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An fMRI-based auditory decoding framework combined with convolutional neural network for predicting the semantics of real-life sounds from brain activity

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APPLIED INTELLIGENCE 2025年第2期55卷 1-12页

作者： Zhao, Mingqian Liu, Baolin Univ Sci & Technol Beijing Sch Comp & Commun Engn Beijing 100083 Peoples R China

Semantic decoding, understood as predicting the semantic information carried by stimuli presented to subjects based on neural signals, is an active area of research. Previous studies have mainly focused on the visual perception process, with relatively little attention paid to complex auditory decoding. Moreover, simple linear models do not achieve optimal performance for the mapping between brain signals and natural sounds. Therefore, a robust approach that combines a pretrained audio tagging model and a nonlinear multilayer perceptron model was proposed to transfer information from non-invasive measured brain activity to deep learning features, thereby generating sound semantics. The results achieved on previously unseen subjects, training without data from the target subjects, and ultimately predicting natural-sound semantics from the fMRI data of unseen subjects. In the study with 30 subjects, the framework in research achieves 23.21% Top-1 and 51.88% Top-5 accuracy scores, which significantly exceed the baseline scores and the scores of other classical algorithms. The approach advances the decoding of auditory neural excitation with the help of deep neural networks, and the proposed model successfully completes a challenging cross-subject decoding task.

关键词： neural decoding Deep semantic representation Real-life sounds Brain activities FMRI

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Low-Power FPGA-Based Spiking neural Networks for Real-Time decoding of Intracortical neural Activity

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IEEE SENSORS JOURNAL 2024年第24期24卷 42448-42459页

作者： Martis, Luca Leone, Gianluca Raffo, Luigi Meloni, Paolo Univ Cagliari Dept Elect & Elect Engn I-09123 Cagliari Italy

Brain-machine interfaces (BMIs) are systems designed to decode neural signals and translate them into commands for external devices. Intracortical microelectrode arrays (MEAs) represent a significant advancement in this field, offering unprecedented spatial and temporal resolutions for monitoring brain activity. However, processing data from MEAs presents challenges due to high data rates and computing power requirements. To address these challenges, we propose a novel solution leveraging spiking neural networks (SNNs) that, due to their similarity to biological neural networks and their event-based nature, promise high compatibility with neural signals and low energy consumption. In this study, we introduce a real-time neural decoding system based on an SNN, deployed on a Lattice iCE40UP5k FPGA. This system is capable of reconstructing multiple target variables, related to the kinematics and kinetics of hand motion, from iEEG signals recorded by a 96-channel MEA. We evaluated the system using two different public datasets, achieving results similar to state-of-the-art neural decoders that use more complex deep learning models. This was obtained while maintaining an average power consumption of 13.9 mW and an average energy consumption per inference of 13.9 uJ.

关键词： decoding Field programmable gate arrays Accuracy Spiking neural networks Real-time systems Signal processing algorithms Microelectrodes Hardware Biological neural networks Training FPGA low power neural decoding real time spike detection spiking neural network (SNN)

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Recording and decoding for neural Prostheses

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PROCEEDINGS OF THE IEEE 2016年第2期104卷 374-391页

作者： Warren, David J. Kellis, Spencer Nieveen, Jacob G. Wendelken, Suzanne M. Dantas, Henrique Davis, Tyler S. Hutchinson, Douglas T. Normann, Richard A. Clark, Gregory A. Mathews, V. John Univ Utah Dept Bioengn Salt Lake City UT 84112 USA CALTECH Biol & Biol Engn Div Pasadena CA 91125 USA Univ Utah Dept Elect & Comp Engn Salt Lake City UT 84112 USA Oregon State Univ Sch Elect Engn & Comp Sci Corvallis OR 97331 USA Univ Utah Dept Neurosurg Salt Lake City UT 84132 USA Univ Utah Dept Orthopaed Surg Salt Lake City UT 84108 USA

This paper reviews technologies and signal processing algorithms for decoding peripheral nerve and electrocorticogram signals to interpret human intent and control prosthetic arms. The review includes a discussion of human motor system physiology and physiological signals that can be used to decode motor intent, electrode technology for acquiring neural data, and signal processing methods including decoders based on Kalman filtering and least-squares regressors. Representative results from human experiments demonstrate the progress that has been made in neural decoding and its potential for developing neuroprosthetic arms that act and feel like natural arms.

关键词： Brain-computer interface electrocorticogram Kalman decoders neural decoding neuroprosthetics peripheral nerve signals

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Bayesian decoding of neural spike trains

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ANNALS OF THE INSTITUTE OF STATISTICAL MATHEMATICS 2010年第1期62卷 37-59页

作者： Koyama, Shinsuke Eden, Uri T. Brown, Emery N. Kass, Robert E. Carnegie Mellon Univ Dept Stat Ctr Neural Basis Cognit Pittsburgh PA 15213 USA Boston Univ Dept Math & Stat Boston MA 02215 USA Massachusetts Gen Hosp Dept Anesthesia & Crit Care Neurosci Stat Res Lab Boston MA 02114 USA Harvard Univ MIT Sch Med Div Hlth Sci & Technol Cambridge MA 02139 USA Carnegie Mellon Univ Ctr Neural Basis Cognit Dept Machine Learning Pittsburgh PA 15213 USA

Perception, memory, learning, and decision making are processes carried out in the brain. The performance of such intelligent tasks is made possible by the communication of neurons through sequences of voltage pulses called spike trains. It is of great interest to have methods of extracting information from spike trains in order to learn about their relationship to behavior. In this article, we review a Bayesian approach to this problem based on state-space representations of point processes. We discuss some of the theory and we describe the way these methods are used in decoding motor cortical activity, in which the hand motion is reconstructed from neural spike trains.

关键词： Point process State-space model Recursive Bayesian filter Sequential Gaussian approximation neural decoding

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Efficient decoding With Steady-State Kalman Filter in neural Interface Systems

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IEEE TRANSACTIONS ON neural SYSTEMS AND REHABILITATION ENGINEERING 2011年第1期19卷 25-34页

作者： Malik, Wasim Q. Truccolo, Wilson Brown, Emery N. Hochberg, Leigh R. Harvard Univ Massachusetts Gen Hosp Sch Med Dept Anesthesia Crit Care & Pain Med Boston MA 02114 USA Brown Univ Sch Engn Providence RI 02912 USA Brown Univ Inst Brain Res Providence RI 02912 USA MIT Dept Brain & Cognit Sci Cambridge MA 02139 USA Rehabil R&D Serv Ctr Restorat & Regenerat Med US Dept Vet Affairs Providence RI 02908 USA Brown Univ Dept Neurosci Providence RI 02912 USA Harvard Univ Massachusetts Gen Hosp Sch Med Dept Neurol Boston MA 02114 USA MIT Harvard Mit Div Hlth Sci & Technol Cambridge MA 02139 USA

The Kalman filter is commonly used in neural interface systems to decode neural activity and estimate the desired movement kinematics. We analyze a low-complexity Kalman filter implementation in which the filter gain is approximated by its steady-state form, computed offline before real-time decoding commences. We evaluate its performance using human motor cortical spike train data obtained from an intracortical recording array as part of an ongoing pilot clinical trial. We demonstrate that the standard Kalman filter gain converges to within 95% of the steady-state filter gain in 1.5 +/- 0.5 s (mean +/- s.d.). The difference in the intended movement velocity decoded by the two filters vanishes within 5 s, with a correlation coefficient of 0.99 between the two decoded velocities over the session length. We also find that the steady-state Kalman filter reduces the computational load (algorithm execution time) for decoding the firing rates of 25 +/- 3 single units by a factor of 7.0 +/- 0.9. We expect that the gain in computational efficiency will be much higher in systems with larger neural ensembles. The steady-state filter can thus provide substantial runtime efficiency at little cost in terms of estimation accuracy. This far more efficient neural decoding approach will facilitate the practical implementation of future large-dimensional, multisignal neural interface systems.

关键词： Brain-computer interfaces (BCI) brain-machine interfaces (BMI) Kalman filter motor cortex neural decoding paralysis spinal cord injury state-space models tetraplegia

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decoding sensorimotor information from superior parietal lobule of macaque via Convolutional neural Networks

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neural NETWORKS 2022年第0期151卷 276-294页

作者： Filippini, Matteo Borra, Davide Ursino, Mauro Magosso, Elisa Fattori, Patrizia Univ Bologna Dept Biomed & Neuromotor Sci Piazza Porta San Donato 2 I-40126 Bologna Italy Univ Bologna Dept Elect Elect & Informat Engn Guglielmo Marcon Cesena Campus Cesena Italy Alma Mater Res Inst Human Ctr Artificial Intellig Bologna Italy

Despite the well-recognized role of the posterior parietal cortex (PPC) in processing sensory information to guide action, the differential encoding properties of this dynamic processing, as operated by different PPC brain areas, are scarcely known. Within the monkey's PPC, the superior parietal lobule hosts areas V6A, PEc, and PE included in the dorso-medial visual stream that is specialized in planning and guiding reaching movements. Here, a Convolutional neural Network (CNN) approach is used to investigate how the information is processed in these areas. We trained two macaque monkeys to perform a delayed reaching task towards 9 positions (distributed on 3 different depth and direction levels) in the 3D peripersonal space. The activity of single cells was recorded from V6A, PEc, PE and fed to convolutional neural networks that were designed and trained to exploit the temporal structure of neuronal activation patterns, to decode the target positions reached by the monkey. Bayesian Optimization was used to define the main CNN hyper-parameters. In addition to discrete positions in space, we used the same network architecture to decode plausible reaching trajectories. We found that data from the most caudal V6A and PEc areas outperformed PE area in the spatial position decoding. In all areas, decoding accuracies started to increase at the time the target to reach was instructed to the monkey, and reached a plateau at movement onset. The results support a dynamic encoding of the different phases and properties of the reaching movement differentially distributed over a network of interconnected areas. This study highlights the usefulness of neurons' firing rate decoding via CNNs to improve our understanding of how sensorimotor information is encoded in PPC to perform reaching movements. The obtained results may have implications in the perspective of novel neuroprosthetic devices based on the decoding of these rich signals for faithfully carrying out patient's intentio

关键词： neural decoding Posterior parietal cortex Convolutional neural network Sensorymotor transformation Brain-computer interfaces Monkey

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decoding neural Representational Spaces Using Multivariate Pattern Analysis

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ANNUAL REVIEW OF NEUROSCIENCE, VOL 37 2014年第1期37卷 435-456页

作者： Haxby, James V. Connolly, Andrew C. Guntupalli, J. Swaroop Dartmouth Coll Ctr Cognit Neurosci Dept Psychol & Brain Sci Hanover NH 03755 USA Univ Trent Ctr Mind Brain Sci CIMeC I-38068 Rovereto Trentino Italy

A major challenge for systems neuroscience is to break the neural code. Computational algorithms for encoding information into neural activity and extracting information from measured activity afford understanding of how percepts, memories, thought, and knowledge are represented in patterns of brain activity. The past decade and a half has seen significant advances in the development of methods for decoding human neural activity, such as multivariate pattern classification, representational similarity analysis, hyperalignment, and stimulus-model-based encoding and decoding. This article reviews these advances and integrates neural decoding methods into a common framework organized around the concept of high-dimensional representational spaces.

关键词： neural decoding MVPA RSA hyperalignment population response fMRI

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Effective Phoneme decoding With Hyperbolic neural Networks for High-Performance Speech BCIs

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IEEE TRANSACTIONS ON neural SYSTEMS AND REHABILITATION ENGINEERING 2024年 32卷 3432-3441页

作者： Tan, Xianhan Lian, Qi Zhu, Junming Zhang, Jianmin Wang, Yueming Qi, Yu Zhejiang Univ Coll Comp Sci Hangzhou 310027 Peoples R China Nanhu Brain Comp Interface Inst Hangzhou 311100 Peoples R China Zhejiang Univ Affiliated Hosp 2 Sch Med Hangzhou 310058 Peoples R China Zhejiang Univ Qiushi Acad Adv Studies Hangzhou 310027 Peoples R China Zhejiang Univ MOE Frontier Sci Ctr Brain Sci & Brain Machine Int Affiliated Mental Hlth Ctr Hangzhou 310027 Peoples R China Zhejiang Univ Hangzhou Peoples Hosp 7 Hangzhou 310027 Peoples R China Zhejiang Univ State Key Lab Brain Machine Intelligence Hangzhou 310027 Peoples R China

Objective: Speech brain-computer interfaces (speech BCIs), which convert brain signals into spoken words or sentences, have demonstrated great potential for high-performance BCI communication. Phonemes are the basic pronunciation units. For monosyllabic languages such as Chinese Mandarin, where a word usually contains less than three phonemes, accurate decoding of phonemes plays a vital role. We found that in the neural representation space, phonemes with similar pronunciations are often inseparable, leading to confusion in phoneme classification. Methods: We mapped the neural signals of phoneme pronunciation into a hyperbolic space for a more distinct phoneme representation. Critically, we proposed a hyperbolic hierarchical clustering approach to specifically learn a phoneme-level structure to guide the representation. Results: We found such representation facilitated greater distance between similar phonemes, effectively reducing confusion. In the phoneme decoding task, our approach demonstrated an average accuracy of 75.21% for 21 phonemes and outperformed existing methods across different experimental days. Conclusion: Our approach showed high accuracy in phoneme classification. By learning the phoneme-level neural structure, the representations of neural signals were more discriminative and interpretable. Significance: Our approach can potentially facilitate high-performance speech BCIs for Chinese and other monosyllabic languages.

关键词： decoding Accuracy Error analysis Brain-computer interfaces Vectors neural activity Kinematics Brain-computer interface speech BCIs neural decoding hyperbolic network hyperbolic clustering

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Unsupervised, piecewise linear decoding enables an accurate prediction of muscle activity in a multi-task brain computer interface

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JOURNAL OF neural ENGINEERING 2025年第1期22卷 2025 Jan 17页

作者： Ma, Xuan Rizzoglio, Fabio Bodkin, Kevin L. Miller, Lee E. Northwestern Univ Dept Neurosci Chicago IL 60208 USA Northwestern Univ Dept Neurobiol Evanston IL USA Northwestern Univ Dept Biomed Engn Evanston IL USA Shirley Ryan AbilityLab Chicago IL USA Northwestern Univ Dept Phys Med & Rehabil Chicago IL USA

Objective. Creating an intracortical brain computer interface (iBCI) capable of seamless transitions between tasks and contexts would greatly enhance user experience. However, the nonlinearity in neural activity presents challenges to computing a global iBCI decoder. We aimed to develop a method that differs from a globally optimized decoder to address this issue. Approach. We devised an unsupervised approach that relies on the structure of a low-dimensional neural manifold to implement a piecewise linear decoder. We created a distinctive dataset in which monkeys performed a diverse set of tasks, some trained, others innate, while we recorded neural signals from the motor cortex (M1) and electromyographs (EMGs) from upper limb muscles. We used both linear and nonlinear dimensionality reduction techniques to discover neural manifolds and applied unsupervised algorithms to identify clusters within those spaces. Finally, we fit a linear decoder of EMG for each cluster. A specific decoder was activated corresponding to the cluster each new neural data point belonged to. Main results. We found clusters in the neural manifolds corresponding with the different tasks or task sub-phases. The performance of piecewise decoding improved as the number of clusters increased and plateaued gradually. With only two clusters it already outperformed a global linear decoder, and unexpectedly, it outperformed even a global recurrent neural network decoder with 10-12 clusters. Significance. This study introduced a computationally lightweight solution for creating iBCI decoders that can function effectively across a broad range of tasks. EMG decoding is particularly challenging, as muscle activity is used, under varying contexts, to control interaction forces and limb stiffness, as well as motion. The results suggest that a piecewise linear decoder can provide a good approximation to the nonlinearity between neural activity and motor outputs, a result of our increased understanding of the

关键词： intracortical BCI multi-task neural decoding EMG piecewise linear

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Advanced EEG Signal Processing and Deep Q-Learning for Accurate Student Attention Monitoring

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IEEE ACCESS 2025年 13卷 47260-47270页

作者： Rehman, Asad Ur Ul Eman, Noor Ullah, Farhan Cacciagrano, Diletta Mostarda, Leonardo Raza, Nabeel Wuhan Univ Sch Cyber Sci & Engn Wuhan 430072 Hubei Peoples R China Univ Agr Faisalabad Dept Comp Sci Faisalabad 03802 Punjab Pakistan Prince Mohammad Bin Fahd Univ Cybersecur Ctr Dhahran 34754 Saudi Arabia Univ Camerino Div Comp Sci I-62032 Camerino Italy Univ Perugia Dept Math & Comp Sci I-06123 Perugia Italy Univ Sci & Technol China Sci Isl Branch Grad Sch Hefei 230026 Anhui Peoples R China

Measuring student attention during learning remains a challenging task. Traditional methods, such as surveys and observations, are subjective and often inaccurate. While EEG can detect attentional changes, its interpretation is complicated by noise and artifacts. This study introduces an enhanced approach that leverages advanced data preprocessing techniques and a Double Deep Q-Network (DDQN) model, a specialized deep reinforcement learning (DRL) algorithm adept at handling complex, multidimensional EEG data. We employ wavelet transformations to generate robust frequency-based representations of raw multi-channel EEG signals, followed by Butterworth bandpass and notch filters to eliminate noise and artifacts. Dimensionality reduction and feature scaling are achieved employing Principal Component Analysis (PCA) alongside Independent Component Analysis (ICA), leading in cleaner and more representative EEG data. Our method significantly improves the accuracy of attention state classification, achieving a test accuracy of 98.4%. This advancement sets a new standard for utilizing EEG data to monitor attention in educational settings, showcasing the effectiveness of our preprocessing techniques in enhancing the DDQN's neural decoding capabilities.

关键词： Electroencephalography Accuracy Brain modeling Electronic mail Support vector machines Real-time systems Principal component analysis Noise Deep reinforcement learning Data preprocessing Student attention EEG signal processing double deep q-network (DDQN) deep reinforcement learning (DRL) attention state classification neural decoding

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