Brain Computer Interface-Based Signal Processing Techniques for Feature Extraction and Classification of Motor Imagery Using EEG: A Literature Review
A communication path for people having severe neural disorders is provided by Brain Computer Interaction. The Brain–Computer Interface in an electroencephalogram is an important and challenging one for managing non-stationary EEG signals. EEG signals are more vulnerable to noise and artifacts. The M...
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| Published in | Biomedical materials & devices (New York, N.Y.) Vol. 2; no. 2; pp. 601 - 613 |
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| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
New York
Springer US
01.09.2024
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| Subjects | |
| Online Access | Get full text |
| ISSN | 2731-4812 2731-4820 2731-4820 |
| DOI | 10.1007/s44174-023-00082-z |
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| Abstract | A communication path for people having severe neural disorders is provided by Brain Computer Interaction. The Brain–Computer Interface in an electroencephalogram is an important and challenging one for managing non-stationary EEG signals. EEG signals are more vulnerable to noise and artifacts. The Motor Imagery-based Brain–Computer Interface is used as a communication channel for people with neural disorders who have no muscular activity. For a well-established and accurate BCI system, two important steps have been used in MI-BCI, such as feature extraction and feature classification. Spectral methods and spatial methods are used for the feature extraction methods. The classifiers translate the features into the device commands. Linear Discriminant Analysis is the most widely used classification algorithm. So far, Support Vector Machine has been used as a classification method. In recent studies, Deep Neural Networks and Convolutional Neural Networks have been used. In this study, the feature extraction approaches as well as the signal classification methods for the motor imagery brain computer interface are thoroughly reviewed and presented. |
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| AbstractList | A communication path for people having severe neural disorders is provided by Brain Computer Interaction. The Brain–Computer Interface in an electroencephalogram is an important and challenging one for managing non-stationary EEG signals. EEG signals are more vulnerable to noise and artifacts. The Motor Imagery-based Brain–Computer Interface is used as a communication channel for people with neural disorders who have no muscular activity. For a well-established and accurate BCI system, two important steps have been used in MI-BCI, such as feature extraction and feature classification. Spectral methods and spatial methods are used for the feature extraction methods. The classifiers translate the features into the device commands. Linear Discriminant Analysis is the most widely used classification algorithm. So far, Support Vector Machine has been used as a classification method. In recent studies, Deep Neural Networks and Convolutional Neural Networks have been used. In this study, the feature extraction approaches as well as the signal classification methods for the motor imagery brain computer interface are thoroughly reviewed and presented. |
| Author | Jaipriya, D. Sriharipriya, K. C. |
| Author_xml | – sequence: 1 givenname: D. surname: Jaipriya fullname: Jaipriya, D. organization: Vellore Institute of Technology – sequence: 2 givenname: K. C. surname: Sriharipriya fullname: Sriharipriya, K. C. email: sriharipriya.kc@vit.ac.in organization: Vellore Institute of Technology |
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| CitedBy_id | crossref_primary_10_1007_s11042_024_19885_3 crossref_primary_10_3390_s25051471 crossref_primary_10_1080_10255842_2023_2301421 crossref_primary_10_1007_s12553_024_00822_1 crossref_primary_10_1016_j_chbr_2024_100508 crossref_primary_10_1038_s41598_023_46643_6 crossref_primary_10_1109_ACCESS_2023_3346674 crossref_primary_10_1007_s12021_024_09685_3 crossref_primary_10_1109_ACCESS_2025_3532515 |
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| Keywords | EEG signal extraction BCI classification techniques Brain computer interface Motor imagery |
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| Title | Brain Computer Interface-Based Signal Processing Techniques for Feature Extraction and Classification of Motor Imagery Using EEG: A Literature Review |
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