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Signal processing techniques for motor imagery brain computer interface: A review

Swati Aggarwal, Nupur Chugh
2019 Array  
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

Li-Wei Ko, Yi-Chen Lu, Humberto Bustince, Yu-Cheng Chang, Yang Chang, Javier Ferandez, Yu-Kai Wang, Jose Antonio Sanz, Gracaliz Pereira Dimuro, Chin-Teng Lin
2019 IEEE Computational Intelligence Magazine  
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

Karel Roots, Yar Muhammad, Naveed Muhammad
2020 Computers  
A major challenge in electroencephalogram (EEG)-based BCI development and research is the cross-subject classification of motor imagery data.  ...  Braincomputer 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

Bin Gu, Minpeng Xu, Lichao Xu, Long Chen, Yufeng Ke, Kun Wang, Jiabei Tang, Dong Ming
2021 Frontiers in Neuroscience  
(EEG)-based cBCI which had six instructions related to six different motor imagery tasks (MI-cBCI), respectively.  ...  ObjectiveCollaborative braincomputer 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

D Hari Krishna, I A.Pasha, T Satya Savithri
2018 International Journal of Engineering & Technology  
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

Shang-Lin Wu, Yu-Ting Liu, Kuang-Pen Chou, Yang-Yin Lin, Jie Lu, Guangquan Zhang, Chun-Hsiang Chuang, Wen-Chieh Lin, Chin-Teng Lin
2016 2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)  
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

Md. A. Mannan Joadder, Joshua J. Myszewski, Mohammad H. Rahman, Inga Wang
2019 Health Information Science and Systems  
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]

Marie-Constance Corsi, Mario Chavez, Denis Schwartz, Laurent Hugueville, Ankit N. Khambhati, Danielle S. Bassett, Fabrizio De Vico Fallani
2018 arXiv   pre-print
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

Wojciech Samek, Alexander Binder, Klaus-Robert Muller
2013 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)  
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

Chun-Hsiang Chuang, Li-Wei Ko, Yuan-Pin Lin, Tzyy-Ping Jung, Chin-Teng Lin
2014 IEEE transactions on neural systems and rehabilitation engineering  
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

Yangyang Miao, Jing Jin, Ian Daly, Cili Zuo, Xingyu Wang, Andrzej Cichocki, Tzyy-Ping Jung
2021 IEEE transactions on neural systems and rehabilitation engineering  
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]

Anton Nijholt, Hayrettin Gürkök
2013 Lecture Notes in Computer Science  
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]

Foroogh Shamsi, Ali Haddad, Laleh Najafizadeh
2020 bioRxiv   pre-print
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

Kaido Värbu, Naveed Muhammad, Yar Muhammad
2022 Sensors  
An electroencephalography (EEG)-based braincomputer 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

Yunfa Fu, Baolei Xu, Lili Pei, Hongyi Li
2011 2011 5th International Conference on Bioinformatics and Biomedical Engineering  
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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