Biology subjects

Wei, C.-S.

Publications and source records attributed to Wei, C.-S..

6 recordsLinked to original sources

bioRxiv2026

A Comprehensive Benchmark of EEG-Based BCI Deep Learning Models for MCI and Dementia Classification

AO_SCPLOWBSTRACTC_SCPLOWElectroencephalography (EEG) is a promising tool for automated detection of mild cognitive impairment (MCI) and dementia, but comparisons across studies are limited by inconsistent datasets and evaluation protocols. This study benchmarks ten deep learning models across four resting-state EEG datasets and eight binary classification tasks using a unified preprocessing pipeline and five-fold subject-wise cross-validation. Each experiment was repeated ten times. SCCNet obtained the highest mean subject-level accuracy, sensitivity, and F1 score, while ShallowConvNet achieved the highest mean segment-level accuracy, specificity, and precision. Subject-level aggregation improved mean accuracy for all evaluated models, and performance varied substantially across datasets and diagnostic tasks. Higher computational cost did not consistently correspond to better classification performance, with several compact architectures remaining competitive with substantially larger models. The results provide a reproducible reference for comparing EEG-based dementia classification models under consistent subject-independent evaluation conditions.

neuroscience↗

bioRxiv2025

YOTO (You Only Think Once): A Human EEG Dataset for Multisensory Perception and Mental Imagery

The YOTO (You Only Think Once) dataset presents a human electroencephalography (EEG) resource for exploring multisensory perception and mental imagery. The study enrolled 26 participants who performed tasks involving both unimodal and multimodal stimuli. Researchers collected high-resolution EEG signals at a 1000 Hz sampling rate to capture high-temporal-resolution neural activity related to internal mental representations. The protocol incorporated visual, auditory, and combined cues to investigate the integration of multiple sensory modalities, and participants provided self-reported vividness ratings that indicate subjective perceptual strength. Technical validation involved event-related potentials (ERPs) and power spectral density (PSD) analyses, which demonstrated the reliability of the data and confirmed distinct neural responses across stimuli. This dataset aims to foster studies on neural decoding, perception, and cognitive modeling, and it is publicly accessible for researchers who seek to advance multimodal mental imagery research and related applications.

neuroscience↗

bioRxiv2025

Forecast Padding Enhances Accuracy and Robustness of EEG-Phase-Synchronized TMS

Closed-loop neuromodulation is a promising personalized treatment for various neuropsychiatric disor-ders, delivering precise stimuli based on real-time brain signals. However, its clinical potential is currently limited by technical challenges inherent in its real-time nature. This article addresses the primary technical challenges in EEG-Phase-Synchronized Transcranial Magnetic Stimulation (TMS), including poor stimulation accuracy and inefficient biomarker detection (correlated with deadlock). These challenges arise from the vulnerability of existing algorithms to filter edge effects. Inspired by predictive coding theory in neuroscience, we propose a novel signal padding method (forecast padding) to mitigate the filter edge effect. To properly quantify the improvements that forecast padding brings about in real-world systems, we introduce a novel delay-relevant validation framework and demonstrate its reliability using experimental data from a real system. Through this framework, we demonstrate that forecast padding significantly improves both stimulation accuracy and deadlock rate. Given the pervasive impact of filter edge effects in closed-loop neuromodulation and other signal processing domains, forecast padding shows broad application potential across various fields.

bioengineering↗

bioRxiv2023

SiamEEGNet: Siamese Neural Network-Based EEG Decoding for Drowsiness Detection

Recent advancements in deep-learning have significantly enhanced EEG-based drowsiness detection. However, most existing methods overlook the importance of relative changes in EEG signals compared to a baseline, a fundamental aspect in conventional EEG analysis including event-related potential and time-frequency spectrograms. We herein introduce SiamEEGNet, a Siamese neural network architecture designed to capture relative changes between EEG data from the baseline and a time window of interest. Our results demonstrate that SiamEEGNet is capable of robustly learning from high-variability data across multiple sessions/subjects and outperforms existing model architectures in cross-subject scenarios. Furthermore, the models interpretability associates with previous findings of drowsiness-related EEG correlates. The promising performance of SiamEEGNet highlights its potential for practical applications in EEG-based drowsiness detection. We have made the source codes available at http://github.com/CECNL/SiamEEGNet.

neuroscience↗

bioRxiv2023

OICNet: A Neural Network for Online EEG Source Separation using Independent Component Analysis

Online source separation of EEG signals plays a crucial role in understanding and interpreting brain dynamics in real-time applications such as brain-computer interfaces (BCIs). In this paper, we propose OICNet, a novel neural network designed specifically for online EEG source separation using independent component analysis, aiming to address the challenges of real-time computational efficiency and reliable extraction of independent components from EEG data streams. The OICNet is trained on a loss function that integrates non-Gaussianity measurement and an orthogonality constraint to achieve effective decomposition of multi-channel EEG signals. We conducted comprehensive evaluation of OICNet on both task-related and task-free EEG datasets with comparison against conventional and network-based ICA counterparts. The results demonstrate that OICNet outperforms existing methods in terms of accuracy and computational efficiency. Overall, OICNet provides high-efficiency real-time EEG source separation capabilities and paves the way for advancements in deep-learning EEG processing in real-world BCI applications.

bioinformatics↗

bioRxiv2021

Fuzzy Integral With Particle Swarm Optimization for CNN-Based Motor-Imagery EEG Classification

Recently, decoding human electroencephalographic (EEG) data using convolutional neural network (CNN) has driven the state-of-the-art recognition of motor-imagery EEG patterns for brain-computer interfacing (BCI). While a variety of CNN models have been used to classify motor-imagery EEG data, it is unclear if aggregating an ensemble of heterogeneous CNN models could further enhance the classification performance. To integrate the outputs of ensemble classifiers, this work utilizes fuzzy integral with particle swarm optimization (PSO) to estimate optimal confidence levels assigned to classifiers. The proposed frame-work aggregates CNN classifiers and fuzzy integral with PSO, achieving robust performance in single-trial classification of motor-imagery EEG data across various CNN model training schemes depending on the scenarios of BCI usage. This proof-of-concept study demonstrates the feasibility of applying fuzzy fusion techniques to enhance CNN-based EEG decoding and benefit practical applications of BCI.

bioengineering↗