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Signal processing techniques for motor imagery brain computer interface: A review
2019
Array
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This paper provides a comprehensive review of dominant feature extraction methods and classification algorithms in brain-computer interface for motor imagery tasks. ...
Motor Imagery Brain Computer Interface (MI-BCI) provides a non-muscular channel for communication to those who are suffering from neuronal disorders. ...
[32] competition 75.11%, Subject-3 57.76% Classification of Motor Imagery for Ear-EEG based Brain-Computer CSP RLDA BCI-III 74.28% Interface [70] competition A Deep Learning Approach for Motor Imagery ...
doi:10.1016/j.array.2019.100003
fatcat:tlkzqreshzgfpeusxub3f5h4bq
Multimodal Fuzzy Fusion for Enhancing the Motor-Imagery-Based Brain Computer Interface
2019
IEEE Computational Intelligence Magazine
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Motor imagery-based brain-computer interfaces are popular because they avoid unnecessary external stimuli. ...
Subsequently, we proposed a novel classification framework called the multimodal fuzzy fusion-based brain-computer interface system. ...
INTRODUCTION Brain-computer interfaces (BCIs) are a method of communication between the human brain and an external device [1] . ...
doi:10.1109/mci.2018.2881647
fatcat:d3qu6lhzi5ggreydwggi7ofbkq
Fusion Convolutional Neural Network for Cross-Subject EEG Motor Imagery Classification
2020
Computers
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A major challenge in electroencephalogram (EEG)-based BCI development and research is the cross-subject classification of motor imagery data. ...
Brain–computer interfaces (BCIs) can help people with limited motor abilities to interact with their environment without external assistance. ...
In this work, we proposed a novel feature fusion-based multi-branch 2D convolutional neural network, termed EEGNet Fusion, for cross-subject EEG motor imagery classification. ...
doi:10.3390/computers9030072
fatcat:7ksnx6jo5jff3jkeorof6r7r3i
Optimization of Task Allocation for Collaborative Brain–Computer Interface Based on Motor Imagery
2021
Frontiers in Neuroscience
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(EEG)-based cBCI which had six instructions related to six different motor imagery tasks (MI-cBCI), respectively. ...
ObjectiveCollaborative brain–computer interfaces (cBCIs) can make the BCI output more credible by jointly decoding concurrent brain signals from multiple collaborators. ...
INTRODUCTION Brain-computer interface (BCI) systems could use human brain signals for the direct control of external devices (Wang and Jung, 2011; Jiang et al., 2018) . ...
doi:10.3389/fnins.2021.683784
pmid:34276292
pmcid:PMC8282908
fatcat:k72khepdpvcfnknvvclnbzmapa
Multiclass classification of motor imagery EEG signals using ensemble classifiers & cross-correlation
2018
International Journal of Engineering & Technology
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To communicate without any muscle movement and purely based on brain signal has been the goal of Brain computer interfacing (BCI). ...
As the similarity measurement was binary in nature, one versus rest (OVR) approach was used for multi class classification. Random subset of features was used to train the ensemble of classifiers. ...
Introduction Motor imagery (MI) or imagery motor activity is a type of mental task used in the brain computer interfacing (BCI) system where in EEG activity will be different for activities before and ...
doi:10.14419/ijet.v7i2.6.10144
fatcat:2wlnvj6qxzhhpeutrxlpn6qovu
A motor imagery based brain-computer interface system via swarm-optimized fuzzy integral and its application
2016
2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)
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Index terms─Brain-computer interface (BCI), Electroencephalography (EEG), Motor imagery (MI), Fuzzy integral, Particle swarm optimization (PSO) 4 I. ...
A brain-computer interface (BCI) system using electroencephalography (EEG) signals provides a convenient means of communication between the human brain and a computer. ...
Jyh-Yeong Chang and all of the members of the Brain Research Center, National Chiao Tung University, Taiwan. ...
doi:10.1109/fuzz-ieee.2016.7738007
dblp:conf/fuzzIEEE/WuLCLLZCLL16
fatcat:licixaqzqbcrflrjklkfmr5s2e
A performance based feature selection technique for subject independent MI based BCI
2019
Health Information Science and Systems
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Significant research has been conducted in the field of brain computer interface (BCI) algorithm development, however, many of the resulting algorithms are both complex, and specific to a particular user ...
The methods used were a novel performance based additive feature fusion algorithm working in conjunction with machine learning in order to classify the motor imagery signals into particular states. ...
These results will assist researchers to determine the best feature extraction method for the classification of subject independent motor imagery based brain computer interface systems. ...
doi:10.1007/s13755-019-0076-2
pmid:31428313
pmcid:PMC6684676
fatcat:jcqcpllphrgz5in4rslhf6admi
Integrating EEG and MEG signals to improve motor imagery classification in brain-computer interfaces
[article]
2018
arXiv
pre-print
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imagery-based brain-computer interfaces (BCIs). ...
We propose a fusion approach that combines features from simultaneously recorded electroencephalographic (EEG) and magnetoencephalographic (MEG) signals to improve classification performances in motor ...
Introduction Brain-computer interfaces (BCIs) exploit the ability of subjects to modulate their brain activity through intentional mental effort, such as in motor imagery (MI). ...
arXiv:1711.07258v2
fatcat:jagr3rkrkbf3pe7u6yunafn6hm
Multiple Kernel Learning for Brain-Computer Interfacing
2013
2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
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2013 pre-print arXiv:1310.6067v1 fatcat:hjgj5itpvja7bfahwtcqazdkce
Combining information from different sources is a common way to improve classification accuracy in Brain-Computer Interfacing (BCI). ...
MKL has been widely used for feature fusion in computer vision and allows to simultaneously learn the classifier and the optimal weighting. ...
Furthermore we plan to investigate the impact of the kernel on classification. As in computer vision we expect that Brain-Computer Interfacing may profit from using non-linear classifiers. ...
doi:10.1109/embc.2013.6611181
pmid:24111368
dblp:conf/embc/SamekBM13
fatcat:n3nwqiqavvd5lpequaxkyfag7m
Independent Component Ensemble of EEG for Brain–Computer Interface
2014
IEEE transactions on neural systems and rehabilitation engineering
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However, various technical problems arise in the building of an online brain-computer interface (BCI). ...
Index Terms-Brain-computer interface (BCI), independent component analysis (ICA), multiple classifier system. ...
Independent Component Ensemble of EEG for Brain-Computer Interface iological indicator of human behaviors. ...
doi:10.1109/tnsre.2013.2293139
pmid:24608683
fatcat:6nk3gprzpjhp7cula62ajrppv4
Learning Common Time-Frequency-Spatial Patterns for Motor Imagery Classification
2021
IEEE transactions on neural systems and rehabilitation engineering
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The common spatial patterns (CSP) algorithm is the most popular spatial filtering method applied to extract electroencephalogram (EEG) features for motor imagery (MI) based brain-computer interface (BCI ...
The effectiveness of the CSP algorithm depends on optimal selection of the frequency band and time window from the EEG. ...
The final decision output is based on the results from multi classifiers to reduce the risk of misclassification. ...
doi:10.1109/tnsre.2021.3071140
pmid:33819158
fatcat:5muu5x75xvefjfol4cdebbpw2e
Multi-Brain Games: Cooperation and Competition
[chapter]
2013
Lecture Notes in Computer Science
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We survey research on multi-user brain-computer interfacing applications and look in particular at 'multi-brain games'. ...
Existing research games are mentioned, but the emphasis is on surveying BCI research that will provide ideas for future multi-brain BCI games. ...
Maybe this multi-brain computer interfacing can lead to more reliable decisions and certainly it can lead to new and interesting applications of BCI. ...
doi:10.1007/978-3-642-39188-0_70
fatcat:yqxa3yj75jhtpffpnw55xn66ny
Early Classification of Motor Tasks Using Dynamic Functional Connectivity Graphs from EEG
[article]
2020
bioRxiv
pre-print
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Objective: Classification of electroencephalography (EEG) signals with high accuracy using short recording intervals has been a challenging problem in developing brain computer interfaces (BCIs). ...
Approach: The proposed approach is based on the concept that the brain functions in a dynamic manner, and utilizes dynamic functional connectivity graphs. ...
The authors would also like to thank Jennifer Huang for her help in EEG data collection. ...
doi:10.1101/2020.08.12.244921
fatcat:pxf4szq56zfhlm2v5rwjp35ela
Past, Present, and Future of EEG-Based BCI Applications
2022
Sensors
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An electroencephalography (EEG)-based brain–computer interface (BCI) is a system that provides a pathway between the brain and external devices by interpreting EEG. ...
The objective of this review is to give a systematic overview of the literature on EEG-based BCI applications from the period of 2009 until 2019. ...
et al. 2020 Instrumentation for motor imagery-based brain computer interfaces relying on dry electrodes: A functional analysis
Table A1 . ...
doi:10.3390/s22093331
pmid:35591021
pmcid:PMC9101004
fatcat:gn6bt4uqavenzbu3nkt32de42m
Reactive Rhythm Activities and Offline Classification of Imagined Speeds of Finger Movements
2011
2011 5th International Conference on Bioinformatics and Biomedical Engineering
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Classification of imagined movement speeds with high accuracy based on EEG is also possible through improving methods in the paper. ...
The study may provide a strategy to realize fine control of robots by brain-controlled robot interface. ...
Lun Zhao, Yuxuan Wang, Yongcheng Li for helpful discussions. Also the authors would like to thank Lijie Dang and Tongran Liu for assistance in acquiring the experiment data. ...
doi:10.1109/icbbe.2011.5780247
fatcat:2judabposvdm5o7uzyhuzcze7i
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