Understanding the neural basis of speech communication is essential for uncovering how sounds are translated into meaning, how that changes with development, ageing, and speech-related deficits, as well as contributing to brain-computer interfaces research. While traditional neurophysiological studies have relied on simplified, controlled paradigms, recent advances have shifted the field toward more ecologically-valid approaches. Here, we examine the impact of continuous speech research and discuss the potential of speech interaction neurophysiology. We present a discussion on how realistic paradigms challenge conventional methods, offering richer insights into neural encoding, functional brain mapping, and neural entrainment. At the same time, they introduce significant analytical and technical complexities, particularly when incorporating social interaction. We discuss the evolving landscape of experimental designs, from discrete to continuous stimuli and from socially-isolated listening to dynamic, multi-agent communication. By synthesising findings across studies, we highlight how naturalistic speech paradigms contribute to refining theories of language processing and open new
Across many domains, large swaths of digital assets are being stored across distributed data repositories, e.g., the DANDI Archive [8]. The distribution and diversity of these repositories impede researchers from formally defining terminology within experiments, integrating information across datasets, and easily querying, reusing, and analyzing data that follow the FAIR principles [15]. As such, it has become increasingly important to have a standardized method to attach contextual metadata to datasets. Neuroscience is an exemplary use case of this issue due to the complex multimodal nature of experiments. Here, we present the HDMF External Resources Data (HERD) standard and related tools, enabling researchers to annotate new and existing datasets by mapping external references to the data without requiring modification of the original dataset. We integrated HERD closely with Neurodata Without Borders (NWB) [2], a widely used data standard for sharing and storing neurophysiology data. By integrating with NWB, our tools provide neuroscientists with the capability to more easily create and manage neurophysiology data in compliance with controlled sets of terms, enhancing rigor and a
Current theories suggest that adaptive decision-making necessitates the interaction between multiple decision-making systems. The computational definitions of different models of decision-making suggest interactions with task demands and complexity. We review these computational theories and derive experimental predictions that will shed light on the underlying neurobiological mechanisms. We use a well-established multi-strategy task and novel neurophysiological analyses from hippocampus and striatum as a case study in the interaction between task structure and navigational complexity. This approach reveals how task structure and navigational complexity interact with each other to identify differences between habitual and planned action choices.
The appearance of an object triggers a shift of gaze toward its location. This orienting response consists of a rapid rotation of the eyes, the saccade, sometimes accompanied by a head rotation. In this chapter, instead of describing the path leading from the target-evoked retinal activity to the changes in muscle tension, we shall take the reverse path. Starting from the muscle contractions, we shall proceed upstream and describe the core neuronal networks in the brainstem and cerebellum that enable us to rapidly and accurately orient the foveae towards visual targets located at different eccentricities and depths.
Sleep paralysis is an experience of being temporarily unable to move or talk during the transitional periods between sleep and wakefulness: at sleep onset or upon awakening. Feeling of paralysis may be accompanied by a variety of vivid and intense sensory experiences, including mentation in visual, auditory, and tactile modalities, as well as a distinct feeling of presence. This chapter discusses a variety of sleep paralysis experiences from the perspective of enactive cognition and cultural neurophenomenology. Current knowledge of neurophysiology and associated conditions is presented, and some techniques for coping with sleep paralysis are proposed. As an experience characterized by a hybrid state of dreaming and waking, sleep paralysis offers a unique window into phenomenology of spontaneous thought in sleep.
Currently virtual reality (VR) usage in training processes is increasing due to their usefulness in the learning processes based on visual information empowered. The information in virtual environments is perceived by sight, sound and touch, but the relationship or impact that these stimuli can have on the oscillatory activity of the brain such as the processing, propagation and synchronization of information still needs to be established in relation to the cognitive load of attention. Therefore, this study seeks to identify the suggested epistemological basis through literature review and current research agendas in the relationship that exists between the immersive virtual environment and the neurophysiology of learning processes by means of the analysis of visual information. The suggested dimensional modeling of this research is composed by the theory of information processing which allows the incorporation of learning through stimuli with the use of attention, perception and storage by means of information management and the Kolb's learning model which defines the perception and processing of information as dimensions of learning. Regarding to the neurophysiology of learning,
Making all data for any observation or experiment openly available is a defining feature of empirical science (e.g., nullius in verba, the motto of the Royal Society). It enhances transparency, reproducibility, and societal trust. While embraced in spirit by many, in practice open data sharing remains the exception in contemporary systems neuroscience. Here, we take stock of the Allen Brain Observatory, an effort to share data and metadata associated with surveys of neuronal activity in the visual system of laboratory mice. The data from these surveys have been used to produce new discoveries, to validate computational algorithms, and as a benchmark for comparison with other data, resulting in over 100 publications and preprints to date. We distill some of the lessons learned about open surveys and data reuse, including remaining barriers to data sharing and what might be done to address these.
We propose an extension of the cable equation by introducing a Caputo time fractional derivative. The fundamental solutions of the most common boundary problems are derived analitically via Laplace Transform, and result be written in terms of known special functions. This generalization could be useful to describe anomalous diffusion phenomena with leakage as signal conduction in spiny dendrites. The presented solutions are computed in Matlab and plotted.
Eye movements, including saccades, are widely regarded as highly sensitive and objective biomarkers of neurophysiologic states. Detecting saccadic signatures in neurologic diseases offers a rapid, portable alternative to brain imaging, avoiding access and cost barriers. Currently, there are no robust AI-enabled video-oculographic solutions (e.g., digital biomarkers) for screening, triaging, or localizing brain abnormalities due to privacy issues and scarce datasets. In this work, we propose the first fully synthetic, patient-free, multimodal eye movement generation pipeline for generalizable saccade analysis. Using this synthetic dataset, we trained a deep learning classifier to distinguish between normal and abnormal (hypometria and hypermetria) saccadic accuracies and evaluated its performance on real-world clinical data. The model achieved an AUROC of 0.76 and a sensitivity of 0.71, showing that the synthetic data has strong potential to generalize for clinical applications, including as a screening tool in at-home and emergency room settings or a tool for precise neuroanatomic localization.
Public neurophysiological datasets are increasingly accessible but remain hard to reuse: turning one into a trained model still takes thousands of lines of code for download, loading, format repair, windowing, and evaluation, and a dataset that meets metadata standards can still fail to load. EEG-Dash is a software resource that catalogues 791 publicly archived recordings (39,778 participants, over 86,051 hours) spanning electroencephalography (EEG), magnetoencephalography (MEG), intracranial EEG (iEEG), electromyography (EMG), and functional near-infrared spectroscopy (fNIRS) from the OpenNeuro and NEMAR archives. It exposes each dataset as an importable, queryable class that preserves signal attributes and loads into machine-learning workflows without custom code, delegating signal handling to MNE-Python, windowing to Braindecode, and format compliance to the official Brain Imaging Data Structure (BIDS) validator. A metadata-first registry adds semantic search, a format-repair layer, automatic dataset-level tags drawn from each source publication, and a feature-extraction framework. The catalogue, with per-record loadability and compliance metadata, supports benchmarking, model d
Scalp electroencephalography (EEG) and intracranial EEG (iEEG) are vital for epilepsy diagnosis and treatment. Their unified analysis offers the potential to harness the complementary strengths of each modality but is challenging due to variations in recording montages, amplitude and signal-to-noise ratio (SNR), and frequency components. To address the aforementioned challenges, this paper introduces EpiNT, a novel Transformer-based pre-trained model for unified EEG and iEEG analysis. EpiNT employs channel-independent modeling with masked autoencoders (MAE) and vector quantization (VQ), along with a frequency domain mapping quantizer to capture crucial frequency features. Pre-trained on over 2,700 hours of multi-modal clinical neurophysiological data from 1,199 patients, EpiNT outperformed both randomly initialized models and other pre-trained methods on six downstream classification tasks, demonstrating robust representation learning capabilities. This work presents a promising approach for unified epilepsy neurophysiology analysis.
We introduce a gradient modeling framework for memristive systems. Our focus is on memristive systems as they appear in neurophysiology and neuromorphic systems. Revisiting the original definition of Chua, we regard memristive elements as gradient operators of quadratic functionals with respect to a metric determined by the memristance. We explore the consequences of gradient properties for the analysis and design of neuromorphic circuits.
Speech-based depression detection has shown promise as an objective diagnostic tool, yet the cross-linguistic robustness of acoustic markers and their neurobiological underpinnings remain underexplored. This study extends Cross-Data Multilevel Attention (CDMA) framework, initially validated on Italian, to investigate these dimensions using a Chinese Mandarin dataset with Electroencephalography (EEG) recordings. We systematically fuse read speech with spontaneous speech across different emotional valences (positive, neutral, negative) to investigate whether emotional arousal is a more critical factor than valence polarity in enhancing detection performance in speech. Additionally, we establish the first neurophysiological validation for a speech-based depression model by correlating its predictions with neural oscillatory patterns during emotional face processing. Our results demonstrate strong cross-linguistic generalizability of the CDMA framework, achieving state-of-the-art performance (F1-score up to 89.6%) on the Chinese dataset, which is comparable to the previous Italian validation. Critically, emotionally valenced speech (both positive and negative) significantly outperforme
Adaptive reasoning enables humans to flexibly adjust inference strategies when environmental rules or contexts change, yet its underlying neural dynamics remain unclear. This study investigated the neurophysiological mechanisms of adaptive reasoning using a card-sorting paradigm combined with electroencephalography and compared human performance with that of a multimodal large language model. Stimulus- and feedback-locked analyses revealed coordinated delta-theta-alpha dynamics: early delta-theta activity reflected exploratory monitoring and rule inference, whereas occipital alpha engagement indicated confirmatory stabilization of attention after successful rule identification. In contrast, the multimodal large language model exhibited only short-term feedback-driven adjustments without hierarchical rule abstraction or genuine adaptive reasoning. These findings identify the neural signatures of human adaptive reasoning and highlight the need for brain-inspired artificial intelligence that incorporates oscillatory feedback coordination for true context-sensitive adaptation.
Adaptive deep brain stimulation (aDBS) has emerged as a promising treatment for Parkinson disease (PD). In aDBS, a surgically placed electrode sends dynamically altered stimuli to the brain based on neurophysiological feedback: an invasive gadget that limits the amount of data one could collect for optimizing the control offline. As a consequence, a plethora of synthetic models of PD and those of the control algorithms have been proposed. Herein, we introduce the first neurophysiologically realistic benchmark for comparing said models. Specifically, our methodology covers not only conventional basal ganglia circuit dynamics and pathological oscillations, but also captures 15 previously dismissed physiological attributes, such as signal instabilities and noise, neural drift, electrode conductance changes and individual variability - all modeled as spatially distributed and temporally registered features via beta-band activity in the brain and a feedback. Furthermore, we purposely built our framework as a structured environment for training and evaluating deep reinforcement learning (RL) algorithms, opening new possibilities for optimizing aDBS control strategies and inviting the mac
This study investigates emotional expression and perception in music performance using computational and neurophysiological methods. The influence of different performance settings, such as repertoire, diatonic modal etudes, and improvisation, as well as levels of expressiveness, on performers' emotional communication and listeners' reactions is explored. Professional musicians performed various tasks, and emotional annotations were provided by both performers and the audience. Audio analysis revealed that expressive and improvisational performances exhibited unique acoustic features, while emotion analysis showed stronger emotional responses. Neurophysiological measurements indicated greater relaxation in improvisational performances. This multimodal study highlights the significance of expressivity in enhancing emotional communication and audience engagement.
Many individuals especially those with autism spectrum disorder (ASD), alexithymia, or other neurodivergent profiles face challenges in recognizing, expressing, or interpreting emotions. To support more inclusive and personalized emotion technologies, we present a real-time multimodal emotion estimation system that combines neurophysiological EEG, ECG, blood volume pulse (BVP), and galvanic skin response (GSR/EDA) and behavioral modalities (facial expressions, and speech) in a unified arousal-valence 2D interface to track moment-to-moment emotional states. This architecture enables interpretable, user-specific analysis and supports applications in emotion education, neuroadaptive feedback, and interaction support for neurodiverse users. Two demonstration scenarios illustrate its application: (1) passive media viewing (2D or VR videos) reveals cortical and autonomic responses to affective content, and (2) semi-scripted conversations with a facilitator or virtual agent capture real-time facial and vocal expressions. These tasks enable controlled and naturalistic emotion monitoring, making the system well-suited for personalized feedback and neurodiversity-informed interaction design.
Background: Silence is a common phenomenon in classrooms, yet its implicit nature limits a clear understanding of students' underlying learning statuses. Aim: This study proposed a nuanced framework to classify classroom silence based on class events and student status, and examined neurophysiological markers to reveal similarities and differences in silent states across achievement groups. Sample: The study involved 54 middle school students during 34 math lessons, with simultaneous recordings of electroencephalogram (EEG), electrodermal activity (EDA), and heart rate signals, alongside video coding of classroom behaviors. Results: We found that high-achieving students showed no significant difference in mean EDA features between strategic silence (i.e., students choose silence deliberately) and active speaking during open questioning but exhibited higher EEG high-frequency relative power spectral density (RPSD) during strategic silence. In structural silence (i.e., students maintain silence following an external command) during directed questioning, they demonstrated significantly higher heart rates while listening to lectures compared to group activities, indicating heightened e
The appearance of an object triggers an orienting gaze movement toward its location. The movement consists of a rapid rotation of the eyes, the saccade, which is accompanied by a head rotation if the target eccentricity exceeds the oculomotor range, by a slow eye movement if it moves. Completing a previous report, we explain the numerous points that lead to questioning the validity of a one-to-one correspondence relation between measured physical values of gaze or head orientation and neuronal activity. Conflating kinematic (or dynamic) numerical values with neurophysiological recordings carries the risk of believing that central neuron activity directly encodes gaze or head orientation rather than mediating changes in extraocular and neck muscle contraction. Rather than reducing mismatches between extrinsic physical parameters (such as position or velocity errors), eye and head movements are behavioral expressions of intrinsic processes that restore a poly-equilibrium, i.e., balances of activities opposing antagonistic visuomotor channels. Past results obtained in the cat and monkey left a treasure allowing a synthesis, which illustrates the tremendous complexity underlying the ch
We apply modified diffusion entropy analysis (MDEA) to assess multifractal dimensions of ON time series (ONTS) and complexity synchronization (CS) analysis to infer information transfer among ONs that are part of a network of organ networks (NoONs). The purpose of this paper is to advance the validation, standardization, and repeatability of MDEA and CS analysis of heterogeneous neurophysiological time series data. Results from processing these datasets show that the complexity of brain, heart, and lung ONTS significantly co-vary over time during cognitive task performance but that certain principles, guidelines, and strategies for the application of MDEA analysis need consideration.