Results 261 to 270 of about 928,321 (341)
A wireless, scalable and modular EEG sensor network platform for unobtrusive brain recordings
Ding R +4 more
europepmc +1 more source
Comparative Analysis of Wearable A-Mode Ultrasound and sEMG for Muscle-Computer Interface
IEEE Transactions on Biomedical Engineering, 2019 Objective: While surface electromyography (sEMG) is still dominant in the field of muscle-computer interface, ultrasound (US) sensing has been regarded as a promising alternative to sEMG, owing to its ability to precisely monitor muscle deformations ...
Xingchen Yang, Jipeng Yan, Honghai Liu
semanticscholar +5 more sources
Medical & Biological Engineering & Computing, 2019 Improper selection of the number and the amplitude of noise channels in noise-assisted multivariate empirical mode decomposition (NA-MEMD) would induce mode mixing and leakage in the obtained intrinsic mode functions (IMF), which would degrade the performance in applications like brain-computer interface (BCI) systems based on motor imagery.
Yang Zheng, Guanghua Xu
semanticscholar +5 more sources
Empirical mode decomposition of EEG signals for brain computer interface
SoutheastCon 2017, 2017 Motor imagery (MI) based brain-computer interface (BCI) systems show potential applications in neural rehabilitation. In MI-BCI systems, the brain signals from movement imagination, without actual movement of limbs, can be acquired, processed and characterized to translate into actionable signals that can be used to activate external devices.
MD Erfanul Alam, Biswanath Samanta
semanticscholar +4 more sources
Journal of Neuroscience Methods, 2010 This paper presents an empirical mode decomposition (EMD) and refined generalized zero crossing (rGZC) approach to achieve frequency recognition in steady-stated visual evoked potential (SSVEP)-based brain computer interfaces (BCIs). Six light emitting diode (LED) flickers with high flickering rates (30, 31, 32, 33, 34, and 35 Hz) functioned as visual ...
Chi-Hsun Wu +9 more
semanticscholar +5 more sources
5th ISSNIP-IEEE Biosignals and Biorobotics Conference (2014): Biosignals and Robotics for Better and Safer Living (BRC), 2014 One of the most promising methods to assist amputated or paralyzed patients in the control of prosthetic devices is the use of a brain computer interface (BCI). The use of a BCI allows the communication between the brain and the prosthetic device through signal processing protocols. However, due to the noisy nature of the brain signal, available signal
Luis Carlos Sarmiento +5 more
semanticscholar +4 more sources
, 2009 This paper presents an Empirical mode decomposition (EMD) approach for achieving high-speed frequency-tagged steady state visual evoked potential (SSVEP) brain computer interface (BCI) system. Empirical mode decomposition (EMD) has been demonstrated as a local and fully data-driven technique for the data processing of nonlinear and non-stationary time ...
Chi-Hsun Wu +2 more
semanticscholar +4 more sources
Biomedizinische Technik/Biomedical Engineering, 2006 Transferring a brain-computer interface (BCI) from the laboratory environment into real world applications is directly related to the problem of identifying user intentions from brain signals without any additional information in real time. From the perspective of signal processing, the BCI has to have an uncued or asynchronous design.
Gernot Müller-Putz +3 more
openalex +4 more sources