IEEE journal of biomedical and health informatics
Mar 6, 2025
Despite numerous studies aimed at improving accuracy, the accurate prediction of epileptic seizures remains a challenge in clinical practice due to the high computational cost, poor real-time performance, and over-reliance on labelled data. To addres...
IEEE journal of biomedical and health informatics
Mar 6, 2025
Accurate control over individual fingers of robotic hands is essential for the progression of human-robot interactions. Accurate prediction of finger forces becomes imperative in this context. The state-of-the-art neural decoders can extract neural s...
IEEE journal of biomedical and health informatics
Mar 6, 2025
EEG signals exhibit non-stationary characteristics, particularly across different subjects, which presents significant challenges in the precise classification of mental workload levels when applying a trained model to new subjects. Domain adaptation...
IEEE journal of biomedical and health informatics
Mar 6, 2025
Effectively learning the temporal dynamics in electroencephalogram (EEG) signals is challenging yet essential for decoding brain activities using brain-computer interfaces (BCIs). Although Transformers are popular for their long-term sequential learn...
IEEE journal of biomedical and health informatics
Mar 6, 2025
High-quality scalp EEG datasets are extremely valuable for motor imagery (MI) analysis. However, due to electrode size and montage, different datasets inevitably experience channel information loss, posing a significant challenge for MI decoding. A 2...
Electroencephalography (EEG) experiments typically generate vast amounts of data due to the high sampling rates and the use of multiple electrodes to capture brain activity. Consequently, storing and transmitting these large datasets is challenging, ...
EEG is the most powerful tool for epilepsy discharge detection in brain. Visual evaluation is hard in long term monitoring EEG data as huge amount of data needs to be inspected. Considering the fast and efficient results from deep learning networks e...
Chronic respiratory diseases affect people worldwide, but conventional diagnostic methods may not be accessible in remote locations far from population centers. Sounds from the human respiratory system have displayed potential in autonomously detecti...
Data fusion, involving the simultaneous integration of signals from multiple sensors, is an emerging field that facilitates more accurate inferences in instrumentation applications. This paper presents a novel fusion methodology for multi-sensor mult...
Electroencephalogram (EEG) signals are time series data containing abundant brain information. However, EEG frequently contains various artifacts, such as electromyographic, electrooculographic, and electrocardiographic. These artifacts can change EE...
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