Automatic Classification of Artifactual ICA-Components for Artifact Removal in EEG Signals
Background Artifacts contained in EEG recordings hamper both, the visual interpretation by experts as well as the algorithmic processing and analysis (e.g. for Brain-Computer Interfaces (BCI) or for Mental State Monitoring). While hand-optimized selection of source components derived from Independen...
Gespeichert in:
| Veröffentlicht in: | Behavioral and brain functions Jg. 7; H. 1; S. 30 |
|---|---|
| Hauptverfasser: | , , |
| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
London
BioMed Central
02.08.2011
Springer Nature B.V BMC |
| Schlagworte: | |
| ISSN: | 1744-9081, 1744-9081 |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| Abstract | Background
Artifacts contained in EEG recordings hamper both, the visual interpretation by experts as well as the algorithmic processing and analysis (e.g. for Brain-Computer Interfaces (BCI) or for Mental State Monitoring). While hand-optimized selection of source components derived from Independent Component Analysis (ICA) to clean EEG data is widespread, the field could greatly profit from automated solutions based on Machine Learning methods. Existing ICA-based removal strategies depend on explicit recordings of an individual's artifacts or have not been shown to reliably identify muscle artifacts.
Methods
We propose an automatic method for the classification of general artifactual source components. They are estimated by TDSEP, an ICA method that takes temporal correlations into account. The linear classifier is based on an optimized feature subset determined by a Linear Programming Machine (LPM). The subset is composed of features from the frequency-, the spatial- and temporal domain. A subject independent classifier was trained on 640 TDSEP components (reaction time (RT) study, n = 12) that were hand labeled by experts as artifactual or brain sources and tested on 1080 new components of RT data of the same study. Generalization was tested on new data from two studies (auditory Event Related Potential (ERP) paradigm, n = 18; motor imagery BCI paradigm, n = 80) that used data with different channel setups and from new subjects.
Results
Based on six features only, the optimized linear classifier performed on level with the inter-expert disagreement (
<
10% Mean Squared Error (MSE)) on the RT data. On data of the auditory ERP study, the same pre-calculated classifier generalized well and achieved 15% MSE. On data of the motor imagery paradigm, we demonstrate that the discriminant information used for BCI is preserved when removing up to 60% of the most artifactual source components.
Conclusions
We propose a universal and efficient classifier of ICA components for the subject independent removal of artifacts from EEG data. Based on linear methods, it is applicable for different electrode placements and supports the introspection of results. Trained on expert ratings of large data sets, it is not restricted to the detection of eye- and muscle artifacts. Its performance and generalization ability is demonstrated on data of different EEG studies. |
|---|---|
| AbstractList | Abstract Background Artifacts contained in EEG recordings hamper both, the visual interpretation by experts as well as the algorithmic processing and analysis (e.g. for Brain-Computer Interfaces (BCI) or for Mental State Monitoring). While hand-optimized selection of source components derived from Independent Component Analysis (ICA) to clean EEG data is widespread, the field could greatly profit from automated solutions based on Machine Learning methods. Existing ICA-based removal strategies depend on explicit recordings of an individual's artifacts or have not been shown to reliably identify muscle artifacts. Methods We propose an automatic method for the classification of general artifactual source components. They are estimated by TDSEP, an ICA method that takes temporal correlations into account. The linear classifier is based on an optimized feature subset determined by a Linear Programming Machine (LPM). The subset is composed of features from the frequency-, the spatial- and temporal domain. A subject independent classifier was trained on 640 TDSEP components (reaction time (RT) study, n = 12) that were hand labeled by experts as artifactual or brain sources and tested on 1080 new components of RT data of the same study. Generalization was tested on new data from two studies (auditory Event Related Potential (ERP) paradigm, n = 18; motor imagery BCI paradigm, n = 80) that used data with different channel setups and from new subjects. Results Based on six features only, the optimized linear classifier performed on level with the inter-expert disagreement (<10% Mean Squared Error (MSE)) on the RT data. On data of the auditory ERP study, the same pre-calculated classifier generalized well and achieved 15% MSE. On data of the motor imagery paradigm, we demonstrate that the discriminant information used for BCI is preserved when removing up to 60% of the most artifactual source components. Conclusions We propose a universal and efficient classifier of ICA components for the subject independent removal of artifacts from EEG data. Based on linear methods, it is applicable for different electrode placements and supports the introspection of results. Trained on expert ratings of large data sets, it is not restricted to the detection of eye- and muscle artifacts. Its performance and generalization ability is demonstrated on data of different EEG studies. Artifacts contained in EEG recordings hamper both, the visual interpretation by experts as well as the algorithmic processing and analysis (e.g. for Brain-Computer Interfaces (BCI) or for Mental State Monitoring). While hand-optimized selection of source components derived from Independent Component Analysis (ICA) to clean EEG data is widespread, the field could greatly profit from automated solutions based on Machine Learning methods. Existing ICA-based removal strategies depend on explicit recordings of an individual's artifacts or have not been shown to reliably identify muscle artifacts.BACKGROUNDArtifacts contained in EEG recordings hamper both, the visual interpretation by experts as well as the algorithmic processing and analysis (e.g. for Brain-Computer Interfaces (BCI) or for Mental State Monitoring). While hand-optimized selection of source components derived from Independent Component Analysis (ICA) to clean EEG data is widespread, the field could greatly profit from automated solutions based on Machine Learning methods. Existing ICA-based removal strategies depend on explicit recordings of an individual's artifacts or have not been shown to reliably identify muscle artifacts.We propose an automatic method for the classification of general artifactual source components. They are estimated by TDSEP, an ICA method that takes temporal correlations into account. The linear classifier is based on an optimized feature subset determined by a Linear Programming Machine (LPM). The subset is composed of features from the frequency-, the spatial- and temporal domain. A subject independent classifier was trained on 640 TDSEP components (reaction time (RT) study, n = 12) that were hand labeled by experts as artifactual or brain sources and tested on 1080 new components of RT data of the same study. Generalization was tested on new data from two studies (auditory Event Related Potential (ERP) paradigm, n = 18; motor imagery BCI paradigm, n = 80) that used data with different channel setups and from new subjects.METHODSWe propose an automatic method for the classification of general artifactual source components. They are estimated by TDSEP, an ICA method that takes temporal correlations into account. The linear classifier is based on an optimized feature subset determined by a Linear Programming Machine (LPM). The subset is composed of features from the frequency-, the spatial- and temporal domain. A subject independent classifier was trained on 640 TDSEP components (reaction time (RT) study, n = 12) that were hand labeled by experts as artifactual or brain sources and tested on 1080 new components of RT data of the same study. Generalization was tested on new data from two studies (auditory Event Related Potential (ERP) paradigm, n = 18; motor imagery BCI paradigm, n = 80) that used data with different channel setups and from new subjects.Based on six features only, the optimized linear classifier performed on level with the inter-expert disagreement (<10% Mean Squared Error (MSE)) on the RT data. On data of the auditory ERP study, the same pre-calculated classifier generalized well and achieved 15% MSE. On data of the motor imagery paradigm, we demonstrate that the discriminant information used for BCI is preserved when removing up to 60% of the most artifactual source components.RESULTSBased on six features only, the optimized linear classifier performed on level with the inter-expert disagreement (<10% Mean Squared Error (MSE)) on the RT data. On data of the auditory ERP study, the same pre-calculated classifier generalized well and achieved 15% MSE. On data of the motor imagery paradigm, we demonstrate that the discriminant information used for BCI is preserved when removing up to 60% of the most artifactual source components.We propose a universal and efficient classifier of ICA components for the subject independent removal of artifacts from EEG data. Based on linear methods, it is applicable for different electrode placements and supports the introspection of results. Trained on expert ratings of large data sets, it is not restricted to the detection of eye- and muscle artifacts. Its performance and generalization ability is demonstrated on data of different EEG studies.CONCLUSIONSWe propose a universal and efficient classifier of ICA components for the subject independent removal of artifacts from EEG data. Based on linear methods, it is applicable for different electrode placements and supports the introspection of results. Trained on expert ratings of large data sets, it is not restricted to the detection of eye- and muscle artifacts. Its performance and generalization ability is demonstrated on data of different EEG studies. Background Artifacts contained in EEG recordings hamper both, the visual interpretation by experts as well as the algorithmic processing and analysis (e.g. for Brain-Computer Interfaces (BCI) or for Mental State Monitoring). While hand-optimized selection of source components derived from Independent Component Analysis (ICA) to clean EEG data is widespread, the field could greatly profit from automated solutions based on Machine Learning methods. Existing ICA-based removal strategies depend on explicit recordings of an individual's artifacts or have not been shown to reliably identify muscle artifacts. Methods We propose an automatic method for the classification of general artifactual source components. They are estimated by TDSEP, an ICA method that takes temporal correlations into account. The linear classifier is based on an optimized feature subset determined by a Linear Programming Machine (LPM). The subset is composed of features from the frequency-, the spatial- and temporal domain. A subject independent classifier was trained on 640 TDSEP components (reaction time (RT) study, n = 12) that were hand labeled by experts as artifactual or brain sources and tested on 1080 new components of RT data of the same study. Generalization was tested on new data from two studies (auditory Event Related Potential (ERP) paradigm, n = 18; motor imagery BCI paradigm, n = 80) that used data with different channel setups and from new subjects. Results Based on six features only, the optimized linear classifier performed on level with the inter-expert disagreement ( < 10% Mean Squared Error (MSE)) on the RT data. On data of the auditory ERP study, the same pre-calculated classifier generalized well and achieved 15% MSE. On data of the motor imagery paradigm, we demonstrate that the discriminant information used for BCI is preserved when removing up to 60% of the most artifactual source components. Conclusions We propose a universal and efficient classifier of ICA components for the subject independent removal of artifacts from EEG data. Based on linear methods, it is applicable for different electrode placements and supports the introspection of results. Trained on expert ratings of large data sets, it is not restricted to the detection of eye- and muscle artifacts. Its performance and generalization ability is demonstrated on data of different EEG studies. Artifacts contained in EEG recordings hamper both, the visual interpretation by experts as well as the algorithmic processing and analysis (e.g. for Brain-Computer Interfaces (BCI) or for Mental State Monitoring). While hand-optimized selection of source components derived from Independent Component Analysis (ICA) to clean EEG data is widespread, the field could greatly profit from automated solutions based on Machine Learning methods. Existing ICA-based removal strategies depend on explicit recordings of an individual's artifacts or have not been shown to reliably identify muscle artifacts. We propose an automatic method for the classification of general artifactual source components. They are estimated by TDSEP, an ICA method that takes temporal correlations into account. The linear classifier is based on an optimized feature subset determined by a Linear Programming Machine (LPM). The subset is composed of features from the frequency-, the spatial- and temporal domain. A subject independent classifier was trained on 640 TDSEP components (reaction time (RT) study, n = 12) that were hand labeled by experts as artifactual or brain sources and tested on 1080 new components of RT data of the same study. Generalization was tested on new data from two studies (auditory Event Related Potential (ERP) paradigm, n = 18; motor imagery BCI paradigm, n = 80) that used data with different channel setups and from new subjects. Based on six features only, the optimized linear classifier performed on level with the inter-expert disagreement (<10% Mean Squared Error (MSE)) on the RT data. On data of the auditory ERP study, the same pre-calculated classifier generalized well and achieved 15% MSE. On data of the motor imagery paradigm, we demonstrate that the discriminant information used for BCI is preserved when removing up to 60% of the most artifactual source components. We propose a universal and efficient classifier of ICA components for the subject independent removal of artifacts from EEG data. Based on linear methods, it is applicable for different electrode placements and supports the introspection of results. Trained on expert ratings of large data sets, it is not restricted to the detection of eye- and muscle artifacts. Its performance and generalization ability is demonstrated on data of different EEG studies. |
| Author | Winkler, Irene Tangermann, Michael Haufe, Stefan |
| AuthorAffiliation | 1 Machine Learning Laboratory, Berlin Institute of Technology, Franklinstr. 28/29, 10587 Berlin, Germany |
| AuthorAffiliation_xml | – name: 1 Machine Learning Laboratory, Berlin Institute of Technology, Franklinstr. 28/29, 10587 Berlin, Germany |
| Author_xml | – sequence: 1 givenname: Irene surname: Winkler fullname: Winkler, Irene email: irene.winkler@tu-berlin.de organization: Machine Learning Laboratory, Berlin Institute of Technology – sequence: 2 givenname: Stefan surname: Haufe fullname: Haufe, Stefan organization: Machine Learning Laboratory, Berlin Institute of Technology – sequence: 3 givenname: Michael surname: Tangermann fullname: Tangermann, Michael organization: Machine Learning Laboratory, Berlin Institute of Technology |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/21810266$$D View this record in MEDLINE/PubMed |
| BookMark | eNp1kktvGyEUhVGUKq9m22U16qarSS6PAWZTybLcNFKkSn1sskEMBhdrBlyYiZR_XxwnbhIpG57nfBzgnqLDEINF6AOGC4wlv8SCsboFiWtRUzhAJ_uFw2fjY3Sa8xqASkbIETomWGIgnJ-g29k0xkGP3lTzXufsnTdlFkMVXTVLo3fajJPuq-v5rJ7HYVOOD2OuXEz77eqHHeJd0fhQLRZX1U-_CrrP79E7Vzp7_tifod9fF7_m3-qb71eFdlMbLtuxpksMDrvSUEJMJySR1GDTaamxdkaDZC3rJOCO8ca0DAvCOyIlNqzhVjt6hq533GXUa7VJftDpXkXt1cNCTCulS1LTW2Vo05DOMaBWMIpBLgtGc0c5AJeuKawvO9Zm6ga7NOWuSfcvoC93gv-jVvFOUSwa1tAC-PwISPHvZPOoBp-N7XsdbJyyki1pmWi4KMpPr5TrOKXtw6kWCAUMnBTRx-d59kGePrAI2E5gUsw5WaeMHx8-sMTzvcKgtnWitqWgtqWghKJQbBevbE_kNw2XO0MuwrCy6X_aNxz_AMQey2Y |
| CitedBy_id | crossref_primary_10_3233_JAD_201560 crossref_primary_10_1002_hbm_26643 crossref_primary_10_1016_j_dib_2017_11_032 crossref_primary_10_1109_TNSRE_2020_2980223 crossref_primary_10_1016_j_eswa_2021_115941 crossref_primary_10_1016_j_bspc_2023_104677 crossref_primary_10_1016_j_jneumeth_2017_10_011 crossref_primary_10_1044_2019_AJA_19_0011 crossref_primary_10_1016_j_displa_2023_102415 crossref_primary_10_1016_j_neuroimage_2021_118713 crossref_primary_10_1162_jocn_a_02257 crossref_primary_10_1371_journal_pone_0258335 crossref_primary_10_1080_2326263X_2017_1304020 crossref_primary_10_1088_1741_2560_13_6_066005 crossref_primary_10_1016_j_dcn_2024_101468 crossref_primary_10_1088_1741_2552_aa8232 crossref_primary_10_4028_www_scientific_net_JBBBE_41_91 crossref_primary_10_1016_j_ijpsycho_2023_01_006 crossref_primary_10_1080_15374416_2020_1796680 crossref_primary_10_1016_j_bpsc_2021_11_006 crossref_primary_10_3389_fnins_2022_974162 crossref_primary_10_1016_j_bspc_2023_104657 crossref_primary_10_1080_10447318_2023_2260983 crossref_primary_10_3389_fnins_2017_00012 crossref_primary_10_1016_j_neuroimage_2024_120619 crossref_primary_10_1038_s41398_021_01763_3 crossref_primary_10_3389_fpsyg_2022_1065598 crossref_primary_10_1109_ACCESS_2018_2842082 crossref_primary_10_1016_j_cogsys_2018_11_002 crossref_primary_10_3390_s19102302 crossref_primary_10_1016_j_physbeh_2023_114321 crossref_primary_10_1016_j_jneuroling_2022_101098 crossref_primary_10_1016_j_engappai_2023_106345 crossref_primary_10_1016_j_infbeh_2022_101807 crossref_primary_10_3390_brainsci15010028 crossref_primary_10_3389_fnins_2019_00441 crossref_primary_10_1002_ejp_4737 crossref_primary_10_1038_s41598_024_68905_7 crossref_primary_10_1002_aur_2131 crossref_primary_10_1038_s41598_021_80995_1 crossref_primary_10_3390_app112110461 crossref_primary_10_1016_j_neuroimage_2019_06_059 crossref_primary_10_1016_j_neuroimage_2024_120848 crossref_primary_10_1038_s41598_024_76423_9 crossref_primary_10_1093_cercor_bhab425 crossref_primary_10_1016_j_clinph_2019_06_012 crossref_primary_10_1016_j_neuroimage_2024_120965 crossref_primary_10_1371_journal_pone_0098322 crossref_primary_10_3389_fnins_2020_577160 crossref_primary_10_1038_s41598_024_82696_x crossref_primary_10_1038_s41598_019_40743_y crossref_primary_10_1080_17470919_2023_2278199 crossref_primary_10_1007_s10548_021_00854_0 crossref_primary_10_1016_j_compbiomed_2024_109305 crossref_primary_10_3390_brainsci13040621 crossref_primary_10_1016_j_neuroimage_2020_117674 crossref_primary_10_1016_j_parkreldis_2021_02_005 crossref_primary_10_1016_j_bspc_2017_06_012 crossref_primary_10_1016_j_pscychresns_2021_111367 crossref_primary_10_1016_j_neuroimage_2020_117670 crossref_primary_10_3390_bdcc5030039 crossref_primary_10_1016_j_sleep_2025_106645 crossref_primary_10_3758_s13415_019_00759_3 crossref_primary_10_1016_j_neuroimage_2019_06_046 crossref_primary_10_1109_TNSRE_2025_3601445 crossref_primary_10_1109_ACCESS_2021_3125728 crossref_primary_10_3389_fnhum_2014_00156 crossref_primary_10_37819_hb_1_2068 crossref_primary_10_1038_s42003_023_05168_4 crossref_primary_10_1063_1_5049191 crossref_primary_10_1016_j_dcn_2023_101260 crossref_primary_10_1002_hbm_26719 crossref_primary_10_1016_j_neucom_2021_09_012 crossref_primary_10_1007_s12144_025_07338_5 crossref_primary_10_1109_TBME_2018_2889512 crossref_primary_10_3389_fnbot_2024_1336438 crossref_primary_10_1007_s11357_023_01041_8 crossref_primary_10_1016_j_biopsycho_2018_10_004 crossref_primary_10_1016_j_neurobiolaging_2024_04_001 crossref_primary_10_1016_j_neuroimage_2023_120426 crossref_primary_10_3389_fnins_2018_00097 crossref_primary_10_1098_rsos_190048 crossref_primary_10_1016_j_neuroimage_2024_120667 crossref_primary_10_3389_fpsyg_2022_823700 crossref_primary_10_1016_j_neuroimage_2019_05_026 crossref_primary_10_15446_ing_investig_v36n3_54037 crossref_primary_10_1016_j_neuroimage_2018_03_016 crossref_primary_10_1007_s10548_022_00917_w crossref_primary_10_1080_1028415X_2021_1954292 crossref_primary_10_3389_fninf_2019_00055 crossref_primary_10_1038_s41598_021_87746_2 crossref_primary_10_3389_fnins_2023_1222472 crossref_primary_10_1109_TNSRE_2025_3555542 crossref_primary_10_1016_j_dcn_2023_101251 crossref_primary_10_3389_fnins_2024_1412527 crossref_primary_10_1038_s41598_019_49726_5 crossref_primary_10_1038_s41598_019_47372_5 crossref_primary_10_12688_f1000research_17613_2 crossref_primary_10_12688_f1000research_17613_1 crossref_primary_10_1152_jn_00163_2025 crossref_primary_10_1016_j_neuroimage_2024_120891 crossref_primary_10_1109_TNSRE_2020_3017167 crossref_primary_10_1016_j_neuropsychologia_2017_09_012 crossref_primary_10_1093_comjnl_bxaa175 crossref_primary_10_1016_j_medengphy_2012_08_017 crossref_primary_10_1016_j_ajp_2023_103654 crossref_primary_10_3390_app9245340 crossref_primary_10_3389_fnins_2017_00548 crossref_primary_10_3389_fphys_2020_615961 crossref_primary_10_1016_j_neuroimage_2018_03_035 crossref_primary_10_1016_j_ebr_2025_100809 crossref_primary_10_1016_j_ijpsycho_2020_09_019 crossref_primary_10_1109_JPROC_2015_2425807 crossref_primary_10_1016_j_compbiomed_2024_109225 crossref_primary_10_1016_j_cortex_2024_02_009 crossref_primary_10_1016_j_bbe_2021_06_007 crossref_primary_10_1088_1741_2552_adbebe crossref_primary_10_1088_2057_1976_acaca2 crossref_primary_10_1061_JCEMD4_COENG_16651 crossref_primary_10_1002_hbm_26727 crossref_primary_10_3389_fnhum_2018_00366 crossref_primary_10_1038_s42003_021_02240_9 crossref_primary_10_3390_brainsci12020293 crossref_primary_10_3758_s13414_024_02916_4 crossref_primary_10_1007_s11633_024_1492_6 crossref_primary_10_1016_j_schres_2025_07_003 crossref_primary_10_3390_brainsci13121639 crossref_primary_10_1186_s41239_025_00554_w crossref_primary_10_1109_TNSRE_2022_3154891 crossref_primary_10_1159_000542360 crossref_primary_10_1016_j_enbuild_2024_114165 crossref_primary_10_1080_0144929X_2021_1876763 crossref_primary_10_1016_j_neuroimage_2019_04_028 crossref_primary_10_3389_fnins_2024_1441799 crossref_primary_10_3390_biomedinformatics2010007 crossref_primary_10_1016_j_neuroimage_2021_118313 crossref_primary_10_1080_17588928_2019_1627303 crossref_primary_10_3390_brainsci15070714 crossref_primary_10_1152_jn_00253_2023 crossref_primary_10_1002_hbm_24851 crossref_primary_10_1016_j_procs_2016_08_253 crossref_primary_10_3389_fnhum_2018_00391 crossref_primary_10_3390_s22134747 crossref_primary_10_1016_j_bspc_2021_103292 crossref_primary_10_3389_fpsyg_2023_1171215 crossref_primary_10_1155_2022_2014001 crossref_primary_10_3389_fpsyt_2019_00719 crossref_primary_10_1108_EL_10_2018_0202 crossref_primary_10_1016_j_dcn_2022_101077 crossref_primary_10_3390_app12052647 crossref_primary_10_1088_1741_2552_ac42b6 crossref_primary_10_1007_s00221_018_5427_8 crossref_primary_10_1038_s41598_023_45512_6 crossref_primary_10_3389_fneur_2021_644874 crossref_primary_10_1016_j_jenvp_2024_102308 crossref_primary_10_1088_1741_2552_ad6a8c crossref_primary_10_1002_dev_22249 crossref_primary_10_1007_s13246_018_0691_2 crossref_primary_10_1186_s40708_022_00167_3 crossref_primary_10_1016_j_jecp_2019_104758 crossref_primary_10_3390_s22197314 crossref_primary_10_1016_j_jneumeth_2015_01_030 crossref_primary_10_1044_2025_JSLHR_24_00836 crossref_primary_10_1016_j_neuropsychologia_2023_108589 crossref_primary_10_3389_fnins_2018_00513 crossref_primary_10_1177_10790632211024241 crossref_primary_10_3390_s20247040 crossref_primary_10_1016_j_bspc_2023_104927 crossref_primary_10_1038_s41598_022_17013_5 crossref_primary_10_1038_s42003_024_06439_4 crossref_primary_10_3390_app13042703 crossref_primary_10_1016_j_neuroimage_2019_02_070 crossref_primary_10_1097_MAO_0000000000004581 crossref_primary_10_1093_scan_nsaf036 crossref_primary_10_1007_s11571_016_9382_4 crossref_primary_10_3389_fnins_2022_801774 crossref_primary_10_3390_electronics14153106 crossref_primary_10_1155_2018_5081258 crossref_primary_10_1088_1741_2552_abd51f crossref_primary_10_3389_fnhum_2016_00193 crossref_primary_10_1016_j_cortex_2022_11_015 crossref_primary_10_1155_2021_6613105 crossref_primary_10_1016_j_heliyon_2024_e27198 crossref_primary_10_1155_2015_720450 crossref_primary_10_3390_brainsci13060969 crossref_primary_10_1016_j_clinph_2018_06_002 crossref_primary_10_1080_0144929X_2025_2504514 crossref_primary_10_1016_j_bandl_2024_105437 crossref_primary_10_1016_j_nbd_2024_106643 crossref_primary_10_1111_ejn_13897 crossref_primary_10_1523_JNEUROSCI_2004_22_2023 crossref_primary_10_3390_nu17152425 crossref_primary_10_3390_s24186103 crossref_primary_10_1038_s41598_017_14474_x crossref_primary_10_1080_1028415X_2020_1730614 crossref_primary_10_1088_1741_2552_aca8ce crossref_primary_10_3389_fnins_2021_660449 crossref_primary_10_1016_j_neuroimage_2020_117266 crossref_primary_10_3389_fnsys_2020_00049 crossref_primary_10_1016_j_neucli_2016_07_002 crossref_primary_10_1152_jn_00388_2019 crossref_primary_10_1016_j_learninstruc_2023_101870 crossref_primary_10_1016_j_ergon_2021_103159 crossref_primary_10_1093_scan_nsaf059 crossref_primary_10_1109_TBME_2014_2300164 crossref_primary_10_1016_j_bspc_2022_103790 crossref_primary_10_1155_2024_9967369 crossref_primary_10_1038_s44184_023_00038_7 crossref_primary_10_3390_s23020928 crossref_primary_10_1523_JNEUROSCI_0564_22_2022 crossref_primary_10_1016_j_jobe_2024_111644 crossref_primary_10_1038_s42003_025_08601_y crossref_primary_10_1109_TNSRE_2017_2678161 crossref_primary_10_1016_j_bspc_2021_103284 crossref_primary_10_1145_3427471 crossref_primary_10_3390_s21144885 crossref_primary_10_3389_fnhum_2018_00096 crossref_primary_10_1016_j_jneuroling_2021_101043 crossref_primary_10_1016_j_jneumeth_2018_06_014 crossref_primary_10_1016_j_neuroimage_2019_116054 crossref_primary_10_1017_S0305000920000501 crossref_primary_10_1080_10548408_2025_2505890 crossref_primary_10_1093_cercor_bhab471 crossref_primary_10_1111_psyp_12810 crossref_primary_10_1016_j_buildenv_2020_107223 crossref_primary_10_3389_fnsys_2020_00053 crossref_primary_10_1007_s13246_022_01135_1 crossref_primary_10_3390_e16126553 crossref_primary_10_1016_j_bpsc_2024_05_001 crossref_primary_10_1007_s12264_025_01375_7 crossref_primary_10_1371_journal_pcbi_1007065 crossref_primary_10_3389_fnhum_2022_852657 crossref_primary_10_3389_fnins_2021_566004 crossref_primary_10_1038_s41598_023_27528_0 crossref_primary_10_1016_j_dcn_2024_101493 crossref_primary_10_1016_j_bandl_2025_105610 crossref_primary_10_1155_2016_4562601 crossref_primary_10_1007_s10899_017_9693_3 crossref_primary_10_1007_s11760_021_01947_w crossref_primary_10_1111_desc_12782 crossref_primary_10_1007_s40708_017_0074_6 crossref_primary_10_3389_fninf_2021_720229 crossref_primary_10_1016_j_ijhcs_2023_103066 crossref_primary_10_1080_23273798_2023_2295499 crossref_primary_10_1093_cercor_bhab086 crossref_primary_10_1016_j_clinph_2021_08_019 crossref_primary_10_1152_jn_00650_2016 crossref_primary_10_1016_j_foodres_2021_110873 crossref_primary_10_3389_fnins_2023_1267901 crossref_primary_10_1016_j_neuroimage_2019_116117 crossref_primary_10_1016_j_concog_2021_103210 crossref_primary_10_1111_mice_12515 crossref_primary_10_1186_s13229_021_00425_x crossref_primary_10_1088_1741_2560_13_1_016018 crossref_primary_10_1016_j_dib_2019_104101 crossref_primary_10_1016_j_neures_2019_10_011 crossref_primary_10_1109_JAS_2020_1003450 crossref_primary_10_3389_fnagi_2021_680200 crossref_primary_10_3389_fnhum_2021_659410 crossref_primary_10_1109_JBHI_2019_2920381 crossref_primary_10_1038_s41598_024_57426_y crossref_primary_10_3389_fnhum_2023_1251690 crossref_primary_10_1080_2326263X_2014_912881 crossref_primary_10_1016_j_jneumeth_2021_109209 crossref_primary_10_1371_journal_pone_0289508 crossref_primary_10_1016_j_bspc_2023_105074 crossref_primary_10_1016_j_neuroimage_2021_118160 crossref_primary_10_3390_brainsci10100712 crossref_primary_10_1093_cercor_bhae241 crossref_primary_10_1038_s41598_020_75861_5 crossref_primary_10_3389_fncom_2022_803384 crossref_primary_10_1016_j_jneumeth_2015_02_025 crossref_primary_10_7717_peerj_4380 crossref_primary_10_1016_j_clinph_2023_12_133 crossref_primary_10_1016_j_bandl_2018_09_005 crossref_primary_10_1080_17470919_2019_1675758 crossref_primary_10_1016_j_neuroimage_2019_116361 crossref_primary_10_1080_10447318_2024_2358461 crossref_primary_10_1002_brb3_70020 crossref_primary_10_1038_s41598_022_05810_x crossref_primary_10_1088_1741_2552_ab7613 crossref_primary_10_1109_JPROC_2023_3286445 crossref_primary_10_1097_PR9_0000000000001251 crossref_primary_10_1016_j_neuroimage_2022_119305 crossref_primary_10_1088_1741_2552_aba87d crossref_primary_10_1111_ejn_15774 crossref_primary_10_1073_pnas_2427088122 crossref_primary_10_1080_17470919_2023_2208878 crossref_primary_10_1016_j_jneumeth_2021_109460 crossref_primary_10_1162_jocn_a_00734 crossref_primary_10_3390_brainsci11101312 crossref_primary_10_1016_j_buildenv_2021_108134 crossref_primary_10_1016_j_jneumeth_2022_109501 crossref_primary_10_1093_schbul_sbaa083 crossref_primary_10_1093_cercor_bhad297 crossref_primary_10_1016_j_jadr_2025_100891 crossref_primary_10_1080_17470919_2024_2358558 crossref_primary_10_1016_j_jneumeth_2014_01_027 crossref_primary_10_1109_TNSRE_2014_2375879 crossref_primary_10_1111_psyp_70087 crossref_primary_10_3390_app9235078 crossref_primary_10_1016_j_psychres_2023_115256 crossref_primary_10_1002_hbm_23938 crossref_primary_10_1007_s10111_020_00653_w crossref_primary_10_1073_pnas_2117000119 crossref_primary_10_1016_j_neucom_2020_04_144 crossref_primary_10_1109_ACCESS_2024_3360328 crossref_primary_10_1002_aur_2701 crossref_primary_10_1038_s42003_025_07805_6 crossref_primary_10_1093_cercor_bhac251 crossref_primary_10_1007_s11042_023_15653_x crossref_primary_10_3389_fnbeh_2023_1285773 crossref_primary_10_1016_j_neuroimage_2022_118991 crossref_primary_10_3389_fnint_2020_00021 crossref_primary_10_1016_j_crmeth_2023_100482 crossref_primary_10_1016_j_neuroimage_2022_118994 crossref_primary_10_1162_neco_a_01415 crossref_primary_10_3390_sym13122337 crossref_primary_10_1109_ACCESS_2013_2260791 crossref_primary_10_1016_j_neunet_2020_11_002 crossref_primary_10_1080_1028415X_2017_1347998 crossref_primary_10_1016_j_dcn_2022_101140 crossref_primary_10_1016_j_eswa_2022_118621 crossref_primary_10_1038_s41598_023_37524_z crossref_primary_10_1109_ACCESS_2020_3046993 crossref_primary_10_3389_fnins_2020_575521 crossref_primary_10_3758_s13414_023_02802_5 crossref_primary_10_3389_fnins_2022_950539 crossref_primary_10_1038_s41598_024_76046_0 crossref_primary_10_1016_j_bspc_2024_106022 crossref_primary_10_1016_j_xpro_2025_103682 crossref_primary_10_1016_j_cortex_2017_05_003 crossref_primary_10_1016_j_jad_2019_05_070 crossref_primary_10_1088_1741_2552_ac01a0 crossref_primary_10_1111_jsr_12679 crossref_primary_10_1016_j_bspc_2022_103942 crossref_primary_10_3389_fphys_2022_817239 crossref_primary_10_1080_10447318_2022_2108586 crossref_primary_10_1016_j_isci_2025_113109 crossref_primary_10_1038_s41398_020_01160_2 crossref_primary_10_1016_j_cognition_2021_104600 crossref_primary_10_3389_fnhum_2023_1126938 crossref_primary_10_1016_j_ijpsycho_2021_02_016 crossref_primary_10_1093_cercor_bhad360 crossref_primary_10_28978_nesciences_328851 crossref_primary_10_1016_j_neuroimage_2022_119624 crossref_primary_10_1038_s41598_024_61316_8 crossref_primary_10_1097_WNR_0000000000001640 crossref_primary_10_1016_j_neuroimage_2021_118578 crossref_primary_10_3389_fnhum_2023_1070404 crossref_primary_10_1016_j_ijpsycho_2018_01_003 crossref_primary_10_1088_1741_2560_11_3_036008 crossref_primary_10_1113_JP286639 crossref_primary_10_1080_17470919_2019_1674686 crossref_primary_10_1371_journal_pone_0210862 crossref_primary_10_1038_srep15890 crossref_primary_10_1111_epi_17897 crossref_primary_10_1523_JNEUROSCI_0861_21_2022 crossref_primary_10_2478_msr_2019_0016 crossref_primary_10_1016_j_dib_2022_108663 crossref_primary_10_1093_milmed_usaf224 crossref_primary_10_1088_1741_2552_ad788e crossref_primary_10_1152_jn_00003_2019 crossref_primary_10_1016_j_neunet_2017_01_005 crossref_primary_10_1016_j_asoc_2020_107028 crossref_primary_10_1016_j_biopsycho_2024_108775 crossref_primary_10_1016_j_bspc_2024_106613 crossref_primary_10_3390_s130506272 crossref_primary_10_1016_j_jneumeth_2021_109282 crossref_primary_10_1080_0954898X_2023_2263083 crossref_primary_10_1016_j_neucli_2017_10_059 crossref_primary_10_1002_aur_2992 crossref_primary_10_1038_s44222_024_00185_2 crossref_primary_10_3390_ijerph19074413 crossref_primary_10_3390_a17110477 crossref_primary_10_1016_j_jneumeth_2013_04_017 crossref_primary_10_1016_j_neuroimage_2023_119896 crossref_primary_10_1109_TCDS_2023_3338460 crossref_primary_10_1038_s41597_022_01524_x crossref_primary_10_1038_s44220_025_00410_w crossref_primary_10_1111_ejn_14398 crossref_primary_10_1162_nol_a_00002 crossref_primary_10_1111_ejn_15120 crossref_primary_10_1002_pne2_70001 crossref_primary_10_1155_2018_1350692 crossref_primary_10_1088_1741_2552_aacfdf crossref_primary_10_1088_1741_2552_ac1037 crossref_primary_10_1088_1741_2552_ad5c04 crossref_primary_10_1371_journal_pbio_3001713 crossref_primary_10_12688_f1000research_17029_1 crossref_primary_10_1038_s41386_023_01586_4 crossref_primary_10_1016_j_compbiomed_2024_108727 crossref_primary_10_3390_brainsci14030267 crossref_primary_10_7554_eLife_85980 crossref_primary_10_3389_fnbot_2021_773477 crossref_primary_10_1097_j_pain_0000000000002469 crossref_primary_10_1007_s12559_014_9282_z crossref_primary_10_1088_1741_2552_acc2e9 crossref_primary_10_1088_1741_2560_11_3_035013 crossref_primary_10_1007_s11571_022_09794_2 crossref_primary_10_1080_20445911_2019_1642898 crossref_primary_10_1007_s11571_017_9447_z crossref_primary_10_1016_j_cortex_2023_10_005 crossref_primary_10_1016_j_neuri_2023_100143 crossref_primary_10_1016_j_neuroimage_2025_121032 crossref_primary_10_1109_TBCAS_2025_3573027 crossref_primary_10_1016_j_artmed_2014_12_006 crossref_primary_10_3390_brainsci14030251 crossref_primary_10_1111_psyp_12290 crossref_primary_10_1016_j_ijpsycho_2018_07_002 crossref_primary_10_1177_13670069241285332 crossref_primary_10_1080_0361073X_2018_1449585 crossref_primary_10_1016_j_heliyon_2024_e38681 crossref_primary_10_1111_psyp_13557 crossref_primary_10_1016_j_nicl_2021_102746 crossref_primary_10_1109_TNNLS_2022_3174528 crossref_primary_10_1088_1741_2552_ac123f crossref_primary_10_3390_foods9121856 crossref_primary_10_3390_brainsci9120355 crossref_primary_10_3390_s23157006 crossref_primary_10_1016_j_dcn_2021_101024 crossref_primary_10_1016_j_dcn_2023_101302 crossref_primary_10_1162_imag_a_00566 crossref_primary_10_1080_17455030_2023_2187237 crossref_primary_10_1007_s00221_021_06128_2 crossref_primary_10_1002_hbm_26550 crossref_primary_10_1093_cercor_bhad079 crossref_primary_10_1016_j_apacoust_2025_110717 crossref_primary_10_1038_s41598_020_61909_z crossref_primary_10_1111_psyp_13566 crossref_primary_10_1080_01691864_2024_2369794 crossref_primary_10_1111_psyp_13321 crossref_primary_10_1038_s41598_024_68398_4 crossref_primary_10_1038_sdata_2018_291 crossref_primary_10_3389_fpsyg_2020_00760 crossref_primary_10_1002_hbm_70267 crossref_primary_10_1109_JSEN_2019_2906572 crossref_primary_10_3390_app11010015 crossref_primary_10_1093_cercor_bhaf025 crossref_primary_10_1007_s00221_017_4999_z crossref_primary_10_1093_cercor_bhad089 crossref_primary_10_1093_scan_nsaa071 crossref_primary_10_1016_j_neuroimage_2022_119586 crossref_primary_10_1523_JNEUROSCI_1849_22_2023 crossref_primary_10_1016_j_jneumeth_2020_108961 crossref_primary_10_1016_j_bandc_2019_103619 crossref_primary_10_1007_s11357_023_01022_x crossref_primary_10_3390_s16020241 crossref_primary_10_1002_hbm_25129 crossref_primary_10_1016_j_cortex_2019_12_027 crossref_primary_10_3390_app11010164 crossref_primary_10_1016_j_brainresbull_2024_111082 crossref_primary_10_1016_j_clinph_2021_06_034 crossref_primary_10_1109_TAFFC_2021_3137857 crossref_primary_10_1038_s41598_020_61119_7 crossref_primary_10_3389_fnhum_2016_00669 crossref_primary_10_3390_app12010389 crossref_primary_10_1038_s41598_024_55366_1 crossref_primary_10_1016_j_neuroimage_2022_119218 crossref_primary_10_1016_j_neuroimage_2020_116934 crossref_primary_10_1016_j_clinph_2021_05_011 crossref_primary_10_1007_s12311_018_0923_8 crossref_primary_10_1073_pnas_2502135122 crossref_primary_10_1162_jocn_a_02169 crossref_primary_10_3389_fpsyg_2021_721672 crossref_primary_10_1007_s11042_022_12887_z crossref_primary_10_1016_j_ijpsycho_2016_11_005 crossref_primary_10_1038_s41380_023_02337_z crossref_primary_10_1016_j_neuroimage_2024_120915 crossref_primary_10_1186_s40708_022_00173_5 crossref_primary_10_1016_j_isci_2025_112429 |
| Cites_doi | 10.1016/j.clinph.2009.01.015 10.1097/WNP.0b013e3180556926 10.1016/S0987-7053(00)00055-1 10.1016/j.neuroimage.2009.10.010 10.1016/j.neuroimage.2010.03.022 10.1016/S1388-2457(03)00093-2 10.1007/BF02512476 10.1016/j.clinph.2006.10.019 10.1016/0013-4694(72)90106-X 10.1007/s10439-008-9442-y 10.1016/j.clinph.2004.11.001 10.1109/TBME.2003.816076 10.1016/S0047-259X(03)00096-4 10.1016/j.jneumeth.2007.09.022 10.1016/j.neuroimage.2008.04.246 10.1016/j.clinph.2003.12.015 10.1016/j.compbiomed.2007.12.001 10.1016/S0167-8760(00)00088-X 10.1016/j.clinph.2005.12.013 |
| ContentType | Journal Article |
| Copyright | Winkler et al; licensee BioMed Central Ltd. 2011 2011 Winkler et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Copyright ©2011 Winkler et al; licensee BioMed Central Ltd. 2011 Winkler et al; licensee BioMed Central Ltd. |
| Copyright_xml | – notice: Winkler et al; licensee BioMed Central Ltd. 2011 – notice: 2011 Winkler et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. – notice: Copyright ©2011 Winkler et al; licensee BioMed Central Ltd. 2011 Winkler et al; licensee BioMed Central Ltd. |
| DBID | C6C AAYXX CITATION CGR CUY CVF ECM EIF NPM 3V. 7QG 7TK 7X7 7XB 88E 88G 8FI 8FJ 8FK ABUWG AFKRA AZQEC BENPR CCPQU DWQXO FYUFA GHDGH GNUQQ K9. M0S M1P M2M PHGZM PHGZT PIMPY PJZUB PKEHL PPXIY PQEST PQQKQ PQUKI PRINS PSYQQ Q9U 7X8 5PM DOA |
| DOI | 10.1186/1744-9081-7-30 |
| DatabaseName | Springer Nature OA Free Journals CrossRef Medline MEDLINE MEDLINE (Ovid) MEDLINE MEDLINE PubMed ProQuest Central (Corporate) Animal Behavior Abstracts Neurosciences Abstracts Health & Medical Collection (ProQuest) ProQuest Central (purchase pre-March 2016) Medical Database (Alumni Edition) Psychology Database (Alumni) Hospital Premium Collection Hospital Premium Collection (Alumni Edition) ProQuest Central (Alumni) (purchase pre-March 2016) ProQuest Central (Alumni) ProQuest Central UK/Ireland ProQuest Central Essentials - QC ProQuest Central ProQuest One ProQuest Central Health Research Premium Collection Health Research Premium Collection (Alumni) ProQuest Central Student ProQuest Health & Medical Complete (Alumni) ProQuest Health & Medical Collection Medical Database Psychology Database ProQuest Central Premium ProQuest One Academic (New) Publicly Available Content Database ProQuest Health & Medical Research Collection ProQuest One Academic Middle East (New) ProQuest One Health & Nursing ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Academic (retired) ProQuest One Academic UKI Edition ProQuest Central China ProQuest One Psychology ProQuest Central Basic MEDLINE - Academic PubMed Central (Full Participant titles) DOAJ Directory of Open Access Journals |
| DatabaseTitle | CrossRef MEDLINE Medline Complete MEDLINE with Full Text PubMed MEDLINE (Ovid) Publicly Available Content Database ProQuest One Psychology ProQuest Central Student ProQuest One Academic Middle East (New) ProQuest Central Essentials ProQuest Health & Medical Complete (Alumni) ProQuest Central (Alumni Edition) ProQuest One Community College ProQuest One Health & Nursing ProQuest Central China ProQuest Central ProQuest Health & Medical Research Collection Health Research Premium Collection Health and Medicine Complete (Alumni Edition) ProQuest Central Korea Health & Medical Research Collection ProQuest Central (New) ProQuest Medical Library (Alumni) ProQuest Central Basic ProQuest One Academic Eastern Edition ProQuest Hospital Collection Health Research Premium Collection (Alumni) ProQuest Psychology Journals (Alumni) Neurosciences Abstracts ProQuest Hospital Collection (Alumni) ProQuest Health & Medical Complete ProQuest Medical Library ProQuest Psychology Journals ProQuest One Academic UKI Edition Animal Behavior Abstracts ProQuest One Academic ProQuest One Academic (New) ProQuest Central (Alumni) MEDLINE - Academic |
| DatabaseTitleList | MEDLINE - Academic Publicly Available Content Database MEDLINE |
| Database_xml | – sequence: 1 dbid: DOA name: DOAJ Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 2 dbid: NPM name: PubMed url: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 3 dbid: PIMPY name: ProQuest Publicly Available Content url: http://search.proquest.com/publiccontent sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Medicine Anatomy & Physiology Psychology |
| EISSN | 1744-9081 |
| EndPage | 30 |
| ExternalDocumentID | oai_doaj_org_article_c3552bf403e743108d81ca6f360068f5 PMC3175453 2504066991 21810266 10_1186_1744_9081_7_30 |
| Genre | Research Support, Non-U.S. Gov't Journal Article Comparative Study |
| GroupedDBID | --- 0R~ 23N 2VQ 2WC 4.4 53G 5GY 5VS 6J9 7X7 88E 8FI 8FJ AAFWJ AAJSJ AASML ABDBF ABIVO ABUWG ACGFO ACGFS ACPRK ACUHS ADBBV ADRAZ ADUKV AENEX AFKRA AFPKN AHBYD AHMBA AHSBF AHYZX ALMA_UNASSIGNED_HOLDINGS AMKLP AMTXH AOIJS AZQEC BAPOH BAWUL BCNDV BENPR BFQNJ BMC BPHCQ BVXVI C1A C6C CCPQU CS3 DIK DU5 DWQXO E3Z EBD EBLON EBS EJD ESX F5P FYUFA GNUQQ GROUPED_DOAJ GX1 H13 HMCUK HYE IAO IHR INH INR IPNFZ IPY ISR ITC KQ8 M1P M2M M48 M~E O5R O5S OK1 OVT P2P PGMZT PHGZM PHGZT PIMPY PJZUB PPXIY PQQKQ PROAC PSQYO PSYQQ PUEGO RBZ RIG RNS ROL RPM RSV SBL SOJ TR2 TUS UKHRP WOQ WOW XSB ~8M AAYXX AFFHD CITATION ALIPV CGR CUY CVF ECM EIF NPM 3V. 7QG 7TK 7XB 8FK K9. PKEHL PQEST PQUKI PRINS Q9U 7X8 5PM |
| ID | FETCH-LOGICAL-c689t-3d10f1f10f322cb78283c1cba8a1afca08494b801b465c941726b2881c456eaf3 |
| IEDL.DBID | RSV |
| ISICitedReferencesCount | 586 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000294944600001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 1744-9081 |
| IngestDate | Tue Oct 14 18:55:40 EDT 2025 Tue Nov 04 01:46:43 EST 2025 Fri Sep 05 12:02:52 EDT 2025 Tue Oct 07 05:24:26 EDT 2025 Thu Apr 03 06:51:40 EDT 2025 Sat Nov 29 03:26:06 EST 2025 Tue Nov 18 22:16:43 EST 2025 Sat Sep 06 07:27:15 EDT 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 1 |
| Keywords | Source Component Common Spatial Pattern Motor Imagery Principal Component Analysis Component Independent Component Analysis |
| Language | English |
| License | This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c689t-3d10f1f10f322cb78283c1cba8a1afca08494b801b465c941726b2881c456eaf3 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 ObjectType-Article-2 ObjectType-Feature-1 content type line 23 |
| OpenAccessLink | https://link.springer.com/10.1186/1744-9081-7-30 |
| PMID | 21810266 |
| PQID | 902301062 |
| PQPubID | 55045 |
| PageCount | 1 |
| ParticipantIDs | doaj_primary_oai_doaj_org_article_c3552bf403e743108d81ca6f360068f5 pubmedcentral_primary_oai_pubmedcentral_nih_gov_3175453 proquest_miscellaneous_892947567 proquest_journals_902301062 pubmed_primary_21810266 crossref_citationtrail_10_1186_1744_9081_7_30 crossref_primary_10_1186_1744_9081_7_30 springer_journals_10_1186_1744_9081_7_30 |
| PublicationCentury | 2000 |
| PublicationDate | 2011-08-02 |
| PublicationDateYYYYMMDD | 2011-08-02 |
| PublicationDate_xml | – month: 08 year: 2011 text: 2011-08-02 day: 02 |
| PublicationDecade | 2010 |
| PublicationPlace | London |
| PublicationPlace_xml | – name: London – name: England |
| PublicationTitle | Behavioral and brain functions |
| PublicationTitleAbbrev | Behav Brain Funct |
| PublicationTitleAlternate | Behav Brain Funct |
| PublicationYear | 2011 |
| Publisher | BioMed Central Springer Nature B.V BMC |
| Publisher_xml | – name: BioMed Central – name: Springer Nature B.V – name: BMC |
| References | 10.1186/1744-9081-7-30-B10 10.1186/1744-9081-7-30-B21 - 10.1186/1744-9081-7-30-B6 10.1186/1744-9081-7-30-B13 10.1186/1744-9081-7-30-B35 10.1186/1744-9081-7-30-B5 10.1186/1744-9081-7-30-B12 10.1186/1744-9081-7-30-B4 10.1186/1744-9081-7-30-B11 10.1186/1744-9081-7-30-B33 10.1186/1744-9081-7-30-B3 10.1186/1744-9081-7-30-B2 10.1186/1744-9081-7-30-B17 10.1186/1744-9081-7-30-B27 10.1186/1744-9081-7-30-B26 10.1186/1744-9081-7-30-B19 10.1186/1744-9081-7-30-B8 20877434 - Front Neurosci. 2010 Sep 07;4:null 12948787 - Clin Neurophysiol. 2003 Sep;114(9):1580-93 15066548 - Clin Neurophysiol. 2004 May;115(5):1220-32 20303409 - Neuroimage. 2010 Jul 15;51(4):1303-9 17271830 - Conf Proc IEEE Eng Med Biol Soc. 2004;2:925-8 8182960 - Med Biol Eng Comput. 1994 Jan;32(1):35-42 19833218 - Neuroimage. 2010 Feb 1;49(3):2416-32 17545826 - J Clin Neurophysiol. 2007 Jun;24(3):232-43 19964963 - Conf Proc IEEE Eng Med Biol Soc. 2009;2009:2470-3 18583157 - Neuroimage. 2008 Aug 15;42(2):726-38 19345611 - Clin Neurophysiol. 2009 May;120(5):868-77 16646851 - Stat Appl Genet Mol Biol. 2005;4:Article32 18288259 - Comput Intell Neurosci. 2007;:82069 15792897 - Clin Neurophysiol. 2005 Apr;116(4):878-85 18222418 - Comput Biol Med. 2008 Mar;38(3):348-60 16458594 - Clin Neurophysiol. 2006 Apr;117(4):912-27 20600976 - Neuroimage. 2011 May 15;56(2):814-25 20636297 - Psychophysiology. 2011 Feb;48(2):229-40 18228142 - Ann Biomed Eng. 2008 Mar;36(3):467-75 17169606 - Clin Neurophysiol. 2007 Mar;118(3):480-94 10740792 - Neurophysiol Clin. 2000 Feb;30(1):5-19 12943278 - IEEE Trans Biomed Eng. 2003 Sep;50(9):1108-16 15032997 - Psychophysiology. 2004 Mar;41(2):313-25 18031824 - J Neurosci Methods. 2008 Jan 15;167(1):82-90 10731767 - Psychophysiology. 2000 Mar;37(2):163-78 4121520 - Electroencephalogr Clin Neurophysiol. 1972 Jun;32(6):701-5 |
| References_xml | – ident: 10.1186/1744-9081-7-30-B19 doi: 10.1016/j.clinph.2009.01.015 – ident: - doi: 10.1097/WNP.0b013e3180556926 – ident: 10.1186/1744-9081-7-30-B6 doi: 10.1016/S0987-7053(00)00055-1 – ident: 10.1186/1744-9081-7-30-B12 doi: 10.1016/j.neuroimage.2009.10.010 – ident: 10.1186/1744-9081-7-30-B33 doi: 10.1016/j.neuroimage.2010.03.022 – ident: 10.1186/1744-9081-7-30-B4 doi: 10.1016/S1388-2457(03)00093-2 – ident: 10.1186/1744-9081-7-30-B26 doi: 10.1007/BF02512476 – ident: 10.1186/1744-9081-7-30-B5 doi: 10.1016/j.clinph.2006.10.019 – ident: 10.1186/1744-9081-7-30-B35 doi: 10.1016/0013-4694(72)90106-X – ident: 10.1186/1744-9081-7-30-B11 doi: 10.1007/s10439-008-9442-y – ident: 10.1186/1744-9081-7-30-B3 doi: 10.1016/j.clinph.2004.11.001 – ident: 10.1186/1744-9081-7-30-B17 doi: 10.1109/TBME.2003.816076 – ident: - doi: 10.1016/S0047-259X(03)00096-4 – ident: 10.1186/1744-9081-7-30-B2 doi: 10.1016/j.jneumeth.2007.09.022 – ident: 10.1186/1744-9081-7-30-B27 doi: 10.1016/j.neuroimage.2008.04.246 – ident: 10.1186/1744-9081-7-30-B13 doi: 10.1016/j.clinph.2003.12.015 – ident: 10.1186/1744-9081-7-30-B10 doi: 10.1016/j.compbiomed.2007.12.001 – ident: 10.1186/1744-9081-7-30-B8 doi: 10.1016/S0167-8760(00)00088-X – ident: 10.1186/1744-9081-7-30-B21 doi: 10.1016/j.clinph.2005.12.013 – reference: 17169606 - Clin Neurophysiol. 2007 Mar;118(3):480-94 – reference: 12943278 - IEEE Trans Biomed Eng. 2003 Sep;50(9):1108-16 – reference: 20600976 - Neuroimage. 2011 May 15;56(2):814-25 – reference: 18583157 - Neuroimage. 2008 Aug 15;42(2):726-38 – reference: 19345611 - Clin Neurophysiol. 2009 May;120(5):868-77 – reference: 17545826 - J Clin Neurophysiol. 2007 Jun;24(3):232-43 – reference: 4121520 - Electroencephalogr Clin Neurophysiol. 1972 Jun;32(6):701-5 – reference: 18288259 - Comput Intell Neurosci. 2007;:82069 – reference: 10740792 - Neurophysiol Clin. 2000 Feb;30(1):5-19 – reference: 19964963 - Conf Proc IEEE Eng Med Biol Soc. 2009;2009:2470-3 – reference: 17271830 - Conf Proc IEEE Eng Med Biol Soc. 2004;2:925-8 – reference: 18228142 - Ann Biomed Eng. 2008 Mar;36(3):467-75 – reference: 16458594 - Clin Neurophysiol. 2006 Apr;117(4):912-27 – reference: 15066548 - Clin Neurophysiol. 2004 May;115(5):1220-32 – reference: 20636297 - Psychophysiology. 2011 Feb;48(2):229-40 – reference: 8182960 - Med Biol Eng Comput. 1994 Jan;32(1):35-42 – reference: 16646851 - Stat Appl Genet Mol Biol. 2005;4:Article32 – reference: 18222418 - Comput Biol Med. 2008 Mar;38(3):348-60 – reference: 12948787 - Clin Neurophysiol. 2003 Sep;114(9):1580-93 – reference: 10731767 - Psychophysiology. 2000 Mar;37(2):163-78 – reference: 18031824 - J Neurosci Methods. 2008 Jan 15;167(1):82-90 – reference: 15792897 - Clin Neurophysiol. 2005 Apr;116(4):878-85 – reference: 20303409 - Neuroimage. 2010 Jul 15;51(4):1303-9 – reference: 15032997 - Psychophysiology. 2004 Mar;41(2):313-25 – reference: 20877434 - Front Neurosci. 2010 Sep 07;4:null – reference: 19833218 - Neuroimage. 2010 Feb 1;49(3):2416-32 |
| SSID | ssj0038422 |
| Score | 2.4973297 |
| Snippet | Background
Artifacts contained in EEG recordings hamper both, the visual interpretation by experts as well as the algorithmic processing and analysis (e.g. for... Artifacts contained in EEG recordings hamper both, the visual interpretation by experts as well as the algorithmic processing and analysis (e.g. for... Abstract Background: Artifacts contained in EEG recordings hamper both, the visual interpretation by experts as well as the algorithmic processing and analysis... Abstract Background Artifacts contained in EEG recordings hamper both, the visual interpretation by experts as well as the algorithmic processing and analysis... |
| SourceID | doaj pubmedcentral proquest pubmed crossref springer |
| SourceType | Open Website Open Access Repository Aggregation Database Index Database Enrichment Source Publisher |
| StartPage | 30 |
| SubjectTerms | Adult Aged Behavioral Therapy Biomedical and Life Sciences Biomedicine Electroencephalography - classification Electroencephalography - methods Evoked Potentials, Auditory - physiology Humans Male Methodology Methods Middle Aged Neurology Neurosciences Psychiatry Reaction Time - physiology Signal Processing, Computer-Assisted - instrumentation Spectrum allocation Studies User-Computer Interface Young Adult |
| SummonAdditionalLinks | – databaseName: DOAJ Directory of Open Access Journals dbid: DOA link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1Lb9QwEB6hCqFeUNnyCAXkAwIuVuPE6zjHpdoCElSIlyou1tixYSWarbq7SP33jJ0HLBRx4ZJDbCWjmbHnm3jyDcBjjYWtEcl5G6y5nGLg6H3g3pN7YS1Qpf4pn15XJyf69LR--0urr1gT1tEDd4o7dBQQCxtkXvoY7HLdaOFQhVLFvxtCYi8l1DMkU90eXGqZzg8IbkteU9Tr6RqFVofjPU4C5lvhKLH2XwU1_6yY_O3YNEWj4z242cNINuvEvwXXfDuB_VlLKfTZJXvCUmFn-mI-gRtv-vPzCeyO-93lPnyebWh2ZGxlqTVmLBpKdmLLkJ4cf3rY0FteHc143DeWbay6YARzx2H2zp8tyVnZomXz-Qv2fvElEjLfho_H8w9HL3nfaoE7pes1WUrkQQS60AJ3lmCDLp1wFjUKDA5zLWtpKZpZqaaulgR7lC00mYIAmMdQ3oGdlqS4B4wSKh0atBQSLWGxCkUjsKgaUQRKt5syAz5o3Liehzy2w_hmUj6ilYkWMtFCpjJlnsHTcf55x8Dx15nPowHHWZE5O90gfzK9P5l_-VMGB4P5Tb-cV_QKytQoeS4yYOMorcN4uIKtX25WRhPOlNVUVRnc7XxllCOiKEp1VQbVlhdtCbo90i6-JqrviO7klHT2bPC3n0JdrYT7_0MJB7A7fDvPiwews77Y-Idw3X1fL1YXj9JS-wGjeSgT priority: 102 providerName: Directory of Open Access Journals – databaseName: ProQuest Central dbid: BENPR link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1Lb9QwEB7BFqFeeGyBhgLyAQGXqHHidZwT2lZbQIJVVR6quFiOE5eVaNLuA6n_nhmvE7RQuHDZQ2xlJ5nxzDf25BuA58qkZWEMGm9liliMjItNXbu4rtG8TMGN9P1TvrzPp1N1eloch9qcRSir7Hyid9RVa2mPfL8gsIz5S_r64jKmplF0uBo6aNyELSIqEwPYOphMj086V5wpkaaBqZEruY_oW8QFBsEYZUs2IpEn7L8OZf5ZLPnbiakPREd3__MR7sGdgEDZeG0y9-FG3QxhZ9xg9n1-xV4wXxPqN9uHcPtDOHofwnbvKq924Ot4hbOJ7JX5rppUb-RVzFrn70zfS6zwX94djmNyOW1DBRsMEXI_zE7q8xbtnM0aNpm8YR9nZ8Tl_AA-H00-Hb6NQ5eG2EpVLFHJPHHc4Q_6Blsi4lCZ5bY0ynDjrEmUKESJgbAUcmQLgYhJlqlS3CJ2q43LHsKgQSl2gWEuplxlSoymJcK43PCKmzSveOowU6-yCOJOY9oGCnPqpPFd-1RGSU0a1qRhnessieBlP_9iTd7x15kHZAD9LCLd9hfa-ZkOa1hbxGZp6USS1YS7ElXhQxjpMkkf2rhRBHud3nXwBAvdKz0C1o_iEqZzGdPU7WqhFUJUkY9kHsGjta31chAAwyxZRpBvWOGGoJsjzeybZwknYChG-M5edfb6S6jrX8Ljf8q_B9vdfnqSPoHBcr6qn8It-2M5W8yfhZX3E6zeNeU priority: 102 providerName: ProQuest |
| Title | Automatic Classification of Artifactual ICA-Components for Artifact Removal in EEG Signals |
| URI | https://link.springer.com/article/10.1186/1744-9081-7-30 https://www.ncbi.nlm.nih.gov/pubmed/21810266 https://www.proquest.com/docview/902301062 https://www.proquest.com/docview/892947567 https://pubmed.ncbi.nlm.nih.gov/PMC3175453 https://doaj.org/article/c3552bf403e743108d81ca6f360068f5 |
| Volume | 7 |
| WOSCitedRecordID | wos000294944600001&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: PRVADU databaseName: Open Access: BioMedCentral Open Access Titles customDbUrl: eissn: 1744-9081 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0038422 issn: 1744-9081 databaseCode: RBZ dateStart: 20050101 isFulltext: true titleUrlDefault: https://www.biomedcentral.com/search/ providerName: BioMedCentral – providerCode: PRVAON databaseName: DOAJ Directory of Open Access Journals customDbUrl: eissn: 1744-9081 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0038422 issn: 1744-9081 databaseCode: DOA dateStart: 20050101 isFulltext: true titleUrlDefault: https://www.doaj.org/ providerName: Directory of Open Access Journals – providerCode: PRVHPJ databaseName: ROAD: Directory of Open Access Scholarly Resources customDbUrl: eissn: 1744-9081 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0038422 issn: 1744-9081 databaseCode: M~E dateStart: 20050101 isFulltext: true titleUrlDefault: https://road.issn.org providerName: ISSN International Centre – providerCode: PRVPQU databaseName: Health & Medical Collection customDbUrl: eissn: 1744-9081 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0038422 issn: 1744-9081 databaseCode: 7X7 dateStart: 20090101 isFulltext: true titleUrlDefault: https://search.proquest.com/healthcomplete providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Central customDbUrl: eissn: 1744-9081 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0038422 issn: 1744-9081 databaseCode: BENPR dateStart: 20090101 isFulltext: true titleUrlDefault: https://www.proquest.com/central providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Publicly Available Content customDbUrl: eissn: 1744-9081 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0038422 issn: 1744-9081 databaseCode: PIMPY dateStart: 20090101 isFulltext: true titleUrlDefault: http://search.proquest.com/publiccontent providerName: ProQuest – providerCode: PRVPQU databaseName: Psychology Database customDbUrl: eissn: 1744-9081 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0038422 issn: 1744-9081 databaseCode: M2M dateStart: 20090101 isFulltext: true titleUrlDefault: https://www.proquest.com/psychology providerName: ProQuest – providerCode: PRVAVX databaseName: SpringerLINK Contemporary 1997-Present customDbUrl: eissn: 1744-9081 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0038422 issn: 1744-9081 databaseCode: RSV dateStart: 20051201 isFulltext: true titleUrlDefault: https://link.springer.com/search?facet-content-type=%22Journal%22 providerName: Springer Nature |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3db9MwED-NDaG98NEBC4PKDwh4iUgcx3Eeu6mDSbSqOpgKL5bj2KMSS6Z-IO2_5-wmQWVDghc_2JfkYt_5fmefzwCvhaJFrhQKb6nykKXKhsoYGxqD4qXyWHF_f8rFp2w8FrNZPtmBqD0L46Pd2y1JP1N7tRb8PUJnFuZowUJ8MTrpe2jqhFPF6flFO_cmglHapGa8_cyW6fEZ-u-ClbejI__YIvWW5_TR__P8GB42KJMMNmLxBHZM1YODQYUe9tUNeUN83KdfUO_Bg1Gzvd6D_W46vDmAb4M1UruErsTfnOliivwwktr6N7szEWv8ytnJIHTTSl25oAyCKLhrJlNzVaMsk3lFhsMP5Hx-6fI1P4Uvp8PPJx_D5iaGUHORr3Ag48jGFgvUf10gqhCJjnWhhIqV1SoSLGcFGruC8VTnDFERL6gQsUZ8ZpRNnsFuhVwcAkF_S9hSFWgxC4RqmYrLWNGsjKlFb7xMAgjbQZK6SVPubsv4Ib27Irh0nSpdp8pMJlEAbzv6602Cjr9SHrsx76hcYm1fUS8uZaOnUiP-ooVlUWIctopEiT-huE24O0xj0wCOWomRjbYv8RPoyKFvTQMgXSuqqdt7UZWp10spEIayLOVZAM834tXx4UAWesI8gGxL8LYY3W6p5t99JnAH_liKffauFb_fTN3dCS_-nfQI9tsF9Ii-hN3VYm1ewX39czVfLvpwL5tlvhR92DsejifTvl_QwHJER1g3ORtNvva9fv4C3rsvXQ |
| linkProvider | Springer Nature |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1Lb9QwEB6VgqAXHlseoTx84HWJmjjZxDkgtMCWVt2uELRoxcU4jl1WoknZB2h_FP-RGW8StFC49cAlh7WVTLzfzHwTj2cAHgnF80wpBG-hMj_uKusrY6xvDMJLZaFKXP-UD4N0OBSjUfZ2DX40Z2EorbKxic5QF5Wmb-TbGZFljF_4i9OvPjWNos3VpoPGEhX7ZvEdI7bp873X-Pc-5nynf_hq16-bCvg6EdkMZQoDG1q8IJR1jg5SRDrUuRIqVFarQMRZnKPdzuOkq7MYHXyScyFCjVTDKBvhfS_ARTTjKWWQpaM2votEzHldFzIUyTZy_djP0OX6uBLBit9z7QHO4rR_pmb-tj_r3N7Otf9swa7D1Zpfs95SIW7Amik7sNkr1aw6WbAnzGW8uq2EDlw-qBMLOrDROoLFJnzszXE2lbJlrmcoZVM5ALPKujvTaZA5PgXl98mgViWlozDk_-0we2dOKtRiNi5Zv_-GvR8fU6Xqm3B0Lm9_C9ZLlOIOMIw0hS1UjlwhR5KaqrAIFU-LkNtUiCLywG8QInVdoJ36hHyRLlATiSRESUKUTGUUePC0nX-6LE3y15kvCXDtLCop7n6oJseytlBSI_PkuY2DyBCrDESBL6ESGyV0jMh2PdhqcCZrOzeVLcg8YO0oGijadVKlqeZTKZCAx2k3ST24vcR2KwfRywAZogfpCupXBF0dKcefXQ10or1xF9fsWaMfv4Q6exHu_lP-h3Bl9_BgIAd7w_0t2Gh2DgJ-D9Znk7m5D5f0t9l4OnngdJ7Bp_PWmZ-kn5Bo |
| linkToPdf | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1Lb9QwEB5VBVW98NhCCeXhAwIuUfNwHEfispRdqCirikJVcbEcxy4r0aTaB1L_PTPeJGhpkZC45BBP4ok9nvkmHs8AvJA6KQutUXgrXYQ80y7U1rrQWhQvXcRa-Popp0f5ZCLPzorjDXjTnYXx0e7dluTqTANlaaoX-5eVWy1xKfYRRvOwQGsWYifosN_iVDCIfPWT004Pp5InSZum8foza2bIZ-u_CWJej5T8Y7vUW6Hx3f_j_x7cadEnG67E5T5s2HoAO8MaPe-LK_aS-XhQ_6N9AFuf2m33AWz3avJqB74Nl0hNiV6Zr6hJsUZ-elnj_JvprMQSezk8GIakbpqagjUYouO-mX22Fw3KOJvWbDR6z06m55TH-QF8HY--HHwI2woNoRGyWOAEx5GLHV5QL5gS0YZMTWxKLXWsndGR5AUv0QiWXGSm4IiWRJlIGRvEbVa79CFs1sjFI2Doh0lX6RItaYkQLtdxFeskr-LEoZdepQGE3YQp06YvpyoaP5R3Y6RQNKiKBlXlKo0CeNXTX64Sd_yV8i3Nf09FCbf9jWZ2rtr1qwzisqR0PEotYa5IVvgRWrhU0CEblwWw10mParXAHLtABw997iQA1rfi8qU9GV3bZjlXEuEpzzORB7C7ErWeDwJf6CGLAPI1IVxjdL2lnn73GcIJFPIMx-x1J4q_mbp5EB7_O-lz2Dp-N1ZHh5OPe7Dd_WOPkiewuZgt7VO4bX4upvPZM78cfwEPwDQo |
| 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=Automatic+Classification+of+Artifactual+ICA-Components+for+Artifact+Removal+in+EEG+Signals&rft.jtitle=Behavioral+and+brain+functions&rft.au=Winkler%2C+Irene&rft.au=Haufe%2C+Stefan&rft.au=Tangermann%2C+Michael&rft.date=2011-08-02&rft.pub=BioMed+Central&rft.eissn=1744-9081&rft.volume=7&rft.issue=1&rft_id=info:doi/10.1186%2F1744-9081-7-30&rft.externalDocID=10_1186_1744_9081_7_30 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1744-9081&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1744-9081&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1744-9081&client=summon |