maximal a posterior motor cortex motor learning neural decoding

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Thin-film, high-density micro-electrocorticographic decoding of a human cortical gyrus 38

Thin-film, high-density micro-electrocorticographic decoding...

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38th Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society (EMBC)

作者： Muller, Leah Felix, Sarah Shah, Kedar G. Lee, Kye Pannu, Satinderpall Chang, Edward F. Univ Calif San Francisco 675 Nelson Rising LaneRoom 511 San Francisco CA 94158 USA Lawrence Livermore Natl Lab 7000 East Ave Livermore CA 94550 USA

ISBN: (纸本)9781457702204

High-density electrocorticography (ECoG) arrays are promising interfaces for high-resolution neural recording from the cortical surface. Commercial options for high-density arrays are limited, and historically tradeoffs must be made between spatial coverage and electrode density. However, thin-film technology is a promising alternative for generating electrode arrays capable of large area coverage and high channel count, with resolution on the order of cortical columns in the functional surface unit of a human gyrus. Here, we evaluate the sensing performance of a high-density thin-film 128-electrode array designed specifically for recording the distributed neural activity of a single human cortical gyrus. We found robust field potential responses throughout the superior temporal gyrus evoked by speech sounds, and clear phonetic feature selectivity at the resolution of 2 mm inter-electrode distance. decoding accuracy improved with increasing density of electrodes over all three patients tested. Thin-film ECoG has significant potential for high-density neural interface applications at the scale of a human gyrus.

关键词： electrocorticography micro-ECoG brain machine interface neural device design spatial resolution neural decoding thin film high density

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A Bayesian-Optimized Convolutional neural Network to Decode Reach-to-Grasp from Macaque Dorsomedial Visual Stream 8th

A Bayesian-Optimized Convolutional Neural Network to Decode ...

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8th Annual International Conference on Machine Learning, Optimization and Data science (LOD)

作者： Borra, Davide Filippini, Matteo Ursino, Mauro Fattori, Patrizia Magosso, Elisa Univ Bologna Dept Elect Elect & Informat Engn Guglielmo Marcon Cesena Campus Cesena Italy Univ Bologna Dept Biomed & Neuromotor Sci DIBINEM Bologna Italy Univ Bologna Alma Mater Res Inst Human Ctr Artificial Intellig Bologna Italy

ISBN: (纸本)9783031258909;9783031258916

neural decoding is crucial to translate the neural activity for Brain-Computer Interfaces (BCIs) and provides information on how external variables (e.g., movement) are represented and encoded in the neural system. Convolutional neural networks (CNNs) are emerging as neural decoders for their high predictive power and are largely applied with electroencephalographic signals;these algorithms, by automatically learning the more relevant class-discriminative features, improve decoding performance over classic decoders based on handcrafted features. However, applications of CNNs for single-neuron decoding are still scarce and require further validation. In this study, a CNN architecture was designed via Bayesian optimization and was applied to decode different grip types from the activity of single neurons of the posterior parietal cortex of macaque (area V6A). The Bayesian-optimized CNN significantly outperformed a naive Bayes classifier, commonly used for neural decoding, and proved to be robust to a reduction of the number of cells and of training trials. Adopting a sliding window decoding approach with a high time resolution (5 ms), the CNN was able to capture grip-discriminant features early after cuing the animal, i.e., when the animal was only attending the object to grasp, further supporting that grip-related neural signatures are strongly encoded in V6A already duringmovement preparation. The proposed approach may have practical implications in invasive BCIs to realize accurate and robust decoders, and may be used together with explanation techniques to design a general tool for neural decoding and analysis, boosting our comprehension of neural encoding.

关键词： neural decoding Convolutional neural networks Dorsomedial visual stream V6A Bayesian optimization

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Repairing Brain-Computer Interfaces with Fault-Based Data Acquisition 22

Repairing Brain-Computer Interfaces with Fault-Based Data Ac...

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ACM/IEEE 44th International Conference on Software Engineering (ICSE)

作者： Winston, Cailin Winston, Caleb Winston, Chloe N. Winston, Claris Winston, Cleah Rao, Rajesh P. N. Just, Rene Univ Washington Seattle WA 98195 USA

ISBN: (纸本)9781450392211

Brain-computer interfaces (BCIs) decode recorded neural signals from the brain and/or stimulate the brain with encoded neural signals. BCIs span both hardware and software and have a wide range of applications in restorative medicine, from restoring movement through prostheses and robotic limbs to restoring sensation and communication through spellers. BCIs also have applications in diagnostic medicine, e.g., providing clinicians with data for detecting seizures, sleep patterns, or emotions. Despite their promise, BCIs have not yet been adopted for longterm, day-to-day use because of challenges related to reliability and robustness, which are needed for safe operation in all scenarios. Ensuring safe operation currently requires hours of manual data collection and recalibration, involving both patients and clinicians. However, data collection is not targeted at eliminating specific faults in a BCI. This paper presents a new methodology for characterizing, detecting, and localizing faults in BCIs. Specifically, it proposes partial test oracles as a method for detecting faults and slice functions as a method for localizing faults to characteristic patterns in the input data or relevant tasks performed by the user. Through targeted data acquisition and retraining, the proposed methodology improves the correctness of BCIs. We evaluated the proposed methodology on five BCI applications. The results show that the proposed methodology (1) precisely localizes faults and (2) can significantly reduce the frequency of faults through retraining based on targeted, fault-based data acquisition. These results suggest that the proposed methodology is a promising step towards repairing faulty BCIs.

关键词： Brain-computer interface neural decoding partial test oracles fault localization

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A Generalized Preprocessing and Feature Extraction Platform for Scalp EEG Signals on FPGA 3

A Generalized Preprocessing and Feature Extraction Platform ...

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IEEE INTERNATIONAL CONFERENCE ON BIOMEDICAL ENGINEERING AND SCIENCES

作者： Wijesinghe, L. P. Wickramasuriya, D. S. Pasqual, Ajith A. Univ Moratuwa Dept Elect & Telecommun Engn Moratuwa 10400 Sri Lanka

ISBN: (纸本)9781479940844

Brain-computer interfaces (BCIs) require real-time feature extraction for translating input EEG signals recorded from a subject into an output command or decision. Owing to the inherent difficulties in EEG signal processing and neural decoding, many of the feature extraction algorithms are complex and computationally demanding. Presently, software does exist to perform real-time feature extraction and classification of EEG signals. However, the requirement of a personal computer is a major obstacle in bringing these technologies to the home and mobile user affording ease of use. We present the FPGA design and novel architecture of a generalized platform that provides a set of predefined features and preprocessing steps that can be configured by a user for BCI applications. The preprocessing steps include power line noise cancellation and baseline removal while the feature set includes a combination of linear and nonlinear, univariate and bivariate measures commonly utilized in BCIs. We provide a comparison of our results with software and also validate the platform by implementing a seizure detection algorithm on a standard dataset and obtained a classification accuracy of over 96%. A gradual transition of BCI systems to hardware would prove beneficial in terms of compactness, power consumption and much faster response to stimuli.

关键词： brain-computer interfaces electroencephalography feature extraction field programmable gate arrays medical disorders medical signal detection neurophysiology signal classification signal denoising BCI application BCI systems BCIs EEG signal classification EEG signal processing FPGA design baseline removal bivariate measures classification accuracy decision feature extraction algorithm feature extraction platform feature set generalized platform generalized preprocessing home input EEG signals mobile user neural decoding nonlinear measures output command personal computer power line noise cancellation preprocessing steps real-time feature extraction scalp EEG signals seizure detection algorithm standard dataset univariate measures Computer architecture Discrete wavelet transforms Electroencephalography Feature extraction Field programmable gate arrays Finite impulse response filters Histograms

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Leveraging Recurrent neural Networks for Predicting Motor Movements from Primate Motor Cortex neural Recordings

Leveraging Recurrent Neural Networks for Predicting Motor Mo...

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2024 Biomedical Circuits and Systems Conference

作者： Wang, Yuanxi Wang, Zuowen Liu, Shih-Chii Univ Zurich Inst Neuroinformat Zurich Switzerland Swiss Fed Inst Technol Zurich Switzerland

ISBN: (纸本)9798350354966;9798350354959

This paper presents an efficient deep learning solution for decoding motor movements from neural recordings in non-human primates. An Autoencoder Gated Recurrent Unit (AEGRU) model was adopted as the model architecture for this task. The autoencoder is only used during the training stage to achieve better generalization. Together with the preprocessing techniques, our model achieved 0.71 R-2 score, surpassing the baseline models in Neurobench and is ranked first for R-2 in the IEEE BioCAS 2024 Grand Challenge on neural decoding. Model pruning is also applied leading to a reduction of 41.4% of the multiply-accumulate (MAC) operations with little change in the R-2 score compared to the unpruned model.

关键词： neural decoding neural motor decoder network autoencoder gated recurrent unit

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Bayesian Point Process Models With Applications in High-Dimensional Neuroscience Studies

Bayesian Point Process Models With Applications in High-Dime...

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作者： Haghverdian, Derenik University of California Irvine

学位级别：Ph.D., Doctor of Philosophy

The hippocampus plays a crucial role in organizing the memory of daily events, yet unraveling its mechanisms poses challenges. decoding the information encoded in the hippocampus is particularly challenging due to sparse neuron activity in non-spatial tasks. However, accurate decoding is crucial for understanding how the hippocampus represents and processes information. This work focuses on decoding neural activity using multivariate point processes, specifically Poisson and Hawkes processes. The first two chapters concentrate on Hawkes processes, which are well-suited for modeling history-dependent phenomena characterized by clustered events, such as the spiking activity in the brain. The study introduces several self-exciting/inhibiting Hawkes process models that effectively capture the interactions among an ensemble of neurons in the hippocampus of rats. The study demonstrates that this approach enables more accurate decoding with significantly lower computational complexity. In the final chapter, we present two novel models for encoding and decoding neural activity using non-homogeneous Poisson processes. The encoding model captures neuronal dynamics in response to stimuli, revealing temporal variations in neuronal activity over time. On the other hand, the decoding model focuses on decoding the underlying stimulus patterns from the spike train data, leveraging the estimated firing rates from the encoding model. These models demonstrate improved decoding accuracy, emphasizing the importance of incorporating temporal dynamics in decoding stimulus patterns from neural activity.

关键词： Bayesian statistics Hawkes processes neural decoding Point processes Sparse estimation

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Sample Reweighting for Label Denoising of neural Activity Data 11

Sample Reweighting for Label Denoising of Neural Activity Da...

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11th International IEEE EMBS Conference on neural Engineering (IEEE/EMBS NER)

作者： Xu, Dongfang Chen, Rong Univ Maryland Sch Med Dept Diagnost Radiol & Nucl Med Baltimore MD 21201 USA

ISBN: (纸本)9781665462921

neural decoding is a powerful technique to explore the relationship between neural activities and behaviors. It often needs massive accurately labeled data to train a model for behavior prediction. However, it is not easy to obtain accurate annotations for massive data, and the label noise is sometimes inevitable and needs to be denoised first. For annotation correction, we propose a sample reweighting method to denoise noisy labels. This method utilizes a small clean validation dataset to assign weights to the training data with label noise. A deep neural network model can be trained based on the weighted training data and the validation data. Based on the neural network model, new labels can be predicted for training data to realize the label denoising. The label denoising experiment is conducted on a functional magnetic resonance imaging dataset with class imbalance. The results show that the sample reweighting method can effectively denoise labels under different annotation qualities or noise levels for each class and it outperforms the baseline methods (validation only and semi-supervised learning). The sample reweighting method can also effectively handle the class imbalance problem. The proposed method is an effective way to tackle the noisy label problem in neural decoding.

关键词： neural decoding Label denoising Sample reweighting Meta-learning

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A NEUROPHYSIOLOGICAL-AUDITORY "LISTEN RECEIPT" FOR COMMUNICATION ENHANCEMENT 49

A NEUROPHYSIOLOGICAL-AUDITORY "LISTEN RECEIPT" FOR COMMUNICA...

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49th IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Beauchene, Christine Brandstein, Michael Quatieri, Thomas F. Thompson, Eric Smalt, Christopher J. MIT Lincoln Lab 244 Wood St Lexington MA 02421 USA Air Force Res Lab Dayton OH USA

ISBN: (纸本)9798350344868;9798350344851

Information overload, and specifically auditory overload, is common in critical situations and detrimental to communication. Currently, there is no auditory equivalent of an email read receipt to know if a person has heard a message, other than waiting for a reply. This work hypothesizes that it may be possible to decode whether a person has indeed heard a message, or in other words, create an an auditory "listen receipt," through use of non-invasive physiological or neural monitoring. We extracted a variety of features derived from Electrodermal activity (EDA), Electroencephalography (EEG), and the correlations between the acoustic envelope of the radio message and EEG to use in the decoder. We were able to classify the cases in which the subject responded correctly to the question in the message, versus the cases where they missed or heard the message incorrectly, with an accuracy of 79% and a receiver operating characteristic (ROC) area under the curve (AUC) of 0.83. This work suggests that the concept of a "listen receipt" may be possible, and future wearable machine-brain interface technologies may be able to automatically determine if an important radio message has been missed for both human-to-human and human-to-machine communication.

关键词： Auditory attention decoding EEG neural decoding speech communication

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