Results 211 to 220 of about 74,310 (255)

Comparative Analysis of Wearable A-Mode Ultrasound and sEMG for Muscle-Computer Interface

IEEE Transactions on Biomedical Engineering, 2020
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. Among different US modalities, A-mode US is more compact and cost-effective for wearable applications against its ...
Xingchen Yang   +2 more
exaly   +3 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
exaly   +2 more sources

A Music Playback System based on Multi-mode Hybrid Brain-computer Interface

2020 IEEE 5th International Conference on Signal and Image Processing (ICSIP), 2020
This paper aims at the problem of designing a music playback system based on multi-mode hybrid Brain-Computer Interface (hBCI), which provides a way of the entertainment for stroke patients to improve the mental living standard. The asynchronous mode by using Alpha waves is applied to the procedure of the music playback system design, which allows ...
Gao Nuo
exaly   +2 more sources

Instantaneous Gaze-Target Detection by Empirical Mode Decomposition: Application to Brain Computer Interface

IFMBE Proceedings, 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, Po-Lei Lee, Lee Po-Lei
exaly   +2 more sources

Quantifying mode mixing and leakage in multivariate empirical mode decomposition and application in motor imagery–based brain-computer interface system

Medical and Biological Engineering and 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, Xu Guanghua
exaly   +3 more sources

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