• An instant-click recording system was designed to obtain momentary affective labels.
• An EEG-based spatiotemporal emotional intensity regression network was proposed.
• The model uses cross-cortical-region attention to fuse global ...

Previous studies in affective computing often use a fixed emotional label to train an emotion classifier with electroencephalography (EEG) from individuals experiencing an affective stimulus. However, EEGs encode emotional dynamics that include ...

Most of the existing motor imagery (MI)-based brain-computer interface (BCI) systems operate in synchronous to the system-generated time slots. But in real-world applications, users want to control the interface asynchronously at their own convenience. ...

Brain-computer interfaces (BCIs) offer a novel method of converting brain activity into valuable data. This study investigates the use of electroencephalogram (EEG) signals for recognizing brainwave signals to record human thoughts. Our research focuses ...

Although e-learning technology provides numerous benefits for educators, enticing students to use e-learning services is a challenge, particularly for the e-learning websites of higher education institutions in Malaysia. E-learning websites of ...

Emotion recognition using multi-modal physiological signals is an emerging field in affective computing that significantly improves performance compared to unimodal approaches. The combination of Electroencephalogram(EEG) and Galvanic Skin Response(GSR) ...

Multimodal emotion recognition based on electroencephalogram (EEG) and compensating physiological signals (e.g., eye tracking) has shown potential in the diagnosis and rehabilitation tracking of depression. Since the multi-channel EEG signals are ...

Sleep staging is significant for the capture of sleep patterns and the assessment of sleep quality. Although previous studies attempted to automatically detect sleep stages and achieved high classification performance, several challenges remain: 1)...

Hybridity in immersive technologies has not been studied for factors that are likely to influence engagement. A noticeable factor is the spatial enclosure that defines where users meet. This involves a mutual object of interest, contents that the users ...

This article investigates the use of human brainwaves for user authentication. We used data collected from 50 volunteers and leveraged the Support Vector Machine (SVM) as a classification algorithm for the case study. User recognition patterns ...

Many recent studies have confirmed that brain activity in the gamma frequency range is associated with many cognitive functions. It has been found that patients with neuropsychiatric disorders such as schizophrenia, Alzheimer's disease, and depression ...

NeuroIS presents a new opportunity for information systems research. Often used neuroscience techniques include brain mapping with the functional magnetic resonance imaging (fMRI) device or eventrelated potential time-domain studies with the ...

Several studies have continuously investigated FFRs using binaural beat (BB) stimulations and their related effects. However, only a few studies have investigated the differences in BB stimulation effects according ...

Since the first recording in 1924, modern developments in technology have enabled human electroencephalogram (EEG) acquisition as a non-invasive process, enabling a multitude of opportunities to learn about human brain dynamics. With the ...

Epilepsy is one of the most common neurological disorders, and there exists a subset of patients with refractory epilepsy that require surgical removal of the epileptogenic foci (EF) area. Studies have shown that high‐frequency oscillations (HFOs) ...

Recently, deep learning-based electroencephalogram (EEG) analysis and decoding have gained widespread attention to monitor a user's clinical condition or identify his/her intention/emotion. Nevertheless, the existing methods mostly model EEG signals ...

Driver distraction is a concern for traffic safety. Most research has been focused on validating or quantifying the relationship between eyes-off-road metrics and driving performance without specifically addressing cognitive aspects of distracted ...

Effective human-multiagent teams will incorporate the cognitive skills of the human with the autonomous capabilities of the multiagent group to maximize task performance. However, producing a seamless fusion requires a greater understanding of the human’s ...

In this paper, we propose a subject-independent learning method for electroencephalogram-based biometrics using the Holo-Hilbert spectral analysis method. We propose a neural network architecture that uses as input the spectral maps constructed ...

• Electroencephalograms were used to measure the workload under a language comprehension task.

Operator's capability for accurately comprehending verbal commands is critically important to maintain the performance of human-machine interaction. It can be evaluated by human mental workload ...

Billions of neurons make up our brains where the emergence of synchronous behavior is one of the most fundamental questions in the field of neuroscience. In a system as complex as the human brain, synchronization of neuronal activity can be useful and ...