Related papers: The EAVI EMG/EEG Board: Hybrid physiological sensi…

The affective brain-computer interface is a crucial technology for affective interaction and emotional intelligence, emerging as a significant area of research in the human-computer interaction. Compared to single-type features, multi-type…

Technology advancements made it easy to measure non-invasive and high-quality electroencephalograph (EEG) signals from human's brain. Hence, development of robust and high-performance AI algorithms becomes crucial to properly process the…

Multimodal learning has been a popular area of research, yet integrating electroencephalogram (EEG) data poses unique challenges due to its inherent variability and limited availability. In this paper, we introduce a novel multimodal…

Brain-computer interfaces (BCIs) harness electroencephalographic signals for direct neural control of devices, offering a significant benefit for individuals with motor impairments. Traditional machine learning methods for EEG-based motor…

In the past few years it has been demonstrated that electroencephalography (EEG) can be recorded from inside the ear (in-ear EEG). To open the door to low-profile earpieces as wearable brain-computer interfaces (BCIs), this work presents a…

Brain-computer interfaces (BCIs) offer transformative potential, but decoding neural signals presents significant challenges. The core premise of this paper is built around demonstrating methods to elucidate the underlying low-dimensional…

Brain Computer Interfaces (BCI) provide the opportunity to control external devices using the brain ElectroEncephaloGram (EEG) signals. In this paper we propose two software framework in order to control a 5 degree of freedom robotic and…

To be practical for real-life applications, models for brain-computer interfaces must be easily and quickly deployable on new subjects, effective on affordable scanning hardware, and small enough to run locally on accessible computing…

Advances on signal, image and video generation underly major breakthroughs on generative medical imaging tasks, including Brain Image Synthesis. Still, the extent to which functional Magnetic Ressonance Imaging (fMRI) can be mapped from the…

Since cadiovascular disease (CVD) posts a heavy threat to people's health, long-term electrocardiogram (ECG) monitoring is of great value for the improvement of treatment. To realize remote long-term ECG monitoring, a low-power wireless…

Classification of electroencephalogram (EEG) and electrocorticogram (ECoG) signals obtained during motor imagery (MI) has substantial application potential, including for communication assistance and rehabilitation support for patients with…

With the rapid advancement of technology, different biometric user authentication, and identification systems are emerging. Traditional biometric systems like face, fingerprint, and iris recognition, keystroke dynamics, etc. are prone to…

This study offers a revolutionary strategy to developing wheelchairs based on the Brain-Computer Interface (BCI) that incorporates Artificial Intelligence (AI) using a The device uses electroencephalogram (EEG) data to mimic wheelchair…

Emotion recognition based on Electroencephalography (EEG) has gained significant attention and diversified development in fields such as neural signal processing and affective computing. However, the unique brain anatomy of individuals…

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…

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,…

Electrocardiogram (ECG) signals are often degraded by various noise sources such as baseline wander, motion artifacts, and electromyographic interference, posing a major challenge in clinical settings. This paper presents a lightweight deep…

Single-channel electroencephalogram (EEG) is a cost-effective, comfortable, and non-invasive method for monitoring brain activity, widely adopted by researchers, consumers, and clinicians. The increasing number and proportion of articles on…

Electroencephalography (EEG) is a non-invasive technique for recording brain electrical activity, widely used in brain-computer interface (BCI) and healthcare. Recent EEG foundation models trained on large-scale datasets have shown improved…

The wearable EEG device sector is advancing rapidly, enabling fast and reliable detection of brain activity for investigating brain function and pathology. However, many current EEG systems remain challenging for users with neurological…