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Kaneshiro, B.

Publications and source records attributed to Kaneshiro, B..

16 recordsLinked to original sources

Bringing Attention to Education: Revisiting the Neural Mechanisms of Selective Attention in Naturalistic Learning

In the context of education, attention can be considered the gateway for learning, yet it remains unclear which neural mechanisms of attention identified under controlled laboratory conditions are most relevant when children engage in meaningful learning. Here, we addressed this question by experimentally manipulating attention while 5th- and 6th-grade students learned novel educational content from their own teacher. Working in partnership with an experienced classroom teacher, we co-developed naturalistic auditory and visual learning streams and manipulated whether students prioritized or ignored the speech. Using school-based electroencephalography and temporal response function modeling, we examined whether attention modulated early sensory or later stages of cortical speech processing. Attention selectively modulated speech processing at approximately 170 ms, with no evidence for modulation at earlier sensory stages, supporting a predominant role for late-stage attentional selection during learning. Importantly, individual differences in attentional modulation were associated with learning: students who more strongly increased neural tracking of the speech when it was task-relevant learned more effectively from spoken instruction. The same late-stage neural mechanism also distinguished students whom their teacher independently identified as stronger attenders during everyday classroom learning. Together, these findings connect late-stage attentional modulation across experimental neural dynamics, individual learning outcomes, and teacher observations of classroom behavior. More broadly, they demonstrate how studying attention within educationally meaningful contexts can help identify which neural mechanisms are most consequential for successful learning.

neuroscience↗

Semi-Automated Identification of EKG and Trigger Artifacts in EEG Using ICA and Spectral Characteristics

A persistent bottleneck in post-Independent Component Analysis (ICA) Electroencephalogram (EEG) preprocessing is the manual identification of artifact components for removal. In practice, this step can be slow, subjective, and difficult to standardize, particularly for cardiac contamination and trigger-related leakage, where artifact structure may be distributed across multiple components or appear outside the highest-variance Independent Components (ICs). We developed the SENSI-EEG-Preproc-ICA-EKG-Trigger Module to make this stage faster and more reproducible without removing the user from the decision process. The Module is a semi-automated MATLAB framework for post-ICA screening of cardiac and trigger-related artifact components using spectral characteristics. EKG candidates are prioritized by detecting harmonic structure around a physiologically plausible heart-rate fundamental, whereas trigger-related candidates are prioritized by measuring harmonic concentration at frequencies determined by the known repetition period of the trigger sequence. The resulting candidates are then reviewed in dedicated interfaces that present scalp topography, time-domain activity, and frequency-domain structure together, allowing the final classification to be confirmed or corrected by the user. In this way, the Module narrows the search space while preserving interpretability and explicit human control over the final keep/remove decision. The release includes a public codebase, a user manual, example workflows, and an accompanying example dataset. This paper presents the Module as a practical methods-and-software contribution for post-ICA EEG cleaning.

neuroscience↗

Auditory attention reorganizes the phase alignment of neural oscillations

Auditory attention enables the selection of behaviorally relevant sounds in dynamic environments, supporting the flexible allocation of neural resources over time. Although neural entrainment has been proposed as a mechanism for temporal prediction in audition, human studies have largely emphasized changes in response strength, leaving unresolved whether attention reorganizes the temporal alignment of entrained activity across hierarchical cortical networks. Here, we introduce the Selective Temporal Alignment of Components (STAC) framework to dissociate stimulus-driven and attention-controlled dynamics using non-invasive EEG. In a series of experiments across two independent adolescent cohorts (n = 79), Reliable Components Analysis (RCA) revealed two dissociable entrained networks with distinct spatial, functional, and attentional profiles: a sensory-driven network that remained tightly stimulus-locked and a frontal-auditory network that exhibited systematic attention-dependent phase shifts. These phase dynamics were consistent across independent cohorts and stable within individuals, and critically, predicted performance on a standardized neuropsychological measure of auditory attention. Together, these findings establish selective temporal alignment as a robust and behaviorally relevant neural mechanism underlying auditory attentional control.

neuroscience↗

EEG Bad-Channel Detection Using Multi-Feature Thresholding and Co-occurrence of High-Amplitude Transients

Bad channels in electroencephalography (EEG) recordings can substantially degrade downstream analyses, particularly in high-density datasets where localized hardware or motionrelated artifacts may affect groups of electrodes in a structured manner. We introduce a MATLAB Module for bad-channel quality control that emphasizes interpretability, relational structure, and human-in-the-loop validation rather than fully automated rejection. The method operates on multichannel EEG data and combines complementary channel-level features, including time-dependent neighbor dissimilarity and amplitude- and variance-based statistics to score and pre-label channels as good, suspicious, or bad. To expose shared artifactual structure, channels are additionally grouped using a similarity measure derived from the co-occurrence of robustly detected high-amplitude transients, allowing channels to be reviewed together. Importantly, clustering is used as an exploratory tool to reveal co-artifactual patterns rather than to impose final class labels, which are confirmed through an interactive review interface supported by summary visualizations and grouped channel displays. This Module is released as a publicly available codebase with documentation, example work-flows, and a supporting dataset. This Module is designed as a quality-control step preceding ICA and does not replace end-to-end data cleaning pipelines, which typically involve additional steps such as interpolation of known bad channels.

neuroscience↗

MatClassRSA v2 release: A MATLAB toolbox for M/EEG classification, proximity matrix construction, and visualization

MatClassRSA is a MATLAB toolbox that performs magnetoen-cephalography and electroencephalography (M/EEG) classification and other analyses related to Representational Similarity Analysis (RSA). The toolbox is designed for cognitive neuroscience researchers who wish to perform classification-based decoding analyses of their data--often repeated trials of evoked responses--or derive Representational Dissimilarity Matrices (RDMs) as input to RSA. Organized as a collection of usercalled functions, MatClassRSA v2 comprises five main modules: Preprocessing, Reliability, Classification, RDM Computation, and Visualization. The Classification module supports multiple classifiers (LDA, RF, SVM) and classification schemes (e.g., multiclass, pairwise, cross-validated, train-test, hyperparameter optimization) and offers basic statistical analyses via permutation testing. Functions in other modules include, for example, trial-averaging and noise normalization; reliability estimation; non-classification RDM construction; and hierarchical and non-hierarchical clustering visualizations. The toolbox is freely available on GitHub under an MIT license and includes the main codebase, a User Manual, example function calls, and illustrative analyses. This preprint provides a general background of M/EEG classification for RSA as well as a narrative overview of the v2 MatClassRSA release, its updated functionalities, and illustrative analyses performed using the toolbox.

neuroscience↗

Reading fluency development is predicted by cortical latency

The development of fluent literacy skills from childhood to adolescence is strongly constrained by the temporal dynamics of word recognition. Capturing the neural basis of these subtle timing changes in word recognition has remained challenging with EEG measures that lack reliability at the individual subject level. Here, we leverage phase information from Steady-State Visual Evoked Potentials (SSVEPs) to derive precise and reliable temporal dynamics of neural signatures underlying visual word form recognition at the individual level and examine their relationship to reading fluency and comprehension. Typically developing readers (N = 68), aged 8-15 years, viewed a stream of four-character stimulus strings presented at 3 Hz. Significant SSVEP signals emerged for nearly all participants. Signals at 3, 6, and 9 Hz harmonics exhibited a phase pattern consistent with a delay model, indicating a mean latency of approximately 170 milliseconds. Individual variations in latencies demonstrated (a) high internal consistency (R = .94); (b) stability across variations in letter string forms (familiar words, nonwords with familiar letters, nonwords with unfamiliar pseudo-characters); (c) a linear relationship with age; and most remarkably, (d) a predictive relationship with individual variation in reading fluency and reading comprehension. These results establish SSVEP visual word form latency as a promising approach for investigating the neural basis of reading development, paving the way for future translational applications in education and offering potential solutions to broader societal challenges in promoting population-level reading fluency.

neuroscience↗

Brain plasticity for visual words: Elementary school teachers can drive changes in weeks that rival those formed over years

This study aims to investigate the impact of vocabulary acquisition through short-term classroom learning and its relation to broader forms of vocabulary learning through long-term exposure in daily life. Through a two week of "learning sprint" in collaboration with a local elementary school and EEG-Steady State Visual Evoked Potentials (EEG-SSVEP) paradigm, we assessed new vocabulary learning in first and second graders within their pedagogical environment. We then compared the results with the word frequency effect, a well-established phenonmenon that reflects long-term vocabulary learning. After two weeks of classroom instruction, newly acquired words elicited neural responses similar to those of high-frequency words, with the effect significantly correlated with childrens phonological decoding skills. Additionally, we successfully replicated the word frequency effect using the SSVEP paradigm for the first time. These findings highlight the potential of the "learning sprint" model for conducting neuroscience research in authentic educational settings, thereby fostering a stronger connection between education and neuroscience.

neuroscience↗

Starting to see a pattern: Longitudinal SSVEP signals reveal how growth in children's cortical sensitivity to visual word form structure is uniquely linked to two-year growth in reading fluency

Learning to read depends on the ability to extract precise details of letter combinations, which convey critical information that distinguishes tens of thousands of visual word forms. To support fluent reading skill, one crucial neural developmental process is ones brain sensitivity to statistical constraints inherent in combining letters into visual word forms. To test this idea in early readers, we tracked the impact of two years of schooling on within-subject longitudinal changes in cortical responses to different properties of words (coarse tuning for print, and fine tuning to visual word forms and whole word representations) and their growth in reading skill. Three stimulus contrasts--words versus pseudofonts, words versus pseudowords, pseudowords versus nonwords--were presented while high-density EEG Steady-State Visual Evoked Potentials (SSVEPs, n=31) were recorded. Internalization of abstract visual word form structures over two years of reading experience resulted in a near doubling of SSVEP amplitude, with increasing left lateralization. Longitudinal changes in brain responses to such word form structural information were linked to the growth in reading, especially in rapid automatic naming for letters. No such changes were observed for whole word representation processing and coarse tuning for print. Collectively, these findings indicate that sensitivity to visual word form structure develops rapidly through exposure to print and is linked to growth in reading skill. Research HighlightsO_LILongitudinal changes in cognitive responses to coarse print tuning, visual word from structure, and whole word representation were examined in early readers. C_LIO_LIVisual word form structure processing demonstrated striking patterns of growth with nearly doubled in EEG amplitude and increased left lateralization. C_LIO_LILongitudinal changes in brain responses to visual word form structural information were linked to the growth in rapid automatic naming for letters. C_LIO_LINo longitudinal changes were observed for whole word representation processing and coarse tuning for print. C_LI

scientific communication and education↗

Early detection of sublexical and lexical processing in beginning readers: evidence from Steady-State Visual Evoked Potentials (SSVEPs)

There are multiple levels of processing relevant to reading that vary in their visual, sublexical and lexical orthographic processing demands. Segregating distinct cortical sources for each of these levels has been challenging in EEG studies of early readers. To address this challenge, we applied recent advances in analyzing high-density EEG using Steady-State Visual Evoked Potentials (SSVEPs) via data-driven Reliable Components Analysis (RCA) in a group of early readers spanning from kindergarten to second grade. Three controlled stimulus contrasts--familiar words versus unfamiliar pseudofonts, familiar words versus orthographically legal pseudowords, and orthographically legal pseudowords versus orthographically illegal nonwords--were used to isolate visual print/letter selectivity, sublexical processing, and lexical processing, respectively. We found robust responses specific to each of these processing levels, even in kindergarteners who have limited knowledge of print. Moreover, comparing amplitudes of these three stimulus contrasts across three reading fluency-based groups and three grade-based groups revealed fluency group and grade group main effects only for lexical contrast (i.e., words versus orthographically legal pseudowords). Furthermore, we found that sublexical orthography-related responses shifted their topographic distribution from the right to left hemisphere from kindergarten to first and second grades. Results suggest that, with more sensitive measures, the sublexical and lexical fine tuning for words--as a bio-marker of reading ability--can be detected at a much earlier stage than previously assumed. Declarations of interestNone

neuroscience↗

Inter-Subject Correlation during New Music Listening: A Study of Electrophysiological and Behavioral Responses to Steve Reich's Piano Phase

Musical minimalism utilizes the temporal manipulation of restricted collections of rhythmic, melodic, and/or harmonic materials. One example, Steve Reichs Piano Phase, offers listeners readily audible formal structures containing unpredictable events at local levels. Pattern recurrences may generate strong expectations which are violated by small temporal and pitch deviations. A hyper-detailed listening strategy prompted by these minute deviations stands in contrast to the type of listening engagement typically cultivated around functional tonal Western music. Recent research has suggested that the inter-subject correlation (ISC) of electroencephalographic (EEG) responses to natural audio-visual stimuli objectively indexes a state of "engagement", demonstrating the potential of this approach for analyzing music listening. But can ISCs capture engagement with minimal music, which features less obvious expectation formation and has historically received a wide range of reactions? To approach this question, we collected EEG and continuous behavioral (CB) data while 30 adults listened to an excerpt from Steve Reichs Piano Phase, as well as three controlled manipulations and a popular-music remix of the work. Our analyses reveal that EEG and CB ISC are highest for the remix stimulus and lowest for our most repetitive manipulation. In addition, we found no statistical differences in overall EEG ISC between our most musically meaningful manipulations and Reichs original piece. We also found that aesthetic evaluations corresponded well with overall EEG ISC. Finally we highlight co-occurrences between stimulus events and time-resolved EEG and CB ISC. We offer the CB paradigm as a useful analysis measure and note the value of minimalist compositions as a limit case for studying music listening using EEG ISC. We show that ISC is less effective at measuring engagement with this minimalist stimulus than with popular music genres and argue that this may be due to a difference between the type of engagement measured by ISC and the particular engagement patterns associated with minimalism.

neuroscience↗

Inter-Subject EEG Correlation Reflects Time-Varying Engagement with Natural Music

Musical engagement can be conceptualized through various activities, modes of listening, and listener states--among these a state of focused engagement. Recent research has reported that this state can be indexed by the inter-subject correlation (ISC) of EEG responses to a shared naturalistic stimulus. While statistically significant ISC has been reported during music listening, these reports have considered only correlations computed across entire excerpts and do not provide insights into time-varying engagement. Here we present the first EEG-ISC investigation of time-varying engagement within a musical work. From a sample of 23 adult musicians who listened to a cello concerto movement, we find varying levels of ISC throughout the excerpt. In particular, significant ISC is observed during periods of musical tension that build to climactic highpoints, but not at the highpoints themselves. In addition, we find that a control stimulus retaining envelope characteristics of the intact music, but little other temporal structure, also elicits significant neural correlation, though to a lesser extent than the original excerpt. In all, our findings shed light on temporal dynamics of listener engagement during music listening, establish connections between salient musical events and EEG ISC, and clarify specific listener states that are indexed by this measure.

neuroscience↗

Neural Sources Underlying Visual Word Form Processing as Revealed by Steady State Visual Evoked Potentials (SSVEP)

EEG has been central to investigations of the time course of various neural functions underpinning visual word recognition. Recently the steady-state visual evoked potential (SSVEP) paradigm has been increasingly adopted for word recognition studies due to its high signal-to-noise ratio. Such studies, however, have been typically framed around a single source in the left ventral occipitotemporal cortex (vOT). Here, we combine SSVEP recorded from 16 adult native English speakers with a data-driven spatial filtering approach--Reliable Components Analysis (RCA)--to elucidate distinct functional sources with overlapping yet separable time courses and topographies that emerge when contrasting words with pseudofont visual controls. The first component topography was maximal over left vOT regions with an early latency (approximately 180 msec). A second component was maximal over more dorsal parietal regions with a longer latency (approximately 260 msec). Both components consistently emerged across a range of parameter manipulations including changes in the spatial overlap between successive stimuli, and changes in both base and deviation frequency. We then contrasted word-in-nonword and word-in-pseudoword to test the hierarchical processing mechanisms underlying visual word recognition. Results suggest that these hierarchical contrasts fail to evoke a unitary component that might be reasonably associated with lexical access.

neuroscience↗

Time-Resolved Correspondences Between Deep Neural Network Layers and EEG Measurements in Object Processing

The ventral visual stream is known to be organized hierarchically, where early visual areas processing simplistic features feed into higher visual areas processing more complex features. Hierarchical convolutional neural networks (CNNs) were largely inspired by this type of brain organization and have been successfully used to model neural responses in different areas of the visual system. In this work, we aim to understand how an instance of these models corresponds to temporal dynamics of human object processing. Using representational similarity analysis (RSA) and various similarity metrics, we compare the model representations with two electroencephalography (EEG) data sets containing responses to a shared set of 72 images. We find that there is a hierarchical relationship between the depth of a layer and the time at which peak correlation with the brain response occurs for certain similarity metrics in both data sets. However, when comparing across layers in the neural network, the correlation onset time did not appear in a strictly hierarchical fashion. We present two additional methods that improve upon the achieved correlations by optimally weighting features from the CNN and show that depending on the similarity metric, deeper layers of the CNN provide a better correspondence than shallow layers to later time points in the EEG responses. However, we do not find that shallow layers provide better correspondences than those of deeper layers to early time points, an observation that violates the hierarchy and is in agreement with the finding from the onset-time analysis. This work makes a first comparison of various response features--including multiple similarity metrics and data sets--with respect to a neural network.

neuroscience↗

Natural Music Evokes Correlated EEG Responses Reflecting Temporal Structure and Beat

The brain activity of multiple subjects has been shown to synchronize during salient moments of natural stimuli, suggesting that correlation of neural responses indexes a brain state operationally termed engagement. While past electroencephalography (EEG) studies have considered both auditory and visual stimuli, the extent to which these results generalize to music--a temporally structured stimulus for which the brain has evolved specialized circuitry--is less understood. Here we investigated neural correlation during natural music listening by recording dense-array EEG responses from N = 48 adult listeners as they heard real-world musical works, some of which were temporally disrupted through shuffling of short-term segments (measures), reversal, or randomization of phase spectra. We measured neural correlation across responses (inter-subject correlation) and between responses and stimulus envelope fluctuations (stimulus-response correlation) in the time and frequency domains. Stimuli retaining basic musical features evoked significantly correlated neural responses in all analyses. However, while unedited songs were self-reported as most pleasant, time-domain correlations were highest during measure-shuffled versions. Frequency-domain measures of correlation (coherence) peaked at frequencies related to the musical beat, although the magnitudes of these spectral peaks did not explain the observed temporal correlations. Our findings show that natural music evokes significant inter-subject and stimulus-response correlations, and suggest that the neural correlates of musical engagement may be distinct from those of enjoyment.

neuroscience↗

Decoding Speech and Music Stimuli from the Frequency Following Response

The ability to differentiate complex sounds is essential for communication. Here, we propose using a machine-learning approach, called classification, to objectively evaluate auditory perception. In this study, we recorded frequency following responses (FFRs) from 13 normal-hearing adult participants to six short music and speech stimuli sharing similar fundamental frequencies but varying in overall spectral and temporal characteristics. Each participant completed a perceptual identification test using the same stimuli. We used linear discriminant analysis to classify FFRs. Results showed statistically significant FFR classification accuracies using both the full response epoch in the time domain (72.3% accuracy, p < 0.001) as well as real and imaginary Fourier coefficients up to 1 kHz (74.6%, p < 0.001). We classified decomposed versions of the responses in order to examine which response features contributed to successful decoding. Classifier accuracies using Fourier magnitude and phase alone in the same frequency range were lower but still significant (58.2% and 41.3% respectively, p < 0.001). Classification of overlapping 20-msec subsets of the FFR in the time domain similarly produced reduced but significant accuracies (42.3%-62.8%, p < 0.001). Participants mean perceptual responses were most accurate (90.6%, p < 0.001). Confusion matrices from FFR classifications and perceptual responses were converted to distance matrices and visualized as dendrograms. FFR classifications and perceptual responses demonstrate similar patterns of confusion across the stimuli. Our results demonstrate that classification can differentiate auditory stimuli from FFR responses with high accuracy. Moreover, the reduced accuracies obtained when the FFR is decomposed in the time and frequency domains suggest that different response features contribute complementary information, similar to how the human auditory system is thought to rely on both timing and frequency information to accurately process sound. Taken together, these results suggest that FFR classification is a promising approach for objective assessment of auditory perception.

neuroscience↗

MatClassRSA: A Matlab toolbox for M/EEG classification and visualization of proximity matrices

MatClassRSA is a Matlab toolbox that performs M/EEG classification and produces various visualizations of the resulting confusion matrices. This toolbox is aimed toward cognitive neuroscience researchers who wish to apply classification-style analyses to repeated trials of data. The functionalities of the toolbox fall into three categories: (1) M/EEG classification, (2) computation of Representational Dissimilarity Matrices (RDMs) from confusion or other proximity matrices, and (3) clustering and tree visualizations of RDMs. MatClassRSA combines out-of-the-box functionality with a variety of customization options. Usage of the toolbox requires only a high-level understanding of machine learning and the ability to call Matlab functions from scripts.

neuroscience↗