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EEG oscillatory patterns and classification of sequential compound limb motor imagery

Weibo Yi, Shuang Qiu, Kun Wang, Hongzhi Qi, Feng He, Peng Zhou, Lixin Zhang, Dong Ming
2016 Journal of NeuroEngineering and Rehabilitation  
The goal of the present study was to verify the feasibility of application of motor sequences involving multiple limbs to brain-computer interface (BCI) systems based on motor imagery (MI).  ...  Conclusions: This work implies that motor sequences involving multiple limbs can be utilized to build a multimodal classification paradigm in MI-based BCI systems, and that prior movement imagination can  ...  Then, the instantaneous phases of the signals could be extracted by Hilbert transformation, and the difference of instantaneous phases corresponding to two different signals was defined as Δφ(t) = φ x  ... 
doi:10.1186/s12984-016-0119-8 pmid:26822435 pmcid:PMC4731999 fatcat:nulclkhu7bcqfa4eik7rxurmca

Evaluation of feature extraction methods for EEG-based brain–computer interfaces in terms of robustness to slight changes in electrode locations

Sun-Ae Park, Han-Jeong Hwang, Jeong-Hwan Lim, Jong-Ho Choi, Hyun-Kyo Jung, Chang-Hwan Im
2013 Medical and Biological Engineering and Computing  
Our results suggest that CC can be used as a promising feature extraction method in motor-imagery-based BCI studies, since it provides high classification accuracy along with being little affected by slight  ...  The performance of four different feature extraction methods [power spectral density (PSD), phase locking value (PLV), a combination of PSD and PLV, and cross-correlation (CC)] were evaluated using five-fold  ...  One of the most widely studied EEG-based BCI paradigms is the motor-imagery-based BCI.  ... 
doi:10.1007/s11517-012-1026-1 pmid:23325145 fatcat:pthqjdegofdhpbrtjvlefekpie

Phase Synchrony Rate for the Recognition of Motor Imagery in Brain-Computer Interface

Le Song, Evian Gordon, Elly Gysels
2005 Neural Information Processing Systems  
Motor imagery attenuates EEG µ and β rhythms over sensorimotor cortices.  ...  Statistical nonparametric tests show that SRs contain significant differences between 2 types of motor imageries.  ...  The instantaneous phase difference ψ ij (t) can be computed using either wavelet analysis or Hilbert transformation.  ... 
dblp:conf/nips/SongGG05 fatcat:5asusg6zsvdwnovfijrh6hiem4

Amplitude and phase coupling measures for feature extraction in an EEG-based brain–computer interface

Qingguo Wei, Yijun Wang, Xiaorong Gao, Shangkai Gao
2007 Journal of Neural Engineering  
Most of the feature extraction methods in existing brain-computer interfaces (BCIs) are based on the dynamic behavior of separate signals, without using the coupling information between different brain  ...  In this paper, amplitude and phase coupling measures, quantified by a nonlinear regressive coefficient and phase locking value respectively, were used for feature extraction.  ...  Also, Wang et al (2006) have applied the phase synchrony measurement in motor cortex for classifying single-trial EEG during motor imagery.  ... 
doi:10.1088/1741-2560/4/2/012 pmid:17409486 fatcat:3mxloyd4vngqfo5sduwkywjmbi

Nonlinear and nonstationary framework for feature extraction and classification of motor imagery

D. Trad, T. Al-ani, E. Monacelli, M. Jemni
2011 2011 IEEE International Conference on Rehabilitation Robotics  
The classification of motor imagery was performed by using two classifiers, Linear Discriminant Analysis (LDA) and Hidden Markov Models (HMMs).  ...  In this work we investigate a nonlinear approach for feature extraction of Electroencephalogram (EEG) signals in order to classify motor imagery for Brain Computer Interface (BCI).  ...  Classification Two different classifiers were implemented to classify the different motor imagery (imagination of right or left hand movement): 1) LDC: The LDC is a linear discriminant classifier based  ... 
doi:10.1109/icorr.2011.5975488 pmid:22275685 fatcat:vwxzbnkrwbcxtoyc6ujqsg5v6y

Imagined Hand Clenching Force and Speed Modulate Brain Activity and Are Classified by NIRS Combined With EEG

Yunfa Fu, Xin Xiong, Changhao Jiang, Baolei Xu, Yongcheng Li, Hongyi Li
2017 IEEE transactions on neural systems and rehabilitation engineering  
The instantaneous phase, frequency, and amplitude feature of the EEG were calculated by Hilbert transform; HbO and the difference between HbO and Hb concentrations were extracted.  ...  of these imagined motor parameters by NIRS-EEG were explored.  ...  To identify different levels of hand clenching force and speed motor imageries, the instantaneous amplitude (IA), instantaneous phase (IP), and instantaneous frequency (IF) of EEG signals were calculated  ... 
doi:10.1109/tnsre.2016.2627809 pmid:27849544 fatcat:wwsna6ogwfe2zlfnymaldbdplq

Graph Convolutional Neural Networks for analysis of EEG signals, BCI application [article]

Mirfarid Musavian Ghazani, Anh Huy Phan
2020 arXiv   pre-print
With the advances and achievements in the field of deep learning in problem solving with using only raw data, few attempts has been carried in recent years, to apply deep learning to tackle EEG among other  ...  signals belonging to different classes in an end to end manner on raw signals without the need to perform extensive feature engineering.  ...  , on the performance of the Motor Imagery EEG signals classification.  ... 
arXiv:2006.14540v1 fatcat:cshs66esdre3bhjjjtrllhgd4a

Decoding Finger Tapping with the Affected Hand in Chronic Stroke Patients During Motor Imagery and Execution

Minji Lee, Ji-Hoon Jeong, Yun-Hee Kim, Seong-Whan Lee
2021 IEEE transactions on neural systems and rehabilitation engineering  
In stroke rehabilitation, motor imagery based on a brain-computer interface is an extremely useful method to control an external device and utilize neurofeedback.  ...  However, there was no significant difference between the accuracies of motor execution and motor imagery.  ...  In this study, we propose a classification framework using a voting module during motor execution and motor imagery with EEG signals.  ... 
doi:10.1109/tnsre.2021.3087506 pmid:34101595 fatcat:bfvsmaidczevddo6mom4ho3qse

Robust electroencephalogram phase estimation with applications in brain-computer interface systems

Esmaeil Seraj, Reza Sameni
2017 Physiological Measurement  
The proposed method for EEG phase calculation is very generic and may be applied to other EEG phase-based studies.  ...  Based on recent theoretical findings in this area, it is shown that some of the phase variations-previously associated to the brain response-are systematic sideeffects of the methods used for EEG phase  ...  Since the primary cortical regions involved in the task of motor imagery are the supplementary motor area (SMA) and the primary motor cortex area (M1), electrodes FCz, C3, and C4 are chosen for this study  ... 
doi:10.1088/1361-6579/aa5bba pmid:28140332 fatcat:me7gldfhpjglxpqqq726di5qmy

Data-Driven Transducer Design and Identification for Internally-Paced Motor Brain Computer Interfaces: A Review

Marie-Caroline Schaeffer, Tetiana Aksenova
2018 Frontiers in Neuroscience  
Motor BCIs aim to permit severely motor-impaired users to regain limb mobility by controlling orthoses or prostheses.  ...  They offer the potential to improve the quality of life of motor-impaired patients.  ...  In addition, the relevance of alternative phase-based features, such as the instantaneous or mean phase difference between two channels, has been investigated in the case of EEG signal in Hamner et al  ... 
doi:10.3389/fnins.2018.00540 pmid:30158847 fatcat:zpm6e55gvfedvhf26xyda4peti

Functional connectivity ensemble method to enhance BCI performance (FUCONE) [article]

Marie-Constance Corsi, Sylvain Chevallier, Fabrizio De Vico Fallani, Florian Yger
2022 arXiv   pre-print
states, such as motor imagery.  ...  Using a meta-analysis to aggregate results across datasets, FUCONE performed significantly better than all state-of-the-art methods.  ...  For that purpose, we based our selection on the FgMDM classification scores, which corresponds in our case to the state-of-the-art classification algorithm in motor imagery-based BCI [49, 50] .  ... 
arXiv:2111.03122v2 fatcat:7wnthfmrm5derbzmygusj4u5ve

Development of speech prostheses: current status and recent advances

Jonathan S Brumberg, Frank H Guenther
2010 Expert Review of Medical Devices  
Some BCI communication systems take the form of letter and word selection paradigms; common methods include letter selection by electroencephalography (EEG) using slow cortical potentials (SCP) [6-10],  ...  This article outlines many well known methods for restoration of communication by BCI and illustrates the difference between spelling devices and direct speech prediction or speech prosthesis.  ...  An initial phase of cued-stimulus speech motor imagery was used for decoding filter calibration.  ... 
doi:10.1586/erd.10.34 pmid:20822389 pmcid:PMC2953242 fatcat:eztvakjqpff2zgi7cmvjv5fd6e

Analysis of the Relationship Between Motor Imagery and Age-Related Fatigue for CNN Classification of the EEG Data

Xiangyun Li, Xiangyun Li, Peng Chen, Xi Yu, Xi Yu, Ning Jiang, Ning Jiang
2022 Frontiers in Aging Neuroscience  
Finally, the motor imagery EEG data was classified by CNNs, and the accuracy was discussed based on the analysis results.ResultFor the young and elderly, ERD was observed in C3 and C4 channels, and their  ...  elderly are less affected by the level of cognitive fatigue during motor imagery, but the classification accuracy of motor imagery data in the e [...]  ...  Motor imagery and mental fatigue: inter-relationship and eeg based estimation. J. Comput.  ... 
doi:10.3389/fnagi.2022.909571 pmid:35912081 pmcid:PMC9329804 doaj:66f17ab9a51d4ae6a082fbef22c4b31a fatcat:sntnjelvszg5zpelax2pasqvb4

Classification of Motor Imagery BCI Using Multivariate Empirical Mode Decomposition

Cheolsoo Park, David Looney, Naveed ur Rehman, Alireza Ahrabian, Danilo P. Mandic
2013 IEEE transactions on neural systems and rehabilitation engineering  
Index Terms-Brain-computer interface (BCI), electroencephalogram (EEG), empirical mode decomposition, motor imagery paradigm, noise assisted multivariate extensions of empirical mode decomposition (NA-MEMD  ...  imagery BCI.  ...  The phase function, , is differentiated to produce the instantaneous frequency, [19] .  ... 
doi:10.1109/tnsre.2012.2229296 pmid:23204288 fatcat:2ejtmm3zbjb2nloyrvmkbmlw7u

Identification of Visual Imagery by Electroencephalography Based on Empirical Mode Decomposition and an Autoregressive Model

Yunfa Fu, Zhaoyang Li, Anmin Gong, Qian Qian, Lei Su, Lei Zhao, António Dourado
2022 Computational Intelligence and Neuroscience  
The results indicate that multiple VI tasks were separable based on EEG and that the combination of EMD and an AR model used in VI feature extraction was better than an HHT or AR model alone.  ...  The traditional imagery task for brain–computer interfaces (BCIs) consists of motor imagery (MI) in which subjects are instructed to imagine moving certain parts of their body.  ...  in motor task recognition based EEG signals which provided favorable results [34, 35] .  ... 
doi:10.1155/2022/1038901 pmid:35140763 pmcid:PMC8818430 fatcat:wjmgxcid2zaidghjt7kqku6qgu
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