{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,19]],"date-time":"2026-08-19T17:58:37Z","timestamp":1787162317267,"version":"build-2736575974"},"reference-count":64,"publisher":"IOP Publishing","issue":"4","license":[{"start":{"date-parts":[[2020,8,24]],"date-time":"2020-08-24T00:00:00Z","timestamp":1598227200000},"content-version":"vor","delay-in-days":23,"URL":"https:\/\/publishingsupport.iopscience.iop.org\/iop-standard\/v1"},{"start":{"date-parts":[[2020,8,24]],"date-time":"2020-08-24T00:00:00Z","timestamp":1598227200000},"content-version":"tdm","delay-in-days":23,"URL":"https:\/\/iopscience.iop.org\/info\/page\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100003399","name":"Science and Technology Commission of Shanghai Municipality","doi-asserted-by":"crossref","award":["2018SHZDZX03"],"award-info":[{"award-number":["2018SHZDZX03"]}],"id":[{"id":"10.13039\/501100003399","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["61761166006"],"award-info":[{"award-number":["61761166006"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Shanghai Municipal Commission of Health and Family Planning","award":["2017ZZ01006"],"award-info":[{"award-number":["2017ZZ01006"]}]}],"content-domain":{"domain":["iopscience.iop.org"],"crossmark-restriction":false},"short-container-title":["J. Neural Eng."],"published-print":{"date-parts":[[2020,8,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    <jats:italic>Objective<\/jats:italic>\n                    . Hand movement is a crucial function for humans\u2019 daily life. Developing brain-machine interface (BMI) to control a robotic hand by brain signals would help the severely paralyzed people partially regain the functional independence. Previous intracranial electroencephalography (iEEG)-based BMIs towards gesture decoding mostly used neural signals from the primary sensorimotor cortex while ignoring the hand movement related signals from posterior parietal cortex (PPC). Here, we propose combining iEEG recordings from PPC with that from primary sensorimotor cortex to enhance the gesture decoding performance of iEEG-based BMI.\n                    <jats:italic>Approach<\/jats:italic>\n                    . Stereoelectroencephalography (SEEG) signals from 25 epilepsy subjects were recorded when they performed a three-class hand gesture task. Across all 25 subjects, we identified 524, 114 and 221 electrodes from three regions of interest (ROIs), including PPC, postcentral cortex (POC) and precentral cortex (PRC), respectively. Based on the time-varying high gamma power (55-150 Hz) of SEEG signal, both the general activation in the task and the fine selectivity to gestures of each electrode in these ROIs along time was evaluated by the coefficient of determination\n                    <jats:italic>r<\/jats:italic>\n                    <jats:sup>2<\/jats:sup>\n                    . According to the activation along time, we further assessed the first activation time of each ROI. Finally, the decoding accuracy for gestures was obtained by linear support vector machine classifier to comparatively explore if the PPC will assist PRC and POC for gesture decoding.\n                    <jats:italic>Main results<\/jats:italic>\n                    . We find that a majority(\n                    <jats:italic>L:<\/jats:italic>\n                    <jats:inline-formula>\n                      <jats:tex-math>\n                        \n                      <\/jats:tex-math>\n                      <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" overflow=\"scroll\">\n                        <mml:mo>&gt;<\/mml:mo>\n                      <\/mml:math>\n                    <\/jats:inline-formula>\n                    60%,\n                    <jats:italic>R:<\/jats:italic>\n                    <jats:inline-formula>\n                      <jats:tex-math>\n                        \n                      <\/jats:tex-math>\n                      <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" overflow=\"scroll\">\n                        <mml:mo>&gt;<\/mml:mo>\n                      <\/mml:math>\n                    <\/jats:inline-formula>\n                    40%) of electrodes in all the three ROIs present significant activation during the task. A large scale temporal activation sequence exists among the ROIs, where PPC activates first, PRC second and POC last. Among the activated electrodes, 15% (PRC), 26% (POC) and 4% (left PPC) of electrodes are significantly selective to gestures. Moreover, decoding accuracy obtained by combining the selective electrodes from three ROIs together is 5%, 3.6%, and 8% higher than that from only PRC and POC when decoding features across, before, and after the movement onset, were used.\n                    <jats:italic>Significance<\/jats:italic>\n                    . This is the first human iEEG study demonstrating that PPC contains neural information about fine hand movement, supporting the role of PPC in hand shape encoding. Combining PPC with primary sensorimotor cortex can provide more information to improve the gesture decoding performance. Our results suggest that PPC could be a rich neural source for iEEG-based BMI. Our findings also demonstrate the early involvement of human PPC in visuomotor task and thus may provide additional implications for further scientific research and BMI applications.\n                  <\/jats:p>","DOI":"10.1088\/1741-2552\/ab9987","type":"journal-article","created":{"date-parts":[[2020,6,4]],"date-time":"2020-06-04T18:30:50Z","timestamp":1591295450000},"page":"046043","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":20,"title":["Enhancing gesture decoding performance using signals from posterior parietal cortex: a stereo-electroencephalograhy (SEEG) study"],"prefix":"10.1088","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2490-5256","authenticated-orcid":false,"given":"Meng","family":"Wang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2530-3916","authenticated-orcid":false,"given":"Guangye","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shize","family":"Jiang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zixuan","family":"Wei","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jie","family":"Hu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liang","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dingguo","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"266","published-online":{"date-parts":[[2020,8,24]]},"reference":[{"key":"jneab9987bib1","doi-asserted-by":"publisher","DOI":"10.1088\/1741-2560\/7\/4\/046002","type":"journal-article","article-title":"Electrocorticographic amplitude predicts finger positions during slow grasping motions of the hand","volume":"7","author":"Acharya","year":"2010","journal-title":"J. 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