Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
40040208
Due to inter-subject variability in electroencephalogram (EEG) signals, the generalization ability of many existing brain-computer interface (BCI) models is significantly limited. Although transfer learning (TL) offers a temporary solution, in scenar...
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
40040096
Brain-computer interfaces (BCIs) suffer from limited accuracy due to noisy electroencephalography (EEG) signals. Existing denoising methods often remove artifacts such as eye movement or use techniques such as linear detrending, which inadvertently d...
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
40040056
The supernumerary robotic finger (SRF) can expand human hand abilities to achieve motor augmentation, and integrate with brain computer interface (BCI) to free the occupation of inherent body degrees of freedom. However, the neuro remodeling mechanis...
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
40039972
Electroencephalogram (EEG) is a valuable technique to record brain electrical activity through electrodes placed on the scalp. Analyzing EEG signals contributes to the understanding of neurological conditions and developing brain-computer interface. ...
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
40039912
Reinforcement learning (RL)-based brain machine interfaces (BMIs) provide a promising solution for paralyzed people. Enhancing the decoding performance of RL-based BMIs relies on the design of effective reward signals. Inverse reinforcement learning ...
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
40039684
Brain-Computer Interface (BCI) is a promising neu-rotechnology offering non-muscular control of external devices, such as neuroprostheses and robotic exoskeletons. A new yet under-explored BCI control paradigm is Motion Trajectory Prediction (MTP). W...
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
40039626
Decoding EEG-based, Motor Imagery Brain-Computer Interfaces (MI-BCI) in a subject-independent manner is very challenging due to high dimensionality of the EEG signal, and high inter-subject variability. In recent years, Convolutional neural networks ...
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
40039504
Invasive brain-machine interfaces can help restore function through the control of external devices while the addition of intracortical microstimulation (ICMS) can elicit sensations of touch and help provide further benefits for individuals living wi...
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
40039415
The Steady-State Visually Evoked Potential (SSVEP) is a robust paradigm for developing a high-speed Brain-Computer Interface (BCI). However, one of the challenges of BCI is to face the variability of EEG signals between subjects to reduce or eliminat...
Stroke is a leading cause of disability worldwide, driving the need for advanced rehabilitation strategies. The integration of Artificial Intelligence (AI) into stroke rehabilitation presents significant advancements across the continuum of care, fro...