Analysis of instantaneous brain interactions contribution to a motor imagery classification task
The purpose of this study is to analyze the contribution of the interactions between electrodes, measured either as correlation or as Jaccard distance, to the classification of two actions in a motor imagery paradigm, namely, left-hand movement and right-hand movement. The analysis is performed in t...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2022-12-01
|
| Series: | Frontiers in Computational Neuroscience |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fncom.2022.990892/full |
| _version_ | 1811177832909373440 |
|---|---|
| author | Jorge Humberto Cristancho Cuervo Jaime F. Delgado Saa Lácides Antonio Ripoll Solano |
| author_facet | Jorge Humberto Cristancho Cuervo Jaime F. Delgado Saa Lácides Antonio Ripoll Solano |
| author_sort | Jorge Humberto Cristancho Cuervo |
| collection | DOAJ |
| description | The purpose of this study is to analyze the contribution of the interactions between electrodes, measured either as correlation or as Jaccard distance, to the classification of two actions in a motor imagery paradigm, namely, left-hand movement and right-hand movement. The analysis is performed in two classifier models, namely, a static (linear discriminant analysis, LDA) model and a dynamic (hidden conditional random field, HCRF) model. The impact of using the sliding window technique (SWT) in the static and dynamic models is also analyzed. The study proved that their combination with temporal features provides significant information to improve the classification in a two-class motor imagery task for LDA (average accuracy: 0.7192 no additional features, 0.7617 by adding correlation, 0.7606 by adding Jaccard distance; p < 0.001) and HCRF (average accuracy: 0.7370 no additional features, 0.7764 by adding correlation, 0.7793 by adding Jaccard distance; p < 0.001). Also, we showed that adding interactions between electrodes improves significantly the performance of each classifier, regarding the nature of the interaction measure or the classifier itself. |
| first_indexed | 2024-04-11T06:08:00Z |
| format | Article |
| id | doaj.art-c408fb4dddee40df86686f9975580a61 |
| institution | Directory Open Access Journal |
| issn | 1662-5188 |
| language | English |
| last_indexed | 2024-04-11T06:08:00Z |
| publishDate | 2022-12-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Computational Neuroscience |
| spelling | doaj.art-c408fb4dddee40df86686f9975580a612022-12-22T04:41:27ZengFrontiers Media S.A.Frontiers in Computational Neuroscience1662-51882022-12-011610.3389/fncom.2022.990892990892Analysis of instantaneous brain interactions contribution to a motor imagery classification taskJorge Humberto Cristancho Cuervo0Jaime F. Delgado Saa1Lácides Antonio Ripoll Solano2Biomedical Signal Processing and Artificial Intelligence, Department of Electrical and Electronics Engineering, Universidad del Norte, Barranquilla, ColombiaSciFork SARL, Geneva, SwitzerlandGrupo de Investigación en Telecomunicaciones y Señales, Department of Electrical and Electronics Engineering, Universidad del Norte, Barranquilla, ColombiaThe purpose of this study is to analyze the contribution of the interactions between electrodes, measured either as correlation or as Jaccard distance, to the classification of two actions in a motor imagery paradigm, namely, left-hand movement and right-hand movement. The analysis is performed in two classifier models, namely, a static (linear discriminant analysis, LDA) model and a dynamic (hidden conditional random field, HCRF) model. The impact of using the sliding window technique (SWT) in the static and dynamic models is also analyzed. The study proved that their combination with temporal features provides significant information to improve the classification in a two-class motor imagery task for LDA (average accuracy: 0.7192 no additional features, 0.7617 by adding correlation, 0.7606 by adding Jaccard distance; p < 0.001) and HCRF (average accuracy: 0.7370 no additional features, 0.7764 by adding correlation, 0.7793 by adding Jaccard distance; p < 0.001). Also, we showed that adding interactions between electrodes improves significantly the performance of each classifier, regarding the nature of the interaction measure or the classifier itself.https://www.frontiersin.org/articles/10.3389/fncom.2022.990892/fullbrain interactionscorrelationJaccard distanceclassifierstatic modeldynamic model |
| spellingShingle | Jorge Humberto Cristancho Cuervo Jaime F. Delgado Saa Lácides Antonio Ripoll Solano Analysis of instantaneous brain interactions contribution to a motor imagery classification task Frontiers in Computational Neuroscience brain interactions correlation Jaccard distance classifier static model dynamic model |
| title | Analysis of instantaneous brain interactions contribution to a motor imagery classification task |
| title_full | Analysis of instantaneous brain interactions contribution to a motor imagery classification task |
| title_fullStr | Analysis of instantaneous brain interactions contribution to a motor imagery classification task |
| title_full_unstemmed | Analysis of instantaneous brain interactions contribution to a motor imagery classification task |
| title_short | Analysis of instantaneous brain interactions contribution to a motor imagery classification task |
| title_sort | analysis of instantaneous brain interactions contribution to a motor imagery classification task |
| topic | brain interactions correlation Jaccard distance classifier static model dynamic model |
| url | https://www.frontiersin.org/articles/10.3389/fncom.2022.990892/full |
| work_keys_str_mv | AT jorgehumbertocristanchocuervo analysisofinstantaneousbraininteractionscontributiontoamotorimageryclassificationtask AT jaimefdelgadosaa analysisofinstantaneousbraininteractionscontributiontoamotorimageryclassificationtask AT lacidesantonioripollsolano analysisofinstantaneousbraininteractionscontributiontoamotorimageryclassificationtask |