Results 261 to 270 of about 645,094 (319)

Frequency recognition in an SSVEP-based brain computer interface using empirical mode decomposition and refined generalized zero-crossing

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, Hsiang‐Chih Chang, Po‐Lei Lee, Kuen-Shing Li, Jyun-Jie Sie, Chia-Wei Sun, Chia-Yen Yang, Po-Hung Li, Hua-Ting Deng, Kuo‐Kai Shyu   +9 more
openalex   +3 more sources

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

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
openalex   +3 more sources

Batch Mode Active Learning Algorithm Combining with Self-training for Multiclass Brain-computer Interfaces

Journal of Information and Computational Science, 2015
In this paper, an batch mode active learning algorithm combining with the beneflts of self-training for solving the multiclass Brain-computer Interface (BCI) problem, which initially only needs a small set of labeled samples to train classiflers. The algorithm applied active learning to select the most informative samples and self-training to select ...
Minyou Chen
openalex   +2 more sources

Brain computer interface (BCI) with EEG signals for automatic vowel recognition based on articulation mode

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, Pablo Lorenzana, Carlos Julio Cortés, W. J. Arcos, J. A. Bacca, Andrés Tovar   +5 more
openalex   +2 more sources

Assessing Mode-Switching Strategies for Assistive Robotic Manipulators Using a Preliminary Version of the Novel Non-invasive Tongue-Computer Interface

2023 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), 2023
The inductive tongue-computer interface allows individuals with tetraplegia to control assistive devices. However, controlling assistive robotic arms often requires more than 14 different commands, which cannot always fit into a single control layout.
Ana S. Santos Cardoso, Mostafa Mohammadi, Rasmus Leck Kæseler, Mads Jochumsen, Lotte N. S. Andreasen Struijk   +4 more
openalex   +3 more sources

65‐3: Multi‐Mode Fusion Human‐Computer Interface Based on EEG and EOG

SID Symposium Digest of Technical Papers
This paper presents a multi‐mode fusion human‐computer interface integrating Electroencephalogram (EEG) and Electrooculogram (EOG) signals to enhance interaction speed and accuracy. Traditional Steady‐State Visual Evoked Potential (SSVEP)‐based Brain‐Computer Interface (BCI) systems suffer from low refresh rates in liquid crystal displays (LCDs ...
Tong Zou, Minghao Xu, Xiong Zhang
openalex   +2 more sources

Novel use of Empirical Mode Decomposition in single-trial classification of motor imagery for use in brain-computer interfaces

2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2013
This paper presents a novel method, based on multi-channel Empirical Mode Decomposition (EMD), of classifying the electroencephalogram (EEG) recordings of imagined movement by a subject within a brain-computer interfacing (BCI) framework. EMD is a technique that divides any non-linear or non-stationary signal into groups of frequency harmonics, called ...
Simon Davies, Christopher J. James
openalex   +3 more sources

Multimedia Teaching Mode in Colleges and Universities Based on Psychology-Based Human-Computer Interaction Interface Design

International Journal of Human–Computer Interaction, 2023
Xiu Jun Wang
openalex   +2 more sources

The Performance Of A Novel P300 Brain-Computer Interface Paradigm With Electrical And Vibration Modes

2022 12th International Conference on CYBER Technology in Automation, Control, and Intelligent Systems (CYBER), 2022
Chenxi Chu, Jingjing Luo, Qiang Du, Xiangke Han, Shijie Guo   +4 more
openalex   +2 more sources

[A Feature Extraction Method for Brain Computer Interface Based on Multivariate Empirical Mode Decomposition].

Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi, 2015
This paper presents a feature extraction method based on multivariate empirical mode decomposition (MEMD) combining with the power spectrum feature, and the method aims at the non-stationary electroencephalogram (EEG) or magnetoencephalogram (MEG) signal in brain-computer interface (BCI) system.
Jinjia Wang, Yuan Liu
openalex   +2 more sources