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Vocal Emotion Perception and Musicality-Insights from EEG decoding

SENSORS 2025年第6期25卷 1669-1669页

作者： Lehnen, Johannes M. Schweinberger, Stefan R. Nussbaum, Christine Friedrich Schiller Univ Jena Dept Clin Psychol Childhood & Adolescence D-07743 Jena Germany Friedrich Schiller Univ Jena Dept Gen Psychol & Cognit Neurosci D-07743 Jena Germany Friedrich Schiller Univ Jena Voice Res Unit D-07743 Jena Germany Univ Geneva Swiss Ctr Affect Sci CH-1205 Geneva Switzerland

Musicians have an advantage in recognizing vocal emotions compared to non-musicians, a performance advantage often attributed to enhanced early auditory sensitivity to pitch. Yet a previous ERP study only detected group differences from 500 ms onward, suggesting that conventional ERP analyses might not be sensitive enough to detect early neural effects. To address this, we re-analyzed EEG data from 38 musicians and 39 non-musicians engaged in a vocal emotion perception task. Stimuli were generated using parameter-specific voice morphing to preserve emotional cues in either the pitch contour (F0) or timbre. By employing a neural decoding framework with a Linear Discriminant Analysis classifier, we tracked the evolution of emotion representations over time in the EEG signal. Converging with the previous ERP study, our findings reveal that musicians-but not non-musicians-exhibited significant emotion decoding between 500 and 900 ms after stimulus onset, a pattern observed for F0-Morphs only. These results suggest that musicians' superior vocal emotion recognition arises from more effective integration of pitch information during later processing stages rather than from enhanced early sensory encoding. Our study also demonstrates the potential of neural decoding approaches using EEG brain activity as a biological sensor for unraveling the temporal dynamics of voice perception.

关键词： vocal emotion perception musicality fundamental frequency (F0) timbre EEG neural decoding

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Less is more: selection from a small set of options improves BCI velocity control

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JOURNAL OF neural ENGINEERING 2025年第2期22卷 026018-026018页

作者： Alcolea, Pedro, I Ma, Xuan Bodkin, Kevin Miller, Lee E. Danziger, Zachary C. Florida Int Univ Dept Biomed Engn Miami FL 33199 USA Northwestern Univ Feinberg Sch Med Dept Neurosci Chicago IL 60611 USA Northwestern Univ Feinberg Sch Med Dept Phys Med & Rehabil Chicago IL 60611 USA Northwestern Univ McCormick Sch Engn Dept Biomed Engn Evanston IL 60208 USA Shirley Ryan AbilityLab Chicago IL 60611 USA Emory Univ Dept Rehabil Med Div Phys Therapy Atlanta GA 30322 USA Emory Univ Atlanta WH Coulter Dept Biomed Engn Atlanta GA 30322 USA

Objective. decoding algorithms used in invasive brain-computer interfaces (iBCIs) typically convert neural activity into continuously varying velocity commands. We hypothesized that putting constraints on which decoded velocity commands are permissible could improve user performance. To test this hypothesis, we designed the discrete direction selection (DDS) decoder, which uses neural activity to select among a small menu of preset cursor velocities. Approach. We tested DDS in a closed-loop cursor control task against many common continuous velocity decoders in both a human-operated real-time iBCI simulator (the jaBCI) and in a monkey using an iBCI. In the jaBCI, we compared performance across four visits by each of 48 na & iuml;ve, able-bodied human subjects using either DDS, direct regression with assist (an affine map from neural activity to cursor velocity, DR-A), ReFIT, or the velocity Kalman Filter (vKF). In a follow up study to verify the jaBCI results, we compared a monkey's performance using an iBCI with either DDS or the Wiener filter decoder (a direct regression decoder that includes time history, WF). Main Result. In the jaBCI, DDS substantially outperformed all other decoders with 93% mean targets hit per visit compared to DR-A, ReFIT, and vKF with 56%, 39%, and 26% mean targets hit, respectively. With the iBCI, the monkey achieved a 61% success rate with DDS and a 37% success rate with WF. Significance. Discretizing the decoded velocity with DDS effectively traded high resolution velocity commands for less tortuous and lower noise trajectories, highlighting the potential benefits of discretization in simplifying online BCI control.

关键词： neural decoding cursor control center-out iBCI model (jaBCI) discrete velocity control monkey iBCI motor cortex

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decoding Cognitive Processes from neural Ensembles

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TRENDS IN COGNITIVE SCIENCES 2018年第12期22卷 1091-1102页

作者： Wallis, Joni D. Univ Calif Berkeley Dept Psychol 3210 Tolman Hall Berkeley CA 94720 USA Univ Calif Berkeley Helen Wills Neurosci Inst Berkeley CA 94720 USA

An intrinsic difficulty in studying cognitive processes is that they are unobservable states that exist in between observable responses to the sensory environment. Cognitive states must be inferred from indirect behavioral measures. Neuroscience potentially provides the tools necessary to measure cognitive processes directly, but it is challenged on two fronts. First, neuroscientific measures often lack the spatiotemporal resolution to identify the neural computations that underlie a cognitive process. Second, the activity of a single neuron, which is the fundamental building block of neural computation, is too noisy to provide accurate measurements of a cognitive process. In this paper, I examine recent developments in neurophysiological recording and analysis methods that provide a potential solution to these problems.

关键词： decision making high-level cognition neural decoding neurophysiology orbitofrontal cortex

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A motor unit action potential-based method for surface electromyography decomposition

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JOURNAL OF NEUROENGINEERING AND REHABILITATION 2025年第1期22卷 1-16页

作者： Chen, Chen Li, Dongxuan Xia, Miaojuan Shanghai Jiao Tong Univ Sch Mech Engn State Key Lab Mech Syst & Vibrat Shanghai 200240 Peoples R China

ObjectiveSurface electromyography (EMG) decomposition is crucial for identifying motor neuron activities by analyzing muscle-generated electrical signals. This study aims to develop and validate a novel motor unit action potential (MUAP)-based method for surface EMG decomposition, addressing the limitations of traditional blind source separation (BSS)-based techniques in computation complexity and motor unit (MU) *** the framework of the convolution kernel compensation algorithm, we developed a MUAP-based decomposition algorithm by reconstructing the MU filters from MUAPs and evaluated its performance using both simulated and experimental datasets. A systematic analysis was conducted on various factors affecting decomposition performance, including MU filter reconstruction methods, EMG covariance matrices, MUAP extraction techniques, and extending factors. The proposed method was subsequently compared to representative BSS-based techniques, such as convolution kernel *** resultsThe MUAP-based method significantly outperformed traditional BSS-based techniques in identifying more MUs and achieving better accuracy, particularly under noisy conditions. It demonstrated superior performance with increased signal complexity and effectively tracked motor units consistently across decompositions. In addition, directly applying the MU filters reconstructed from MUAPs to decomposition exhibited marked computational *** and significanceThe MUAP-based method enhances EMG decomposition accuracy, robustness, and efficiency, offering reliable motor unit tracking and real-time processing capabilities. These advancements highlight its potential for clinical diagnostics and neurorehabilitation, representing a promising step forward in non-invasive motor neuron analysis.

关键词： Electromyography decomposition Motor unit action potential neural decoding Blind source separation Motor unit tracking

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Non-Invasive Brain-Computer Interfaces: State of the Art and Trends

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IEEE REVIEWS IN BIOMEDICAL ENGINEERING 2025年 18卷 26-49页

作者： Edelman, Bradley J. Zhang, Shuailei Schalk, Gerwin Brunner, Peter Mueller-Putz, Gernot Guan, Cuntai He, Bin Max Planck Inst Psychiat D-80804 Munich Germany Max Planck Inst Biol Intelligence D-82152 Planegg Germany Nanyang Technol Univ Singapore 639798 Singapore Fudan Univ Shanghai 200433 Peoples R China Washington Univ St Louis MO 63130 USA Graz Univ Technol A-8010 Graz Austria Carnegie Mellon Univ Pittsburgh PA 15217 USA

Brain-computer interface (BCI) is a rapidly evolving technology that has the potential to widely influence research, clinical and recreational use. Non-invasive BCI approaches are particularly common as they can impact a large number of participants safely and at a relatively low cost. Where traditional non-invasive BCIs were used for simple computer cursor tasks, it is now increasingly common for these systems to control robotic devices for complex tasks that may be useful in daily life. In this review, we provide an overview of the general BCI framework as well as the various methods that can be used to record neural activity, extract signals of interest, and decode brain states. In this context, we summarize the current state-of-the-art of non-invasive BCI research, focusing on trends in both the application of BCIs for controlling external devices and algorithm development to optimize their use. We also discuss various open-source BCI toolboxes and software, and describe their impact on the field at large.

关键词： Recording Electrodes decoding Electroencephalography Task analysis Motors Robots BCI brain-computer interface deep learning electroencephalography manifold classification motor imagery motor-related cortical potentials neural decoding neurotechnology robotic arm transfer learning

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An optogenetics- and imaging-assisted simultaneous multiple patch-clamp recording system for decoding complex neural circuits

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NATURE PROTOCOLS 2015年第3期10卷 397-412页

作者： Wang, Guangfu Wyskiel, Daniel R. Yang, Weiguo Wang, Yiqing Milbern, Lana C. Lalanne, Txomin Jiang, Xiaolong Shen, Ying Sun, Qian-Quan Zhu, J. Julius Univ Virginia Dept Pharmacol Charlottesville VA 22908 USA Univ Wyoming Dept Zool & Physiol Laramie WY 82071 USA Univ Virginia Dept Chem Charlottesville VA USA Bordeaux Univ Dept Hlth & Life Sci Neurosci Grad Program Bordeaux France Zhejiang Univ Sch Med Dept Neurobiol Hangzhou 310003 Zhejiang Peoples R China Zhejiang Univ Sch Med Key Lab Med Neurobiol Chinese Minist Hlth Hangzhou 310003 Zhejiang Peoples R China Univ Virginia Dept Neurosci Charlottesville VA 22908 USA

Deciphering neuronal circuitry is central to understanding brain function and dysfunction, yet it remains a daunting task. To facilitate the dissection of neuronal circuits, a process requiring functional analysis of synaptic connections and morphological identification of interconnected neurons, we present here a method for stable simultaneous octuple patch-clamp recordings. This method allows physiological analysis of synaptic interconnections among 4-8 simultaneously recorded neurons and/or 10-30 sequentially recorded neurons, and it allows anatomical identification of >85% of recorded interneurons and >99% of recorded principal neurons. We describe how to apply the method to rodent tissue slices;however, it can be used on other model organisms. We also describe the latest refinements and optimizations of mechanics, electronics, optics and software programs that are central to the realization of a combined single- and two-photon microscopy-based, optogenetics- and imaging-assisted, stable, simultaneous quadruple-viguple patch-clamp recording system. Setting up the system, from the beginning of instrument assembly and software installation to full operation, can be completed in 3-4 d.

关键词： neural decoding Neuronal physiology Patch clamp Synaptic transmission

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Speech Reception Threshold Estimation via EEG-Based Continuous Speech Envelope Reconstruction

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EUROPEAN JOURNAL OF NEUROSCIENCE 2025年第6期61卷 e70083页

作者： Borges, Heidi B. Zaar, Johannes Alickovic, Emina Christensen, Christian B. Kidmose, Preben Eriksholm Res Ctr Snekkersten Denmark Aarhus Univ Dept Elect & Comp Engn Aarhus Denmark Tech Univ Denmark Dept Hlth Technol Hearing Syst Lyngby Denmark Linkoping Univ Dept Elect Engn Linkoping Sweden

This study investigates the potential of speech reception threshold (SRT) estimation through electroencephalography (EEG) based envelope reconstruction techniques with continuous speech. Additionally, we investigate the influence of the stimuli's signal-to-noise ratio (SNR) on the temporal response function (TRF). Twenty young normal-hearing participants listened to audiobook excerpts with varying background noise levels while EEG was recorded. A linear decoder was trained to reconstruct the speech envelope from the EEG data. The reconstruction accuracy was calculated as the Pearson's correlation between the reconstructed and actual speech envelopes. An EEG SRT estimate (SRTneuro) was obtained as the midpoint of a sigmoid function fitted to the reconstruction accuracy versus SNR data points. Additionally, the TRF was estimated at each SNR level, followed by a statistical analysis to reveal significant effects of SNR levels on the latencies and amplitudes of the most prominent components. The SRTneuro was within 3 dB of the behavioral SRT for all participants. The TRF analysis showed a significant latency decrease for N1 and P2 and a significant amplitude magnitude increase for N1 and P2 with increasing SNR. The results suggest that both envelope reconstruction accuracy and the TRF components are influenced by changes in SNR, indicating they may be linked to the same underlying neural process.

关键词： EEG measures envelope tracking neural decoding speech in noise speech intelligibility

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Bayesian encoding and decoding as distinct perspectives on neural coding

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NATURE NEUROSCIENCE 2023年第12期26卷 2063-2072页

作者： Lange, Richard D. Shivkumar, Sabyasachi Chattoraj, Ankani Haefner, Ralf M. Univ Penn Dept Neurobiol Philadelphia PA 19104 USA Univ Rochester Dept Brain & Cognit Sci Rochester NY USA Rochester Inst Technol Dept Comp Sci Rochester NY 14623 USA Columbia Univ Zuckerman Mind Brain Behav Inst New York NY USA

The Bayesian brain hypothesis is one of the most influential ideas in neuroscience. However, unstated differences in how Bayesian ideas are operationalized make it difficult to draw general conclusions about how Bayesian computations map onto neural circuits. Here, we identify one such unstated difference: some theories ask how neural circuits could recover information about the world from sensory neural activity (Bayesian decoding), whereas others ask how neural circuits could implement inference in an internal model (Bayesian encoding). These two approaches require profoundly different assumptions and lead to different interpretations of empirical data. We contrast them in terms of motivations, empirical support and relationship to neural data. We also use a simple model to argue that encoding and decoding models are complementary rather than competing. Appreciating the distinction between Bayesian encoding and Bayesian decoding will help to organize future work and enable stronger empirical tests about the nature of inference in the brain. This paper characterizes two distinct philosophies underlying previous work on how Bayesian computations are linked to neural data, highlighting how different theories may be motivated by different tacit assumptions and thereby explain different data.

关键词： neural decoding neural encoding Sensory processing

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Simultaneous neural Spike Encoding and decoding Based on Cross-modal Dual Deep Generative Model

Simultaneous Neural Spike Encoding and Decoding Based on Cro...

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International Joint Conference on neural Networks (IJCNN) held as part of the IEEE World Congress on Computational Intelligence (IEEE WCCI)

作者： Zhou, Qiongyi Du, Changde Li, Dan Wang, Haibao Liu, Jian K. He, Huiguang Chinese Acad Sci Inst Automat Res Ctr Brain Inspired Intelligence Beijing 100190 Peoples R China Chinese Acad Sci Inst Automat Natl Lab Pattern Recognit 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 Chinese Acad Sci Ctr Excellence Brain Sci & Intelligence Technol Shanghai 200031 Peoples R China Univ Leicester Dept Neurosci Psychol & Behav Ctr Syst Neurosci Leicester LE1 7RH Leics England

ISBN: (纸本)9781728169262

neural encoding and decoding of retinal ganglion cells (RGCs) have been attached great importance in the research work of brain-machine interfaces. Much effort has been invested to mimic RGC and get insight into RGC signals to reconstruct stimuli. However, there remain two challenges. On the one hand, complex nonlinear processes in retinal neural circuits hinder encoding models from enhancing their ability to fit the natural stimuli and modelling RGCs accurately. On the other hand, current research of the decoding process is separate from that of the encoding process, in which the liaison of mutual promotion between them is neglected. In order to alleviate the above problems, we propose a cross-modal dual deep generative model (CDDG) in this paper. CDDG treats the RGC spike signals and the stimuli as two modalities, which learns a shared latent representation for the concatenated modality and two modal-specific latent representations. Then, it imposes distribution consistency restriction on different latent space, cross-consistency and cycle-consistency constraints on the generated variables. Thus, our model ensures cross-modal generation from RGC spike signals to stimuli and vice versa. In our framework, the generation from stimuli to RGC spike signals is equivalent to neural encoding while the inverse process is equivalent to neural decoding. Hence, the proposed method integrates neural encoding and decoding and exploits the reciprocity between them. The experimental results demonstrate that our proposed method can achieve excellent encoding and decoding performance compared with the state-of-the-art methods on three salamander RGC spike datasets with natural stimuli.

关键词： dual learning cross-modal generation retinal ganglion cells neural encoding neural decoding

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Graph Emotion decoding from Visually Evoked neural Responses 25th

Graph Emotion Decoding from Visually Evoked Neural Responses

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25th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI)

作者： Huang, Zhongyu Du, Changde Wang, Yingheng He, Huiguang Chinese Acad Sci Inst Automat Natl Lab Pattern Recognit Res Ctr Brain Inspired Intelligence Beijing Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing Peoples R China Tsinghua Univ Dept Elect Engn Beijing Peoples R China Johns Hopkins Univ Dept Biomed Engn Baltimore MD USA Chinese Acad Sci Ctr Excellence Brain Sci & Intelligence Technol Beijing Peoples R China

ISBN: (纸本)9783031164521;9783031164514

Brain signal-based affective computing has recently drawn considerable attention due to its potential widespread applications. Most existing efforts exploit emotion similarities or brain region similarities to learn emotion representations. However, the relationships between emotions and brain regions are not explicitly incorporated into the representation learning process. Consequently, the learned representations may not be informative enough to benefit downstream tasks, e.g., emotion decoding. In this work, we propose a novel neural decoding framework, Graph Emotion decoding (GED), which integrates the relationships between emotions and brain regions via a bipartite graph structure into the neural decoding process. Further analysis shows that exploiting such relationships helps learn better representations, verifying the rationality and effectiveness of GED. Comprehensive experiments on visually evoked emotion datasets demonstrate the superiority of our model. The code is publicly available at https://***1998/GED.

关键词： neural decoding Graph neural networks Brain Emotion

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