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Considering the fact that MI electroencephalogram signals are more non-stationary in frequency domain than in time domain, and the activated intensities of\n                    <jats:italic>\u03b1<\/jats:italic>\n                    band (8\u201313\u00a0Hz) and\n                    <jats:italic>\u03b2<\/jats:italic>\n                    band [13\u201330\u00a0Hz, with\n                    <jats:inline-formula>\n                      <jats:alternatives>\n                        <jats:tex-math>$$\\beta_{1}$$<\/jats:tex-math>\n                        <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                          <mml:msub>\n                            <mml:mi>\u03b2<\/mml:mi>\n                            <mml:mn>1<\/mml:mn>\n                          <\/mml:msub>\n                        <\/mml:math>\n                      <\/jats:alternatives>\n                    <\/jats:inline-formula>\n                    (13\u201321\u00a0Hz) and\n                    <jats:inline-formula>\n                      <jats:alternatives>\n                        <jats:tex-math>$$\\beta_{2}$$<\/jats:tex-math>\n                        <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                          <mml:msub>\n                            <mml:mi>\u03b2<\/mml:mi>\n                            <mml:mn>2<\/mml:mn>\n                          <\/mml:msub>\n                        <\/mml:math>\n                      <\/jats:alternatives>\n                    <\/jats:inline-formula>\n                    (21\u201330\u00a0Hz) included] have considerable differences for different subjects, a dynamic DTF (DDTF) with variable model order and frequency band is proposed to construct the brain functional networks (BFNs), whose information flows and outflows are further calculated as network features and evaluated by support vector machine. Extensive experiments are conducted based on a public BCI competition dataset and a real-world dataset, the highest recognition rate achieve 100% and 86%, respectively. The experimental results suggest that DDTF can reflect the dynamic evolution of BFN, the best subject-based DDTF appears in one of four frequency sub-bands (\n                    <jats:italic>\u03b1<\/jats:italic>\n                    ,\n                    <jats:italic>\u03b2<\/jats:italic>\n                    ,\n                    <jats:inline-formula>\n                      <jats:alternatives>\n                        <jats:tex-math>$$\\beta_{1} ,$$<\/jats:tex-math>\n                        <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                          <mml:mrow>\n                            <mml:msub>\n                              <mml:mi>\u03b2<\/mml:mi>\n                              <mml:mn>1<\/mml:mn>\n                            <\/mml:msub>\n                            <mml:mo>,<\/mml:mo>\n                          <\/mml:mrow>\n                        <\/mml:math>\n                      <\/jats:alternatives>\n                    <\/jats:inline-formula>\n                    <jats:inline-formula>\n                      <jats:alternatives>\n                        <jats:tex-math>$${ }\\beta_{2}$$<\/jats:tex-math>\n                        <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                          <mml:mrow>\n                            <mml:mrow\/>\n                            <mml:msub>\n                              <mml:mi>\u03b2<\/mml:mi>\n                              <mml:mn>2<\/mml:mn>\n                            <\/mml:msub>\n                          <\/mml:mrow>\n                        <\/mml:math>\n                      <\/jats:alternatives>\n                    <\/jats:inline-formula>\n                    ) for discrimination of MI tasks and is much more related to the current and previous states. Besides, DDTF is superior compared to granger causality-based and traditional feature extraction methods, the\n                    <jats:italic>t<\/jats:italic>\n                    -test and Kappa values show its statistical significance and high consistency as well.\n                  <\/jats:p>","DOI":"10.1186\/s40708-022-00154-8","type":"journal-article","created":{"date-parts":[[2022,3,18]],"date-time":"2022-03-18T14:02:53Z","timestamp":1647612173000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["A dynamic directed transfer function for brain functional network-based feature extraction"],"prefix":"10.1186","volume":"9","author":[{"given":"Mingai","family":"Li","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Na","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,3,18]]},"reference":[{"issue":"6","key":"154_CR1","doi-asserted-by":"publisher","first-page":"767","DOI":"10.1016\/S1388-2457(02)00057-3","volume":"113","author":"JR Wolpaw","year":"2002","unstructured":"Wolpaw JR, Birbaumer N, McFarland DJ et al (2002) Brain\u2013computer interfaces for communication and control. 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