Various sensing modalities, including external and internal sensors, have been employed in research on human activity recognition (HAR). Among these, internal sensors, particularly wearable technologies, hold significant promise due to their lightwei...
Background: Performance estimates in motor-imagery electroencephalography (MI-EEG) can depend strongly on how observations are partitioned for training, model selection, and testing. Random sample- or window-level splitting may place data from the sa...
The recent scientific literature abounds in proposals of seizure forecasting methods that exploit machine learning to automatically analyze electroencephalogram (EEG) signals. Deep learning algorithms seem to achieve a particularly remarkable perform...
Depression is a widespread mental health disorder whose early diagnosis is often hindered by the reliance on subjective clinical assessments. This study proposes a subject-aware machine learning framework for detecting depression using wrist-worn act...
Background: Inflammatory bowel disease (IBD) is characterised by severe intestinal microbial dysbiosis. Most machine learning diagnostic models built on the longitudinal HMP2 cohort suffer serious data leakage from random sample-level cross-validatio...
Skeletal muscle DNA methylation is closely associated with molecular adaptation to exercise training, but longitudinal studies in this area are constrained by the difficulty of repeated muscle biopsy sampling and extended follow-up. The resulting dat...
Electroencephalography (EEG)-based Brain–Computer Interfaces offer direct insight into a driver’s mental state, relevant to autonomous-vehicle perception stacks. This work addresses vigilance/drowsiness specifically, one of several driver...
The current study proposes an improved DenseNet-based Sequential Multimodal Biometric Authentication System, involving face and ear modality for better human identification. The architecture is composed of three convolutional layers and two dense lay...
Eye-tracking technology enables communication for individuals with muscle control difficulties, making it a valuable assistive tool. Traditional systems rely on electrooculography (EOG) or infrared devices, which are accurate but costly and invasive....
In this article, we introduce explainable methods to understand how Human Activity Recognition (HAR) mobile systems perform based on the chosen validation strategies. Our results introduce a new way to discover potential bias problems that overestima...
This study proposes a novel SSA-EMS framework that integrates Singular Spectrum Analysis (SSA) with Effect-Matched Spatial Filtering (EMS), combining the noise-reduction capability of SSA with the dynamic feature extraction advantages of EMS to optim...
Background/Objectives: Electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) can objectively capture the spatiotemporal dynamics of brain activity during affective cognition, and their combination is promising for improving e...
Background/Objectives: Detecting Alzheimer’s disease (AD) from normal aging using eyes-open (EO) EEG is challenging due to stimulus-driven nonstationarity and fragmented oscillatory responses. This study aims to determine whether prototype-base...
Wearable monitoring of autonomic dysregulation preceding anxiety disorders must generalize to unseen wearers; reported accuracy reflects the evaluation protocol as much as the model. We measured it with one fixed instrument, the Autonomic-Branch Quan...
This study evaluates fixed and learned logit fusion for clip-level pre-drowsiness classification on an adaptively curated cohort from the University of Texas at Arlington Real-Life Drowsiness Dataset (UTA-RLDD). The target is an operationally defined...
Background/Objectives: EEG-based mental performance classification requires participant-separated validation and traceable feature identities. Methods: BioTWavePat-XFE constructs five single-rhythm and 15 signed cross-rhythm channel-rank transition l...
Individual differences often appear in electroencephalography (EEG) data collected from different subjects due to its weak, nonstationary and low signal-to-noise ratio properties. This causes many machine learning methods to have poor generalization...
Human activity recognition (HAR) using inertial sensors is essential for health monitoring and wellness applications, yet robust classification in real-world adult scenarios remains challenging due to subject variability and activity transitions in s...
In this paper, utilising real-internet traffic data, we modified a popular network emulator to better imitate real network traffic and studied its subjective and objective implications on QoE for cloud-gaming apps. Subjective QoE evaluation was then...
Background/Objectives: Electroencephalography (EEG), with its high temporal resolution, non-invasiveness, and cost-effectiveness, provides a suitable modality for investigating task-related signal patterns associated with Visual Sustained Attention (...
Traditional taste evaluation relies heavily on manual sensory analysis, which is highly subjective and inefficient with poor cross-individual generalization, limiting its application in industrial flavor detection. To achieve accurate cross-subject t...
Entropy-based analyses have emerged as a powerful tool for quantifying the complexity, regularity, and information content of complex biological signals, such as electroencephalography (EEG). In this regard, EEG-based lie detection offers the advanta...
With the widespread use of emotion recognition, cross-subject emotion recognition based on EEG signals has become a hot topic in affective computing. Electroencephalography (EEG) can be used to detect the brain’s electrical activity associated...
Driving fatigued is a prevalent issue frequently contributing to traffic accidents, prompting the development of automated fatigue detection methods based on various data sources, particularly reliable physiological signals. However, challenges in ac...
Reducing user-specific calibration is critical for practical steady-state visual evoked potential (SSVEP)-based brain–computer interfaces (BCIs), yet few-shot cross-subject decoding remains challenged by heterogeneous source transferability and...
This study proposes a lightweight domain-assisted deep learning framework for binary human presence detection using ultra-wideband (UWB) radar. The proposed methodology processes raw UWB radar signals through statistically screened, physics-grounded...
Background: Shoulder injury is common among athletes who engage in sports where the upper limb is actively involved. These injuries can affect an athlete’s performance and ability to return to sports at the preinjury level. This study aimed to...
Real-time facial emotion recognition remains challenging due to the high dimensionality and computational cost of dense facial representations, which limit their applicability in resource-constrained and real-time scenarios. This study proposes a com...
A brain-computer interface (BCI) translates a user’s thoughts such as motor imagery (MI) into the control of external devices. However, some people, who are defined as BCI illiteracy, cannot control BCI effectively. The main characteristics of...
Motor imagery (MI) classification using EEG signals is central to brain–computer interfaces but remains challenging due to low signal-to-noise ratio, non-stationarity, and high inter-subject variability. We introduce EEG-TriNet++, a multi-branc...
Background: Alzheimer’s disease (AD) is a progressive neurodegenerative disorder that poses critical challenges in global healthcare due to its increasing prevalence and severity. Diagnosing AD and other dementias, such as frontotemporal dement...
Within the broader context of improving interactions between artificial intelligence and humans, the question has arisen regarding whether auditory and rhythmic support could increase attention for visual stimuli that do not stand out clearly from an...
Event-Related Desynchronization (ERD) or Electroencephalogram (EEG) wavelet is essential for motor imagery (MI) classification and BMI (Brain–Machine Interface) application. However, it is difficult to recognize multiple tasks for non-trained subject...
Wearable inertial sensors produce multi-axis motion signals with rich spatial and temporal structure. Existing deep-learning pipelines for human activity recognition (HAR) rarely tackle three issues jointly: explicit modeling of the body-part groupin...
In the field of brain–computer interfaces (BCI), the decoding of motor imagery EEG signals is significantly hindered by individual differences in EEG signals, which limits the generalization ability of decoding models. To address this challenge...
Spatiotemporal deep learning models, like Graph Neural Networks (GNNs), Transformers, and selective State Space Models (Mamba), have achieved impressive performance in electroencephalogram (EEG) decoding and affective computing. However, their genera...
Neonatal seizures are a critical early indicator of neurological injury, yet effective automated detection is challenged by significant inter-subject variability in electroencephalogram (EEG) signals. To address this generalization gap, this study in...
Background/Objectives: Electroencephalography (EEG) is a promising modality for fatigue detection because it directly reflects neural states; however, it is hindered by the need for subject-specific calibration and its reliance on unstable labeling....
Accurate emotion recognition is crucial for enhancing human–computer interaction, and brain–computer interface (BCI) technology offers an efficient means for emotion detection using the EEG signal. However, existing methods face significa...
Pronoun resolution is essential for language comprehension, yet the neural mechanisms underlying this process remain poorly characterized. Here, we investigate the neural dynamics of impersonal pronoun processing using electroencephalography combined...
Action quality assessment in vocational fitter training is constrained by limited expert-annotated data and insufficiently interpretable predictions. To achieve accurate scoring and rubric-aligned, auditable explanations with limited data, this study...
Virtual reality exposure therapy (VRET) can have a significant impact towards assessing and potentially treating various anxiety disorders. One of the main strengths of VRET systems is that they provide an opportunity for a psychologist to interact w...
Background/Objectives: Hybrid EEG-fNIRS emotion recognition aims to accurately identify an individual’s emotional state by analyzing neurophysiological signals and constitutes an important research direction in affective brain–computer in...
The deployment of conditionally automated vehicles raises safety concerns, as drivers often engage in non-driving-related tasks (NDRTs), delaying takeover responses. This study investigates driver state monitoring (DSM) using multimodal physiological...
BACKGROUND. Mental task identification using electroencephalography (EEG) signals is required for patients with limited or no motor movements. A subject-independent mental task classification framework can be applied to identify the mental task of a...
Motor imagery (MI) promotes motor learning and encourages brain–computer interface systems that entail electroencephalogram (EEG) decoding. However, a long period of training is required to master brain rhythms’ self-regulation, resulting in users wi...
Background: Shooting performance depends on environmental conditions and neural state during preparation. Few studies have applied machine learning (ML) to classify shooting conditions and performance levels using EEG. This study assessed whether pre...
While electroencephalography (EEG) is effective for assessing stress and mental workload, its widespread adoption is currently hindered by the complex setup of most existing EEG systems. This article presents a new in-ear EEG system and investigates...
Major depressive disorder (MDD) is a prevalent and severe mental disorder, and EEG-based automated detection has become a promising approach for auxiliary screening diagnosis. In this work, we propose a novel multiscale spatiotemporal graph neural ne...
Accurate and subject-independent recognition of shoulder physiotherapy exercises from wrist-worn inertial measurement unit (IMU) signals is essential for automated home-based rehabilitation monitoring, yet existing deep learning models are too parame...
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