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This paper examines the patient experience of long-term monitoring through the case of electroencephalograms (EEG), devices used to diagnose neurological conditions by recording brain activity. During such long-term monitoring, patients are admitted to ...

Multimedia sources such as images and audio commonly activate human senses to perceive objects, but limited research has explored the combined effect of these stimuli on predicting semantic object perception. In this study, we compare the performance of ...

The integration of human emotions into multimedia applications shows great potential for enriching user experiences and enhancing engagement across various digital platforms. Unlike traditional methods such as questionnaires, facial expressions, and ...

Visual similarity estimation plays a fundamental role both in human cognition and multimedia information processing as it is the basis for many applications ranging from image search and recommendation to visual content generation. Existing computational ...

Due to the small size of valid samples, multi-source EEG features with high dimensionality can easily cause problems such as overfitting and poor real-time performance of the emotion recognition classifier. Feature selection has been demonstrated as an ...

Emotion recognition based on electroencephalogram (EEG) has garnered increasing attention in recent years due to non-invasiveness and high reliability of EEG measurements. Despite the promising performance achieved by numerous existing methods, several ...

Understanding human assessment of semantically salient parts of multimedia content is crucial for developing human-centric applications, such as annotation tools, search and recommender systems, and systems able to generate new media matching human ...

Electroencephalogram (EEG)-based Brain-Computer Interfaces (BCIs) have garnered significant interest across various domains, including rehabilitation and robotics. Despite advancements in neural network-based EEG decoding, maintaining performance across ...

In order for an earable to function, it is necessary for the sensor electrodes within the ear to remain in constant contact with the skin. However, body movements tend to disrupt the extent of contact, leading to noisy signals being captured, which are ...

Integrating physiological signals such as electroencephalogram (EEG), with other data such as interview audio, may offer valuable multimodal insights into psychological states or neurological disorders. Recent advancements with Large Language Models (...

The traditional process of EEG-based BCI data collection is cum- bersome and expertise-dependent due to complex hardware in- strumentation, but also because data collection software typically comes as an on-premise solution with features tailored to ...

While traditional earphones primarily offer private audio spaces, so-called "earables" emerged to offer a variety of sensing capabilities. Pioneering platforms like OpenEarable have introduced novel sensing platforms targeted at the ears, incorporating ...

One of the foundational goals of Information Retrieval (IR) is to satisfy searchers' Information Needs (IN). Understanding how INs physically manifest has long been a complex and elusive process. However, recent studies utilising Electroencephalography (...

The promise of Brain-Computer Interfaces (BCIs) is counterbalanced by concerns about vulnerabilities. Recent studies have revealed that EEG-based BCIs are susceptible to security breaches. However, current attack approaches are challenging to execute in ...

Brainwave measuring devices have transitioned from specialized medical tools to user-friendly and economically accessible consumer products. This shift has opened new avenues for pervasive services, with applications spanning brain-computer interfaces (...

Behaviour biometrics are being explored as a viable alternative to overcome the limitations of traditional authentication methods such as passwords and static biometrics. Also, they are being considered as a viable authentication method for IoT devices ...

Emotion recognition from physiological signals is a topic of widespread interest, and researchers continue to develop novel techniques for perceiving emotions. However, the emergence of deep learning has highlighted the need for high-quality emotional ...

Emotion recognition based on electroencephalography (EEG) has attracted significant attention and achieved considerable advances in the fields of affective computing and human-computer interaction. However, most existing studies ignore the coupling and ...

The accuracy of recent deep learning based clinical decision support systems is promising. However, lack of model interpretability remains an obstacle to widespread adoption of artificial intelligence in healthcare. Using sleep as a case study, we ...

Recently, brain-computer interface (BCI) technology has made impressive progress and has been developed for many applications. Thereinto, the BCI system based on rapid serial visual presentation (RSVP) is a promising information detection technology. ...