Blair Kaneshiro

This study investigates how attention modulates neural speech processing during learning and links it to individual learning outcomes and teacher observations.

The paper introduces a semi-automated MATLAB module for identifying and removing EKG and trigger artifacts in post-ICA EEG data using spectral characteristics and user review.

The paper investigates how auditory attention affects neural oscillation phase alignment using EEG and identifies two distinct entrained networks with attention-dependent dynamics.

The paper introduces a MATLAB module for detecting bad EEG channels using multi-feature thresholding and co-occurrence of high-amplitude transients, with a focus on interpretability and human-in-the-loop validation.

MatClassRSA v2 is a MATLAB toolbox for M/EEG classification, RDM computation, and visualization, offering updated modules and functionalities for cognitive neuroscience research.

The paper uses SSVEP phase information to measure neural latency in visual word form recognition and shows it predicts reading fluency and comprehension in children.

This study shows that two weeks of classroom vocabulary instruction can produce neural responses similar to long-term vocabulary learning, correlating with phonological decoding skills.

This study examines time-varying neural engagement during music listening using EEG inter-subject correlations, finding significant correlations during musical tension but not at climactic peaks.

The paper examines inter-subject correlation (ISC) of EEG and behavioral responses to minimalist music, finding lower ISC for minimalist stimuli compared to popular music.

The paper uses SSVEPs and RCA to detect sublexical and lexical processing in early readers, finding early evidence of lexical fine-tuning in kindergarteners.

The paper uses SSVEP and RCA to identify distinct neural sources in visual word recognition, finding two components with different latencies and topographies.

This study compares CNN layer representations with EEG data to find hierarchical and non-hierarchical correlations in object processing timing.

This study examines neural correlations during music listening, finding that disrupted musical structures increase time-domain correlations while beat-related frequencies show peak coherence.

This study uses machine learning to classify frequency following responses (FFRs) from speech and music stimuli, showing high accuracy and similar patterns to human perception.

MatClassRSA is a Matlab toolbox for M/EEG classification and visualization of proximity matrices, offering tools for classification, RDM computation, and clustering.