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Filters
A review of channel selection algorithms for EEG signal processing
2015
EURASIP Journal on Advances in Signal Processing
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In addition, different evaluation approaches such as filtering, wrapper, embedded, hybrid, and human-based techniques have been widely used for the evaluation of the selected subset of channels. ...
Signal processing tools such as time-domain analysis, power spectral estimation, and wavelet transform have been used for feature extraction and hence for channel selection in most of channel selection ...
[34] , who proposed a channel selection method for motor imagery classification based on the sorting of common spatial pattern (CSP) filter coefficients. ...
doi:10.1186/s13634-015-0251-9
fatcat:vdp2xjlvavczrg4l3rvibuyt5m
Decoding spectro-temporal representation for motor imagery recognition using ECoG-based brain-computer interfaces
2020
Journal of Integrative Neuroscience
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One of the challenges in brain-computer interface systems is obtaining motor imagery recognition from brain activities. ...
Brain-signal decoding robustness and system performance improvement during the motor imagery process are two of the essential issues in brain-computer interface research. ...
Acknowledgment This work was supported by the National Natural Science Foundation of China (Grant No. 61701270, 61701279, 81472159, 81871508 and 61773246)
Conflict of Interest The authors declare no ...
doi:10.31083/j.jin.2020.02.1269
pmid:32706190
fatcat:6z4b43tg4vcmnlvbdhfxrcd7ei
Relevant EEG features for the classification of spontaneous motor-related tasks
2002
Biological cybernetics
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This study empirically demonstrates the benefits of heuristic feature selection methods for EEG-based classification of mental tasks. ...
A few groups around the world have developed brain-computer interfaces (BCI) that rely upon the recognition of motor-related tasks (i.e., imagination of movements) from on-line EEG signals. ...
Thus people can for instance communicate using their brain activity by selecting letters from a virtual keyboard. This alternative communication channel is called a brain-computer interface (BCI) . ...
doi:10.1007/s004220100282
pmid:11908842
fatcat:3ysizdncw5dmbmgag2ly4246te
Enhanced Performance by Time-Frequency-Phase Feature for EEG-Based BCI Systems
2014
The Scientific World Journal
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Support vector machines (SVMs) and extreme learning machines (ELMs) are compared for classification between clench speed and clench force motor imagery using the optimized feature. ...
The time-frequency-phase features are extracted from mu rhythm and beta rhythms, and the features are optimized using three process methods: no-scaled feature using "MIFS" feature selection criterion, ...
Depending on whether the classifier is included in the selection process, feature selection methods can be grouped into "wrapper" methods and "filter" methods [32] . ...
doi:10.1155/2014/420561
pmid:25045733
pmcid:PMC4087262
fatcat:3g3nrh7p3rh3vfjk5bob4djvqe
Filtering techniques for channel selection in motor imagery EEG applications: a survey
2019
Artificial Intelligence Review
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In this paper, we present a survey of recent development in filtering channel selection techniques along with their feature extraction and classification methods for MI-based EEG applications. ...
Different channel selection evaluation algorithms such as filtering, wrapper, and hybrid methods have been used for extracting optimal channel subsets by using predefined criteria. ...
The device that uses brain signals to control and operate the environment is called the Brain Computer Interface (BCI) system (Wolpaw et al. 2002) . ...
doi:10.1007/s10462-019-09694-8
fatcat:lh6col6fozgqrcxauh6dwytiem
A performance based feature selection technique for subject independent MI based BCI
2019
Health Information Science and Systems
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We have proposed a novel computer aided feature selection method to determine the best set of features for distinguishing between motor imagery tasks in lieu of the manual feature selection that has been ...
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 ...
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
EEG Channel Selection Based on Correlation Coefficient for Motor Imagery Classification: A Study on Healthy Subjects and ALS Patient
2018
2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
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The Common Spatial Pattern (CSP) algorithm is effective in extracting discriminative features from EEG data for motor imagery-based Brain-Computer Interface (BCI). ...
Brain-Computer Interface (BCI) provides an alternate channel of interaction for people with severe motor disabilities. ...
Brain-computer interface (BCI) opens a possibility for the patient with server motor disability or neural disorder to communicate and control. ...
doi:10.1109/embc.2018.8512701
pmid:30440791
fatcat:clbmeafx5zexxoh473txbfde64
Differential evolution algorithm as a tool for optimal feature subset selection in motor imagery EEG
2017
Expert systems with applications
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One of the challenges in developing a Brain Computer Interface (BCI) is dealing with the high dimensionality of the data when extracting features from EEG signals. ...
Different feature selection algorithms have been proposed to overcome this problem but most of them involve complex transformed features, which require high computation and also result in increasing size ...
The hybrid feature selection method uses a combination of filtering and wrapper techniques to select features. Some of the algorithms used for EEG data are discussed in the next subsection. ...
doi:10.1016/j.eswa.2017.07.033
fatcat:7o3cqteigrafxk7xn3flkzlt7i
Translational Algorithms: The Heart of a Brain Computer Interface
[chapter]
2014
Intelligent Systems Reference Library
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commands to the outside world for control purposes; this is popularly known as the brain computer interface. ...
This chapter discusses these computational strategies and translational techniques including dimensionality reduction, feature extraction, feature selection, and classification techniques. ...
The authors are thankful to the BCI research community for their support and guidance through their publications and resources on the web. P r e -p r i n t v e r s i o n ...
doi:10.1007/978-3-319-10978-7_4
fatcat:vztl3uozh5c47ggcf3tuvgi5yy
Effect of Feature and Channel Selection on EEG Classification
2006
IEEE Engineering in Medicine and Biology Society. Conference Proceedings
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When applied to a Brain-Computer Interface (BCI) problem, results indicate that improvement in classification accuracy can be achieved by considering the right combination of channels and features. ...
of features, and (iii) selecting individual features from different channels. ...
The authors would like to thank the Department of Medical Informatics, University of Technology, Graz, Austria for providing the data. ...
doi:10.1109/iembs.2006.4397869
fatcat:y7ontafprnc3tlnqkf65u7iy6e
Effect of Feature and Channel Selection on EEG Classification
2006
2006 International Conference of the IEEE Engineering in Medicine and Biology Society
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When applied to a Brain-Computer Interface (BCI) problem, results indicate that improvement in classification accuracy can be achieved by considering the right combination of channels and features. ...
of features, and (iii) selecting individual features from different channels. ...
The authors would like to thank the Department of Medical Informatics, University of Technology, Graz, Austria for providing the data. ...
doi:10.1109/iembs.2006.259833
pmid:17946093
fatcat:n6zkkmm5mbailldl6u33nddiqm
Functional Connectivity and Classification of Actual and Imaginary Motor Movement
2019
International Journal of Engineering and Advanced Technology
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To instruct the brain computer interface signals generated by electrodes of EEG must resemble with actual motor movement. Selection of electrodes placement plays an important role for this purpose. ...
Imaginary Motor movement is an utmost important for the designing of brain computer interface to assist the individual with physically disability. ...
Filter, wrapper and hybrid methods for channel selection are discussed in [10] . ...
doi:10.35940/ijeat.b3257.129219
fatcat:efks4ge3jbawbcqfspx5mrgrey
End-to-end learnable EEG channel selection for deep neural networks with Gumbel-softmax
[article]
2021
arXiv
pre-print
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2021 pre-print arXiv:2102.09050v2 fatcat:nhgx4h6xnjantevfxllutfsqg4Other Versions
This generic approach is evaluated on two different EEG tasks: motor imagery brain-computer interfaces and auditory attention decoding. ...
Wrapper-based channel selection methods aim to match the channel selection step to the target model, yet they require to re-train the model multiple times on different candidate channel subsets, which ...
ACKNOWLEDGEMENTS We would like to thank Professor Tom Francart, Bernd Accou and Mohammad Jalilpour Monesi for contributing the dataset for the auditory match-mismatch task. ...
arXiv:2102.09050v3
fatcat:gumzhlzehfcuddgppaoz7lzqj4
A Transform-Based Feature Extraction Approach for Motor Imagery Tasks Classification
2015
IEEE Journal of Translational Engineering in Health and Medicine
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In this paper, we present a new motor imagery classification method in the context of electroencephalography (EEG)-based brain-computer interface (BCI). ...
Our method came second with an average accuracy of 81.38%. INDEX TERMS Brain-computer interface, channel selection, feature extraction, linear prediction, orthogonal transform. ...
Algorithms for channel selection can be divided into two main categories: Filter-based and wrapper-based. ...
doi:10.1109/jtehm.2015.2485261
pmid:27170898
pmcid:PMC4861551
fatcat:jv4wsh7acfcszbdjhleftc6tzu
EEG sensorimotor rhythms' variation and functional connectivity measures during motor imagery: linear relations and classification approaches
2017
PeerJ
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These alterations have been used in the field of brain-computer interfaces (BCI), in an attempt to assign distinct MI tasks to commands of such a system. ...
Moreover, the number of features for the graph method was considerably larger. ...
For both bands, accuracy rates were slightly better using FCM elements as features for the FS approaches (2) and (3) (i.e., using the wrapper combined with the Pearson and Fisher filters respectively ...
doi:10.7717/peerj.3983
pmid:29134143
pmcid:PMC5681853
fatcat:wxgwa5zeujarbof6o6w7xkjscu
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