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Windowing is a common technique in EEG machine learning classification and other time series tasks. However, a challenge arises when employing this technique: computational expense inhibits learning global relationships across an entire…
Brain biometrics based on electroencephalography (EEG) have been used increasingly for personal identification. Traditional machine learning techniques as well as modern day deep learning methods have been applied with promising results. In…
This study investigated the neural dynamics associated with short-term exposure to different virtual classroom designs with different window placement and room dimension. Participants engaged in five brief cognitive tasks in each design…
Measuring brain activity with electroencephalography (EEG) is mature enough to assess mental states. Combined with existing methods, such tool can be used to strengthen the understanding of user experience. We contribute a set of methods to…
Motor imagery (MI) based EEG represents a frontier in enabling direct neural control of external devices and advancing neural rehabilitation. This study introduces a novel time embedding technique, termed traveling-wave based time…
This study investigated the dynamic connectivity patterns between EEG and fMRI modalities, contributing to our understanding of brain network interactions. By employing a comprehensive approach that integrated static and dynamic analyses of…
In recent years, there has been a shift of interest towards the field of biometric authentication, which proves the identity of the user using their biological characteristics. We explore a novel biometric based on the electrical activity…
Adaptive user interfaces have the advantage of being able to dynamically change their aspect and/or behaviour depending on the characteristics of the context of use, i.e. to improve user experience(UX). UX is an important quality factor…
Understanding the correlation between EEG features and cognitive tasks is crucial for elucidating brain function. Brain activity synchronizes during speaking and listening tasks. However, it is challenging to estimate task-dependent brain…
A Bio-metrics system is actually a pattern recognition system that utilizes various patterns like iris, retina and biological traits like fingerprint, voice recognition, facial geometry and hand geometry. What makes Bio-metrics really…
EEG signals in emotion recognition absorb special attention owing to their high temporal resolution and their information about what happens in the brain. Different regions of brain work together to process information and meanwhile the…
The analysis of brain connectivity aims to understand the emergence of functional networks into the brain. This information can be used in the process of electroencephalographic (EEG) signal analysis and classification for a braincomputer…
Electrocardiogram (ECG) biometrics have emerged as a promising modality for continuous, liveness-aware authentication in wearable systems. However, many prior studies report overly optimistic results due to data leakage (e.g., random splits…
Riemannian geometry has been successfully used in many brain-computer interface (BCI) classification problems and demonstrated superior performance. In this paper, for the first time, it is applied to BCI regression problems, an important…
The idea to estimate the statistical interdependence among (interacting) brain regions has motivated numerous researchers to investigate how the resulting connectivity patterns and networks may organize themselves under any conceivable…
This paper is targeted in the area of biometric data enabled security system based on the machine learning for the digital health. The disadvantages of traditional authentication systems include the risks of forgetfulness, loss, and theft.…
As a promising candidate to complement traditional biometric modalities, brain biometrics using electroencephalography (EEG) data has received a widespread attention in recent years. However, compared with existing biometrics such as…
Electroencephalography (EEG) is a neuroimaging technique that records brain neural activity with high temporal resolution. Unlike other methods, EEG does not require prohibitively expensive equipment and can be easily set up using…
Even though a few initial works have shown on small sets of data some level of bias in the performance of fingerprint recognition technology with respect to certain demographic groups, there is still not sufficient evidence to understand…
Electroencephalogram (EEG) is a very promising and widely implemented procedure to study brain signals and activities by amplifying and measuring the post-synaptical potential arising from electrical impulses produced by neurons and…