Optimum Spatio-Spectral Filtering Network for Brain-Computer Interface
This paper proposes a feature extraction method for motor imagery brain-computer interface (BCI) using electroencephalogram. We consider the primary neurophysiologic phenomenon of motor imagery, termed event-related desynchronization, and formulate the learning task for feature extraction as maximiz...
Saved in:
| Published in: | IEEE transactions on neural networks Vol. 22; no. 1; pp. 52 - 63 |
|---|---|
| Main Authors: | , , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
New York, NY
IEEE
01.01.2011
Institute of Electrical and Electronics Engineers |
| Subjects: | |
| ISSN: | 1045-9227, 1941-0093, 1941-0093 |
| Online Access: | Get full text |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Abstract | This paper proposes a feature extraction method for motor imagery brain-computer interface (BCI) using electroencephalogram. We consider the primary neurophysiologic phenomenon of motor imagery, termed event-related desynchronization, and formulate the learning task for feature extraction as maximizing the mutual information between the spatio-spectral filtering parameters and the class labels. After introducing a nonparametric estimate of mutual information, a gradient-based learning algorithm is devised to efficiently optimize the spatial filters in conjunction with a band-pass filter. The proposed method is compared with two existing methods on real data: a BCI Competition IV dataset as well as our data collected from seven human subjects. The results indicate the superior performance of the method for motor imagery classification, as it produced higher classification accuracy with statistical significance (≥95% confidence level) in most cases. |
|---|---|
| AbstractList | This paper proposes a feature extraction method for motor imagery brain-computer interface (BCI) using electroencephalogram. We consider the primary neurophysiologic phenomenon of motor imagery, termed event-related desynchronization, and formulate the learning task for feature extraction as maximizing the mutual information between the spatio-spectral filtering parameters and the class labels. After introducing a nonparametric estimate of mutual information, a gradient-based learning algorithm is devised to efficiently optimize the spatial filters in conjunction with a band-pass filter. The proposed method is compared with two existing methods on real data: a BCI Competition IV dataset as well as our data collected from seven human subjects. The results indicate the superior performance of the method for motor imagery classification, as it produced higher classification accuracy with statistical significance (≥95% confidence level) in most cases. This paper proposes a feature extraction method for motor imagery brain-computer interface (BCI) using electroencephalogram. We consider the primary neurophysiologic phenomenon of motor imagery, termed event-related desynchronization, and formulate the learning task for feature extraction as maximizing the mutual information between the spatio-spectral filtering parameters and the class labels. After introducing a nonparametric estimate of mutual information, a gradient-based learning algorithm is devised to efficiently optimize the spatial filters in conjunction with a band-pass filter. The proposed method is compared with two existing methods on real data: a BCI Competition IV dataset as well as our data collected from seven human subjects. The results indicate the superior performance of the method for motor imagery classification, as it produced higher classification accuracy with statistical significance ( ≥ 95% confidence level) in most cases.This paper proposes a feature extraction method for motor imagery brain-computer interface (BCI) using electroencephalogram. We consider the primary neurophysiologic phenomenon of motor imagery, termed event-related desynchronization, and formulate the learning task for feature extraction as maximizing the mutual information between the spatio-spectral filtering parameters and the class labels. After introducing a nonparametric estimate of mutual information, a gradient-based learning algorithm is devised to efficiently optimize the spatial filters in conjunction with a band-pass filter. The proposed method is compared with two existing methods on real data: a BCI Competition IV dataset as well as our data collected from seven human subjects. The results indicate the superior performance of the method for motor imagery classification, as it produced higher classification accuracy with statistical significance ( ≥ 95% confidence level) in most cases. This paper proposes a feature extraction method for motor imagery brain-computer interface (BCI) using electroencephalogram. We consider the primary neurophysiologic phenomenon of motor imagery, termed event-related desynchronization, and formulate the learning task for feature extraction as maximizing the mutual information between the spatio-spectral filtering parameters and the class labels. After introducing a nonparametric estimate of mutual information, a gradient-based learning algorithm is devised to efficiently optimize the spatial filters in conjunction with a band-pass filter. The proposed method is compared with two existing methods on real data: a BCI Competition IV dataset as well as our data collected from seven human subjects. The results indicate the superior performance of the method for motor imagery classification, as it produced higher classification accuracy with statistical significance ( ≥ 95% confidence level) in most cases. |
| Author | Haihong Zhang Cuntai Guan Kai Keng Ang Chuanchu Wang Zheng Yang Chin |
| Author_xml | – sequence: 1 givenname: Haihong surname: Zhang fullname: Zhang, Haihong email: hhzhang@i2r.a-star.edu.sg organization: Institute for Infocomm Research, Agency for Science, Technology and Research, 138632, Singapore. hhzhang@i2r.a-star.edu.sg – sequence: 2 givenname: Zheng Yang surname: Chin fullname: Chin, Zheng Yang – sequence: 3 givenname: Kai Keng surname: Ang fullname: Ang, Kai Keng – sequence: 4 givenname: Cuntai surname: Guan fullname: Guan, Cuntai – sequence: 5 givenname: Chuanchu surname: Wang fullname: Wang, Chuanchu |
| BackLink | http://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=23740519$$DView record in Pascal Francis https://www.ncbi.nlm.nih.gov/pubmed/21216696$$D View this record in MEDLINE/PubMed |
| BookMark | eNp9kc1rFTEUxYNU7Jd7QZDZiKupN8kkkyz14dNCeV20XYc7mTsSnS-TGcT_3jzeq0IX3SS55HcO3HPO2ck4jcTYGw5XnIP9eL_bXQnIkwBTgbUv2Bm3FS8BrDzJb6hUaYWoT9l5Sj8AeKVAv2KngguutdVnbHs7L2FYh-JuxiVM5d1MfonYF9vQLxTD-L3Y0fJ7ij-LborF54hhLDfTMK_5t7ge89mhp0v2ssM-0evjfcEetl_uN9_Km9uv15tPN6WXlVlK5BKJt53wRojWmrbWqhK8UxoJpDFCVaCpRWo60dQaawEoWmWwFU1jZSMv2IeD7xynXyulxQ0heep7HGlakzNScW5A2ky-O5JrM1Dr5hgGjH_c4-oZeH8EMHnsu4ijD-k_J-sKFN8b6QPn45RSpM75sOyzGnNOoXcc3L4Ll7tw-y7csYsshCfCR-9nJG8PkkBE_3CluVHayL_mH5Lb |
| CODEN | ITNNEP |
| CitedBy_id | crossref_primary_10_1016_j_bspc_2020_101917 crossref_primary_10_1016_j_irbm_2019_11_002 crossref_primary_10_1109_JPROC_2015_2404941 crossref_primary_10_1016_j_neucom_2012_11_004 crossref_primary_10_1109_TPAMI_2012_69 crossref_primary_10_1109_TPAMI_2014_2330598 crossref_primary_10_1109_TCYB_2019_2963709 crossref_primary_10_3390_sym12091387 crossref_primary_10_1109_TNNLS_2016_2601084 crossref_primary_10_1002_acs_1236 crossref_primary_10_1109_ACCESS_2020_2967814 crossref_primary_10_3390_electronics9020203 crossref_primary_10_1007_s10916_016_0567_5 crossref_primary_10_1109_TBME_2015_2487738 crossref_primary_10_1109_TNSRE_2017_2757519 crossref_primary_10_1109_TNNLS_2013_2249087 crossref_primary_10_3390_math12111727 crossref_primary_10_1016_j_jneumeth_2016_12_010 crossref_primary_10_3389_fnins_2021_715855 crossref_primary_10_1016_j_eswa_2016_08_007 crossref_primary_10_1016_j_compbiomed_2016_10_004 crossref_primary_10_1016_j_neucom_2018_04_087 crossref_primary_10_4028_www_scientific_net_AMR_981_171 crossref_primary_10_1016_j_protcy_2016_05_219 crossref_primary_10_1109_TNNLS_2015_2402694 crossref_primary_10_1109_ACCESS_2019_2941867 crossref_primary_10_1109_TBME_2014_2345458 crossref_primary_10_1016_j_patcog_2021_107918 crossref_primary_10_1109_LSP_2018_2823683 crossref_primary_10_3389_fnins_2018_00540 crossref_primary_10_1109_TCDS_2016_2555952 crossref_primary_10_1088_1741_2560_8_4_046035 |
| Cites_doi | 10.1023/A:1007958904918 10.1016/j.patcog.2009.12.013 10.1016/j.patcog.2003.12.002 10.1109/MIS.2008.41 10.1109/TBME.2007.903709 10.1016/S0169-7161(82)02038-0 10.1109/IEMBS.2008.4650130 10.1109/TNSRE.2003.816866 10.1109/TBME.2008.2009768 10.1109/TNSRE.2003.814456 10.1109/TBME.2006.883649 10.1016/S1388-2457(98)00043-1 10.1088/1741-2560/1/3/002 10.1109/TBME.2008.921154 10.1016/S0013-4694(97)00066-7 10.1109/MSP.2008.4408441 10.1016/S1388-2457(02)00057-3 10.1109/IEMBS.2009.5334093 10.1016/S1388-2457(03)00093-2 10.1093/oso/9780198523963.001.0001 10.1212/01.WNL.0000158616.43002.6D 10.1109/TNSRE.2006.875567 10.1109/86.895946 10.1109/TBME.2004.827088 10.1016/S0013-4694(97)00080-1 10.1109/TBME.2005.851521 10.1109/5.939829 10.1109/TBME.2008.919125 10.1016/S1388-2457(98)00038-8 10.1016/j.neuroimage.2007.01.051 10.1088/1741-2560/3/3/003 10.1109/TNN.2008.2005601 10.1002/9780470316849 10.1088/1741-2560/4/2/R03 10.1088/1741-2560/5/1/002 10.1113/jphysiol.2006.125948 |
| ContentType | Journal Article |
| Copyright | 2015 INIST-CNRS |
| Copyright_xml | – notice: 2015 INIST-CNRS |
| DBID | 97E RIA RIE AAYXX CITATION IQODW CGR CUY CVF ECM EIF NPM 7X8 |
| DOI | 10.1109/TNN.2010.2084099 |
| DatabaseName | IEEE Xplore (IEEE) IEEE All-Society Periodicals Package (ASPP) 1998–Present IEEE Electronic Library (IEL) CrossRef Pascal-Francis Medline MEDLINE MEDLINE (Ovid) MEDLINE MEDLINE PubMed MEDLINE - Academic |
| DatabaseTitle | CrossRef MEDLINE Medline Complete MEDLINE with Full Text PubMed MEDLINE (Ovid) MEDLINE - Academic |
| DatabaseTitleList | MEDLINE - Academic MEDLINE |
| Database_xml | – sequence: 1 dbid: NPM name: PubMed url: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 2 dbid: RIE name: IEEE Electronic Library (IEL) url: https://ieeexplore.ieee.org/ sourceTypes: Publisher – sequence: 3 dbid: 7X8 name: MEDLINE - Academic url: https://search.proquest.com/medline sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering Anatomy & Physiology Computer Science Applied Sciences |
| EISSN | 1941-0093 |
| EndPage | 63 |
| ExternalDocumentID | 21216696 23740519 10_1109_TNN_2010_2084099 5618568 |
| Genre | orig-research Research Support, Non-U.S. Gov't Journal Article Comparative Study |
| GroupedDBID | --- -~X .DC 0R~ 29I 4.4 53G 5GY 5VS 6IK 97E AAJGR AASAJ AAWTH ABAZT ABJNI ABQJQ ABVLG ACGFS AETIX AGQYO AGSQL AHBIQ AI. AIBXA ALLEH ALMA_UNASSIGNED_HOLDINGS ASUFR ATWAV BEFXN BFFAM BGNUA BKEBE BPEOZ CS3 DU5 EBS EJD F5P HZ~ H~9 ICLAB IFIPE IFJZH IPLJI JAVBF LAI M43 MS~ O9- OCL P2P RIA RIE RNS S10 TAE TN5 VH1 AAYXX CITATION IQODW RIG CGR CUY CVF ECM EIF NPM 7X8 |
| ID | FETCH-LOGICAL-c348t-a13ae1df2c822d98d765421f56ae038825406edaebf2b76a720a2d58ad2bb93b3 |
| IEDL.DBID | RIE |
| ISICitedReferencesCount | 60 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000286010000005&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 1045-9227 1941-0093 |
| IngestDate | Fri Sep 05 08:32:29 EDT 2025 Mon Jul 21 05:48:38 EDT 2025 Mon Jul 21 09:13:56 EDT 2025 Tue Nov 18 21:45:10 EST 2025 Sat Nov 29 08:08:15 EST 2025 Tue Aug 26 17:18:09 EDT 2025 |
| IsPeerReviewed | false |
| IsScholarly | false |
| Issue | 1 |
| Keywords | Brain Gradient Statistical analysis Filtering Electroencephalography Pattern recognition Network interfaces spatio-spectral filtering motor imagery electroencephalography Optimization Search algorithm Brain-computer interface User interface Classification Feature extraction Spatial filters Desynchronization Learning algorithm Artificial intelligence Pattern extraction Mutual information |
| Language | English |
| License | https://ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html CC BY 4.0 |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c348t-a13ae1df2c822d98d765421f56ae038825406edaebf2b76a720a2d58ad2bb93b3 |
| Notes | ObjectType-Article-2 SourceType-Scholarly Journals-1 ObjectType-Feature-1 content type line 23 |
| PMID | 21216696 |
| PQID | 835118039 |
| PQPubID | 23479 |
| PageCount | 12 |
| ParticipantIDs | pascalfrancis_primary_23740519 pubmed_primary_21216696 crossref_citationtrail_10_1109_TNN_2010_2084099 proquest_miscellaneous_835118039 ieee_primary_5618568 crossref_primary_10_1109_TNN_2010_2084099 |
| PublicationCentury | 2000 |
| PublicationDate | 2011-Jan. 2011-01-00 2011 2011-Jan 20110101 |
| PublicationDateYYYYMMDD | 2011-01-01 |
| PublicationDate_xml | – month: 01 year: 2011 text: 2011-Jan. |
| PublicationDecade | 2010 |
| PublicationPlace | New York, NY |
| PublicationPlace_xml | – name: New York, NY – name: United States |
| PublicationTitle | IEEE transactions on neural networks |
| PublicationTitleAbbrev | TNN |
| PublicationTitleAlternate | IEEE Trans Neural Netw |
| PublicationYear | 2011 |
| Publisher | IEEE Institute of Electrical and Electronics Engineers |
| Publisher_xml | – name: IEEE – name: Institute of Electrical and Electronics Engineers |
| References | ref35 ref13 ref37 ref15 ref36 ref14 ref31 ref30 ref11 ref32 ref10 cover (ref27) 2006 ref2 ang (ref22) 2008 ref1 ref39 ref17 ref16 ref19 ref18 wolpaw (ref3) 2002; 113 bowman (ref34) 1997 (ref24) 0 ref23 scott (ref33) 1992 ref25 ref20 ref41 chang (ref38) 2001 ref21 fukunaga (ref26) 1990 ref29 ref8 ref7 goncharova (ref12) 2003; 114 ref9 ref4 muller-gerking (ref5) 1999; 110 ben-bassat (ref28) 1982; 2 ref6 ref40 |
| References_xml | – ident: ref32 doi: 10.1023/A:1007958904918 – ident: ref30 doi: 10.1016/j.patcog.2009.12.013 – year: 2006 ident: ref27 publication-title: Elements of Information Theory – ident: ref29 doi: 10.1016/j.patcog.2003.12.002 – ident: ref2 doi: 10.1109/MIS.2008.41 – ident: ref36 doi: 10.1109/TBME.2007.903709 – volume: 2 start-page: 773 year: 1982 ident: ref28 publication-title: Handbook of Statistics doi: 10.1016/S0169-7161(82)02038-0 – ident: ref23 doi: 10.1109/IEMBS.2008.4650130 – ident: ref39 doi: 10.1109/TNSRE.2003.816866 – ident: ref15 doi: 10.1109/TBME.2008.2009768 – year: 2001 ident: ref38 publication-title: LIBSVM A library for support vector machines – start-page: 2391 year: 2008 ident: ref22 article-title: filter bank common spatial pattern (fbcsp) in brain-computer interface publication-title: Proc Int Joint Conf Neural Netw – ident: ref9 doi: 10.1109/TNSRE.2003.814456 – ident: ref20 doi: 10.1109/TBME.2006.883649 – ident: ref11 doi: 10.1016/S1388-2457(98)00043-1 – ident: ref14 doi: 10.1088/1741-2560/1/3/002 – ident: ref8 doi: 10.1109/TBME.2008.921154 – ident: ref10 doi: 10.1016/S0013-4694(97)00066-7 – ident: ref17 doi: 10.1109/MSP.2008.4408441 – volume: 113 start-page: 767 year: 2002 ident: ref3 article-title: brain-computer interfaces for communication and control publication-title: Clin Neurophysiol doi: 10.1016/S1388-2457(02)00057-3 – ident: ref25 doi: 10.1109/IEMBS.2009.5334093 – volume: 114 start-page: 1580 year: 2003 ident: ref12 article-title: emg contamination of eeg: spectral and topographical characteristics publication-title: Clin Neurophysiol doi: 10.1016/S1388-2457(03)00093-2 – year: 1997 ident: ref34 publication-title: Applied Smoothing Techniques for Data Analysis The Kernel Approach with S-Plus Illustrations doi: 10.1093/oso/9780198523963.001.0001 – ident: ref6 doi: 10.1212/01.WNL.0000158616.43002.6D – ident: ref35 doi: 10.1109/TNSRE.2006.875567 – ident: ref16 doi: 10.1109/86.895946 – ident: ref7 doi: 10.1109/TBME.2004.827088 – ident: ref4 doi: 10.1016/S0013-4694(97)00080-1 – ident: ref19 doi: 10.1109/TBME.2005.851521 – ident: ref37 doi: 10.1109/5.939829 – ident: ref21 doi: 10.1109/TBME.2008.919125 – volume: 110 start-page: 787 year: 1999 ident: ref5 article-title: designing optimal spatial filtering of single trial eeg classification in a movement task publication-title: Clin Neurophysiol doi: 10.1016/S1388-2457(98)00038-8 – year: 0 ident: ref24 publication-title: BCI Competition IV – ident: ref41 doi: 10.1016/j.neuroimage.2007.01.051 – ident: ref18 doi: 10.1088/1741-2560/3/3/003 – ident: ref31 doi: 10.1109/TNN.2008.2005601 – year: 1992 ident: ref33 publication-title: Multivariate Density Estimation Theory Practice and Visualization doi: 10.1002/9780470316849 – ident: ref13 doi: 10.1088/1741-2560/4/2/R03 – ident: ref40 doi: 10.1088/1741-2560/5/1/002 – year: 1990 ident: ref26 publication-title: Introduction to statistical pattern recognition – ident: ref1 doi: 10.1113/jphysiol.2006.125948 |
| SSID | ssj0014506 |
| Score | 1.6648642 |
| Snippet | This paper proposes a feature extraction method for motor imagery brain-computer interface (BCI) using electroencephalogram. We consider the primary... |
| SourceID | proquest pubmed pascalfrancis crossref ieee |
| SourceType | Aggregation Database Index Database Enrichment Source Publisher |
| StartPage | 52 |
| SubjectTerms | Applied sciences Artificial intelligence Band pass filters Brain - physiology Brain-computer interface Computer science; control theory; systems Computer systems and distributed systems. User interface Electroencephalography Electroencephalography - methods Entropy Evoked Potentials, Motor - physiology Exact sciences and technology Feature extraction Humans Male motor imagery electroencephalography Mutual information Neural Networks (Computer) Optimization Pattern Recognition, Automated - standards Rhythm Software spatio-spectral filtering User-Computer Interface |
| Title | Optimum Spatio-Spectral Filtering Network for Brain-Computer Interface |
| URI | https://ieeexplore.ieee.org/document/5618568 https://www.ncbi.nlm.nih.gov/pubmed/21216696 https://www.proquest.com/docview/835118039 |
| Volume | 22 |
| WOSCitedRecordID | wos000286010000005&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVIEE databaseName: IEEE Electronic Library (IEL) customDbUrl: eissn: 1941-0093 dateEnd: 20111231 omitProxy: false ssIdentifier: ssj0014506 issn: 1045-9227 databaseCode: RIE dateStart: 19900101 isFulltext: true titleUrlDefault: https://ieeexplore.ieee.org/ providerName: IEEE |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3Na9swFH-0ZYf10HbJtmZtgw5jMJgWW7I-fOzGQk_eYB3kZiRLhkLjjCYZ7L-fnuS4HayDgg8GP9nGvyfrfen9AN7y1mteeEEVNxktcuVoqRpHgydgM2bbVrtENqGqSi8W5bc9-DDshfHex-Iz_xFPYy7frZothspmYa3XQup92FdKpr1aQ8agEJFHM3gXgpaMqV1KMitn11WVarhYhu5MGRsAs1xK7NT_YDWK9CpYHGnW4fu0idjiccszrkDz46e9-wkc9ZYmuUyq8QL2fDeC8WUXvOzlb_KOxNrPGFQfwfGO3IH0c30Ehw86FY5h_jX8WpbbJfkeK7Ap0tZjjITMbzDdHmRIlQrKSbCCyScknqDDTWPYsTWNfwk_5l-uP1_RnoKBNrzQG2pybnzuWtYEQ8KV2ikkuMpbIY3HPjLBvcykd8bbllkljWKZYU5o45i1Jbf8FRx0q86fAtFCKBOMvVx7UXgWrjFmnZQ2uHw5s2ICsx0UddP3J0eajNs6-ilZWQcca8Sx7nGcwPthxM_Um-M_smPEZJDr4ZjA9C-0h-uMqwKt2wmQHfx1mHiYTTGdX23XtcYUrM54EHmd1OJ-cK9db_790DN4niLTeJzDweZu6y_gWfNrc7O-mwblXuhpVO4_tRLyUg |
| linkProvider | IEEE |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3da9RAEB9qFWwfrN5Ze37UfRBBcL1kN5vdPFbxqFij4Al9C7vZCRR6udK7E_zv3dnkYgUVhDwEMpuE_Gaz87XzA3ghGzQyQ8W1tAnPUu15oWvPgyfgEuGaxviObEKXpTk_L77swOthLwwixuIzfEOnMZfvl_WGQmXTsNYblZtbcJuYs_rdWkPOIFORSTP4F4oXQuhtUjIppvOy7Kq4REIOTRFbAIs0z6lX_431KBKsUHmkXYUv1HTUFn-3PeMaNDv4v7e_D_d6W5OddMrxAHawHcH4pA1-9uIHe8li9WcMq4_gYEvvwPrZPoL9G70KxzD7HH4ui82CfY012JyI6ylKwmYXlHAPMqzsSspZsIPZW6Ke4MNNY-CxsTU-hG-z9_N3p7wnYeC1zMya21RaTH0j6mBK-MJ4TRRXaaNyi9RJJjiYSY7eomuE07nVIrHCK2O9cK6QTh7Cbrts8QiYUUrbYO6lBlWGIlwTwvk8d8HpS4VTE5huoajqvkM5EWVcVtFTSYoq4FgRjlWP4wReDSOuuu4c_5AdEyaDXA_HBI5_Q3u4LqTOyL6dANvCX4WpR_kU2-Jys6oMJWFNIoPIo04tfg3utevxnx_6HO6ezj-dVWcfyo9PYK-LU9PxFHbX1xt8Bnfq7-uL1fVxVPGfxN70sw |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Optimum+Spatio-Spectral+Filtering+Network+for+Brain-Computer+Interface&rft.jtitle=IEEE+transactions+on+neural+networks&rft.au=HAIHONG+ZHANG&rft.au=ZHENG+YANG+CHIN&rft.au=KAI+KENG+ANG&rft.au=CUNTAI+GUAN&rft.date=2011&rft.pub=Institute+of+Electrical+and+Electronics+Engineers&rft.issn=1045-9227&rft.volume=22&rft.issue=1&rft.spage=52&rft.epage=63&rft_id=info:doi/10.1109%2FTNN.2010.2084099&rft.externalDBID=n%2Fa&rft.externalDocID=23740519 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1045-9227&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1045-9227&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1045-9227&client=summon |