{"id":{"repo_id":"vt","oai_identifier":"oai:vtechworks.lib.vt.edu:10919/71371"},"canonical_url":"https://search.dev.ndltd.org/etd/vt/oai:vtechworks.lib.vt.edu:10919/71371","repository":{"repo_id":"vt","name":"Virginia Tech","base_url":"https://vtechworks.lib.vt.edu/oai/request"},"display":{"title":"Comparison and Development of Algorithms for Motor Imagery Classification in EEG- based Brain-Computer Interfaces","abstract":"Brain-computer interfaces are an emerging technology that could provide channels for communication and control to severely disabled people suffering from locked-in syndrome. It has been found that motor imagery can be detected and classified from EEG signals. The motivation of the present work was to compare several algorithms for motor imagery classification in EEG signals as well as to test several novel algorithms. The algorithms tested included the popular method of common spatial patterns (CSP) spatial filtering followed by linear discriminant analysis (LDA) classification of log-variance features (CSP+LDA). A second set of algorithms used classification based on concepts from Riemannian geometry. The basic idea of these methods is that sample spatial covariance matrices (SCMs) of EEG epochs belong to the Riemannian manifold of symmetric positive-definite (SPD) matrices and that the tangent space at any SPD matrix on the manifold is a finite-dimensional Euclidean space. Riemannian classification methods tested included minimum distance to Riemannian mean (MDRM), tangent space LDA (TSLDA), and Fisher geodesic filtering followed by MDRM classification (FGDA). The novel algorithms aimed to combine the CSP method with the Riemannian geometry methods. CSP spatial filtering was performed prior to sample SCM calculation and subsequent classification using Riemannian methods. The novel algorithms were found to improve classification accuracy as well as reduce the computational costs of Riemannian classification methods for binary, synchronous classification on BCI competition IV dataset 2a.","abstract_html":"Brain-computer interfaces are an emerging technology that could provide channels for communication and control to severely disabled people suffering from locked-in syndrome. It has been found that motor imagery can be detected and classified from EEG signals. The motivation of the present work was to compare several algorithms for motor imagery classification in EEG signals as well as to test several novel algorithms. The algorithms tested included the popular method of common spatial patterns (CSP) spatial filtering followed by linear discriminant analysis (LDA) classification of log-variance features (CSP+LDA). A second set of algorithms used classification based on concepts from Riemannian geometry. The basic idea of these methods is that sample spatial covariance matrices (SCMs) of EEG epochs belong to the Riemannian manifold of symmetric positive-definite (SPD) matrices and that the tangent space at any SPD matrix on the manifold is a finite-dimensional Euclidean space. Riemannian classification methods tested included minimum distance to Riemannian mean (MDRM), tangent space LDA (TSLDA), and Fisher geodesic filtering followed by MDRM classification (FGDA). The novel algorithms aimed to combine the CSP method with the Riemannian geometry methods. CSP spatial filtering was performed prior to sample SCM calculation and subsequent classification using Riemannian methods. The novel algorithms were found to improve classification accuracy as well as reduce the computational costs of Riemannian classification methods for binary, synchronous classification on BCI competition IV dataset 2a.","abstract_has_math":false,"creators":["Ailsworth, James William Jr."],"institution":"Virginia Tech","degree_name":"Master of Science","degree_level":"masters","degree_discipline":"Mechanical Engineering","degree_department":"Mechanical Engineering","school":null,"contributors":[],"advisors":[],"committee_chairs":["Kurdila, Andrew J."],"committee_members":["Leonessa, Alexander","Batra, Dhruv"],"year":2016,"date_issued":"2016-06-20","date_published":"2016-06-20","updated_at":"2026-07-22T22:18:46Z","subjects":["Brain-Computer Interface","Motor Imagery","Common Spatial Patterns","Riemannian Geometry"],"languages":[],"rights":["In Copyright"],"rights_urls":["http://rightsstatements.org/vocab/InC/1.0/"],"identifier_entries":[{"key":"dc:identifier.other","label":"Dc Identifier Other","values":["vt_gsexam:8433"],"render_values":[{"text":"vt_gsexam:8433","href":null,"code":true}]}]},"links":{"outbound_url":"http://hdl.handle.net/10919/71371","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.committeechair","label":"Committee Chair","values":["Kurdila, Andrew J."]},{"key":"dc:contributor.committeemember","label":"Committee Member","values":["Leonessa, Alexander","Batra, Dhruv"]},{"key":"dc:contributor.department","label":"Department","values":["Mechanical Engineering"]},{"key":"dc:creator","label":"Author","values":["Ailsworth, James William Jr."]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2016-06-20T17:52:32Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2016-06-20T17:52:32Z"]},{"key":"dc:date.issued","label":"Date","values":["2016-06-20"]},{"key":"dc:publisher","label":"Institution","values":["Virginia Tech"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Mechanical Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["masters"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["Virginia Polytechnic Institute and State University"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Brain-Computer Interface","Motor Imagery","Common Spatial Patterns","Riemannian Geometry"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:rights","label":"Dc Rights","values":["In Copyright"]},{"key":"dc:rights.uri","label":"Rights URI","values":["http://rightsstatements.org/vocab/InC/1.0/"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.other","label":"Dc Identifier Other","values":["vt_gsexam:8433"]},{"key":"dc:identifier.uri","label":"Identifier URI","values":["http://hdl.handle.net/10919/71371"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Brain-computer interfaces are an emerging technology that could provide channels for communication and control to severely disabled people suffering from locked-in syndrome. It has been found that motor imagery can be detected and classified from EEG signals. The motivation of the present work was to compare several algorithms for motor imagery classification in EEG signals as well as to test several novel algorithms. The algorithms tested included the popular method of common spatial patterns (CSP) spatial filtering followed by linear discriminant analysis (LDA) classification of log-variance features (CSP+LDA). A second set of algorithms used classification based on concepts from Riemannian geometry. The basic idea of these methods is that sample spatial covariance matrices (SCMs) of EEG epochs belong to the Riemannian manifold of symmetric positive-definite (SPD) matrices and that the tangent space at any SPD matrix on the manifold is a finite-dimensional Euclidean space. Riemannian classification methods tested included minimum distance to Riemannian mean (MDRM), tangent space LDA (TSLDA), and Fisher geodesic filtering followed by MDRM classification (FGDA). The novel algorithms aimed to combine the CSP method with the Riemannian geometry methods. CSP spatial filtering was performed prior to sample SCM calculation and subsequent classification using Riemannian methods. The novel algorithms were found to improve classification accuracy as well as reduce the computational costs of Riemannian classification methods for binary, synchronous classification on BCI competition IV dataset 2a."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["Master of Science"]},{"key":"dc:format.medium","label":"Dc Format Medium","values":["ETD"]},{"key":"dc:title","label":"Title","values":["Comparison and Development of Algorithms for Motor Imagery Classification in EEG- based Brain-Computer Interfaces"]}]}],"canonical_facts":{"dc:contributor.committeechair":["Kurdila, Andrew J."],"dc:contributor.committeemember":["Leonessa, Alexander","Batra, Dhruv"],"dc:contributor.department":["Mechanical Engineering"],"dc:creator":["Ailsworth, James William Jr."],"dc:date.accessioned":["2016-06-20T17:52:32Z"],"dc:date.available":["2016-06-20T17:52:32Z"],"dc:date.issued":["2016-06-20"],"dc:description.abstract":["Brain-computer interfaces are an emerging technology that could provide channels for communication and control to severely disabled people suffering from locked-in syndrome. It has been found that motor imagery can be detected and classified from EEG signals. The motivation of the present work was to compare several algorithms for motor imagery classification in EEG signals as well as to test several novel algorithms. The algorithms tested included the popular method of common spatial patterns (CSP) spatial filtering followed by linear discriminant analysis (LDA) classification of log-variance features (CSP+LDA). A second set of algorithms used classification based on concepts from Riemannian geometry. The basic idea of these methods is that sample spatial covariance matrices (SCMs) of EEG epochs belong to the Riemannian manifold of symmetric positive-definite (SPD) matrices and that the tangent space at any SPD matrix on the manifold is a finite-dimensional Euclidean space. Riemannian classification methods tested included minimum distance to Riemannian mean (MDRM), tangent space LDA (TSLDA), and Fisher geodesic filtering followed by MDRM classification (FGDA). The novel algorithms aimed to combine the CSP method with the Riemannian geometry methods. CSP spatial filtering was performed prior to sample SCM calculation and subsequent classification using Riemannian methods. 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