The study investigates how the source of normalization statistics affects the performance of wrist electrodermal activity (EDA) affect‑recognition models. Using the SAFE‑EDA convolutional network pretrained on expert artifact annotations, the authors compare models trained with normalization derived only from training subjects versus from the held‑out subject’s full recording. They find that pretraining improves macro‑F1 when using training‑only statistics, but the benefit diminishes when using the held‑out subject’s data, and that artifact supervision outperforms self‑supervised pretraining. Across multiple configurations, pretrained models generally perform better, though the interaction with per‑user normalization varies by dataset.
By Haochen Chai, Xinbi Luo, Zining Liu, Fangfang Jiang
arXiv:2601. 10191v2 Announce Type: replace Abstract: Automated analysis of needle electromyography (nEMG) signals is emerging as a tool to support the detection of neuromuscular diseases (NMDs), yet the signals' high and heterogeneous sampling rates pose substantial computational challenges for feature-based machine-learning models, particularly for near real-time analysis.
By Mathieu Cherpitel, Janne Luijten, Thomas B\"ack, Camiel Verhamme, Martijn Tannemaat, Anna V. Kononova
This study evaluates machine learning and deep‑learning models for classifying balanced versus imbalanced postural states in immersive virtual reality using a multimodal dataset of kinematic, EMG, and EDA signals. The Mamba‑inspired CNN (MI‑CNN) achieved the highest accuracy (96.76%) and, through SHapley Additive exPlanations (SHAP), identified kinematic features as the most influential for detecting imbalance. Even after reducing input dimensionality by 33% based on SHAP importance, the model maintained near‑optimal performance (0.957 accuracy and F1‑score).
By Nipa Anjum, Md Irfan Pavel, Robert Gonzalez Jr, Kevin Desai, Alberto Cordova, M. Rasel Mahmud, John Quarles
arXiv:2606. 30344v1 Announce Type: cross Abstract: Visual classifiers can achieve high matched-distribution accuracy while relying on low-level cues that fail under conflict or suppression.
By Chanho Park, Woochan Lee, Janyeong Oh, Geongho Gong, Minshu Kim, Yeachan Kwak, Seongim Choi
arXiv:2607. 27568v1 Announce Type: new Abstract: Recognition accuracy obtained during a recording session does not persist when a user puts on the electrodes again after the electrodes had previously been removed.
By Jethro Odeyemi, W. J. Zhang
The paper evaluates how readout settings and operating points affect event-stream candidate selection in event-triggered recording. Using 520 DailyDVS‑200 clips and 47 participants, the authors compare several encoder configurations—including DINO‑CLS, patch mean, and MobileNetV3‑Small—across different accumulation durations and view counts, reporting metrics such as target capture, false triggers, and partial AUC. The study finds that lightweight baselines and specific operating points significantly influence performance, while no clear advantage is observed for the JEPA‑specific method.
By Yifan Shuai
arXiv:2605. 14941v2 Announce Type: replace-cross Abstract: Electroencephalogram (EEG) signals are highly susceptible to artifacts, resulting in a low signal-to-noise ratio, which makes extraction of meaningful neural information challenging.
By Shantanu Sarkar, Jose L. Contreras-Vidal
DenoFlow introduces a flow‑matching approach to denoise steady‑state visual evoked potential (SSVEP) EEG signals, avoiding direct mapping from noisy to clean data. It uses a field network that predicts the velocity of a straight path between contaminated and clean trials, integrating this field to produce denoised outputs while simultaneously training a classifier to supervise the result. Experiments on two public SSVEP datasets demonstrate that DenoFlow surpasses seven baseline denoising models in both signal fidelity and downstream decoding accuracy.
By Zhentao He, Ziwei Wang, Dongrui Wu
BRIDGE-EEG is an efficient multi‑task EEG classification pipeline that leverages self‑supervised pretraining while dramatically reducing model size. It maps heterogeneous EEG recordings to a unified 62‑channel time‑frequency representation, pretrains an SE‑ResNet18 teacher with SimCLR, and distills it into smaller SE‑ResNet8 and SE‑ResNet4 students. The compact models achieve accuracy comparable to or better than larger foundation models on abnormality detection and emotion recognition, and they consume up to three times less energy on edge devices, enabling deployment on wearable hardware.
By Meghna Roy Chowdhury, Chengwei Zhou, Haotian Yu, Gourav Datta, Shreyas Sen
arXiv:2608.30835v1 Announce Type: cross
Abstract: Background and Objective: Quality control is a prerequisite for whole-slide image analysis, yet the benchmarks on which quality-control methods are c...
By Konstantinos Moutselos, Ilias Maglogiannis
The paper introduces GRAFT-EEG, a method that removes the active‑set common‑reference gauge and transports each native EEG mask through a template motor leadfield, enabling target‑statistics‑blind transfer for cross‑dataset motor‑imagery decoding. Using a source‑fitted spectral classifier and a bounded state‑space‑model residual, the approach achieves higher balanced accuracy (60.865%) than a strict‑clean baseline (52.684%) across five leave‑one‑dataset‑out targets, and outperforms several control conditions in post‑hoc tests. The study also reports positive results in a separate 60‑participant same‑paradigm evaluation, though the dynamic‑static comparison was inconclusive.
"whyItMatters":"The method demonstrates that accounting for observation‑operator changes can substantially improve cross‑dataset EEG decoding performance, offering a more robust approach for motor‑imagery applications."
By Ying Zhou, Mengting Liu
arXiv:2607.27655v2 Announce Type: replace
Abstract: Checkpoint selection can improve an electroencephalography (EEG) emotion-recognition score without improving performance on other trials. We compar...
By Hanting Suo, Hongxun Wang, Yuwen Li