Robustly Effective Approaches on Motor Imagery-Based Brain Computer Interfaces
Motor Imagery Brain Computer Interfaces (MI-BCIs) are systems that receive the users’ brain activity as an input signal in order to communicate between the brain and the interface or an action to be performed through the detection of the imagination of a movement. Brainwaves’ features are crucial fo...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-04-01
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| Series: | Computers |
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| Online Access: | https://www.mdpi.com/2073-431X/11/5/61 |
| _version_ | 1797500565075263488 |
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| author | Seraphim S. Moumgiakmas George A. Papakostas |
| author_facet | Seraphim S. Moumgiakmas George A. Papakostas |
| author_sort | Seraphim S. Moumgiakmas |
| collection | DOAJ |
| description | Motor Imagery Brain Computer Interfaces (MI-BCIs) are systems that receive the users’ brain activity as an input signal in order to communicate between the brain and the interface or an action to be performed through the detection of the imagination of a movement. Brainwaves’ features are crucial for the performance of the interface to be increased. The robustness of these features must be ensured in order for the effectiveness to remain high in various subjects. The present work consists of a review, which includes scientific publications related to the use of robust feature extraction methods in Motor Imagery from 2017 until today. The research showed that the majority of the works focus on spatial features through Common Spatial Patterns (CSP) methods (44.26%). Based on the combination of accuracy percentages and K-values, which show the effectiveness of each approach, Wavelet Transform (WT) has shown higher robustness than CSP and PSD methods in the majority of the datasets used for comparison and also in the majority of the works included in the present review, although they had a lower usage percentage in the literature (16.65%). The research showed that there was an increase in 2019 of the detection of spatial features to increase the robustness of an approach, but the time-frequency features, or a combination of those, achieve better results with their increase starting from 2019 onwards. Additionally, Wavelet Transforms and their variants, in combination with deep learning, manage to achieve high percentages thus making a method robustly accurate. |
| first_indexed | 2024-03-10T03:05:38Z |
| format | Article |
| id | doaj.art-45566ccbf68f44bf8e3d1d40dd9d9458 |
| institution | Directory Open Access Journal |
| issn | 2073-431X |
| language | English |
| last_indexed | 2024-03-10T03:05:38Z |
| publishDate | 2022-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Computers |
| spelling | doaj.art-45566ccbf68f44bf8e3d1d40dd9d94582023-11-23T10:33:27ZengMDPI AGComputers2073-431X2022-04-011156110.3390/computers11050061Robustly Effective Approaches on Motor Imagery-Based Brain Computer InterfacesSeraphim S. Moumgiakmas0George A. Papakostas1MLV Research Group, Department of Computer Science, International Hellenic University, 65404 Kavala, GreeceMLV Research Group, Department of Computer Science, International Hellenic University, 65404 Kavala, GreeceMotor Imagery Brain Computer Interfaces (MI-BCIs) are systems that receive the users’ brain activity as an input signal in order to communicate between the brain and the interface or an action to be performed through the detection of the imagination of a movement. Brainwaves’ features are crucial for the performance of the interface to be increased. The robustness of these features must be ensured in order for the effectiveness to remain high in various subjects. The present work consists of a review, which includes scientific publications related to the use of robust feature extraction methods in Motor Imagery from 2017 until today. The research showed that the majority of the works focus on spatial features through Common Spatial Patterns (CSP) methods (44.26%). Based on the combination of accuracy percentages and K-values, which show the effectiveness of each approach, Wavelet Transform (WT) has shown higher robustness than CSP and PSD methods in the majority of the datasets used for comparison and also in the majority of the works included in the present review, although they had a lower usage percentage in the literature (16.65%). The research showed that there was an increase in 2019 of the detection of spatial features to increase the robustness of an approach, but the time-frequency features, or a combination of those, achieve better results with their increase starting from 2019 onwards. Additionally, Wavelet Transforms and their variants, in combination with deep learning, manage to achieve high percentages thus making a method robustly accurate.https://www.mdpi.com/2073-431X/11/5/61brain computer interfacemotor imageryrobustfeature extractionEEG |
| spellingShingle | Seraphim S. Moumgiakmas George A. Papakostas Robustly Effective Approaches on Motor Imagery-Based Brain Computer Interfaces Computers brain computer interface motor imagery robust feature extraction EEG |
| title | Robustly Effective Approaches on Motor Imagery-Based Brain Computer Interfaces |
| title_full | Robustly Effective Approaches on Motor Imagery-Based Brain Computer Interfaces |
| title_fullStr | Robustly Effective Approaches on Motor Imagery-Based Brain Computer Interfaces |
| title_full_unstemmed | Robustly Effective Approaches on Motor Imagery-Based Brain Computer Interfaces |
| title_short | Robustly Effective Approaches on Motor Imagery-Based Brain Computer Interfaces |
| title_sort | robustly effective approaches on motor imagery based brain computer interfaces |
| topic | brain computer interface motor imagery robust feature extraction EEG |
| url | https://www.mdpi.com/2073-431X/11/5/61 |
| work_keys_str_mv | AT seraphimsmoumgiakmas robustlyeffectiveapproachesonmotorimagerybasedbraincomputerinterfaces AT georgeapapakostas robustlyeffectiveapproachesonmotorimagerybasedbraincomputerinterfaces |