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People suffering from hearing impairment often have difficulties participating in conversations in so-called `cocktail party' scenarios with multiple people talking simultaneously. Although advanced algorithms exist to suppress background…
Auditory attention is a selective type of hearing in which people focus their attention intentionally on a specific source of a sound or spoken words whilst ignoring or inhibiting other auditory stimuli. In some sense, the auditory…
Convolutional neural networks (CNNs) are widely used to recognize the user's state through electroencephalography (EEG) signals. In the previous studies, the EEG signals are usually fed into the CNNs in the form of high-dimensional raw…
The ear-EEG has emerged as a promising candidate for wearable brain monitoring in real-world scenarios. While experimental studies have validated ear-EEG in multiple scenarios, the source-sensor relationship for a variety of neural sources…
Electroencephalography (EEG)-based emotion recognition plays a critical role in affective computing and emerging decision-support systems, yet remains challenging due to high-dimensional, noisy, and subject-dependent signals. This study…
The recent developments in wearable devices and the Internet of Medical Things (IoMT) allow real-time monitoring and recording of electrocardiogram (ECG) signals. However, continuous monitoring of ECG signals is challenging in low-power…
A lack of driver's vigilance is the main cause of most vehicle crashes. Electroencephalography(EEG) has been reliable and efficient tool for drivers' drowsiness estimation. Even though previous studies have developed accurate and robust…
This paper proposes a novel lightweight method using the multitaper power spectrum to estimate arousal levels at wearable devices. We show that the spectral slope (1/f) of the electrophysiological power spectrum reflects the scale-free…
Clinical electroencephalography is routinely used to evaluate patients with diverse and often overlapping neurological conditions, yet interpretation remains manual, time-intensive, and variable across experts. While automated EEG analysis…
Understanding the interaction of neural and cardiac systems during cognitive activity is critical to advancing physiological computing. Although EEG has been the gold standard for assessing mental workload, its limited portability restricts…
Advanced multimodal AI agents can now collaborate with users to solve challenges in the world. Yet, these emerging contextual AI systems rely on explicit communication channels between the user and system. We hypothesize that implicit…
Engagement is a vital metric in the advertising industry and its automatic estimation has huge commercial implications. This work presents a basic and simple framework for engagement estimation using EEG (electroencephalography) data…
Neuromarketing is an emerging field that combines neuroscience and marketing to understand the factors that influence consumer decisions better. The study proposes a method to understand consumers' positive and negative reactions to…
Significant research has been conducted in recent years to design low-cost alternatives to the current EEG monitoring systems used in healthcare facilities. Testing such systems on a vulnerable population such as newborns is complicated due…
Conventional biometrics have been employed in high security user authentication systems for over 20 years now. However, some of these modalities face low security issues in common practice. Brain wave based user authentication has emerged…
Cybersickness is an unpleasant side effect of exposure to a virtual reality (VR) experience and refers to such physiological repercussions as nausea and dizziness triggered in response to VR exposure. Given the debilitating effect of…
Epilepsy which is characterized by seizures is studied using EEG signals by recording the electrical activity of the brain. Different types of communication between different parts of the brain are characterized by many state of the art…
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…
Recent promising results in auditory attention decoding (AAD) using scalp electroencephalography (EEG) have motivated the exploration of cEEGrid, a flexible and portable ear-EEG system. While prior cEEGrid-based studies have confirmed the…
Electroencephalogram (EEG)-based emotion decoding can objectively quantify people's emotional state and has broad application prospects in human-computer interaction and early detection of emotional disorders. Recently emerging deep…