{"id":"W2018055379","doi":"10.1109/ccece.2013.6567777","title":"Electroencephalogram as a mechanism for human communication","year":2013,"lang":"en","type":"article","venue":"","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Regina","funders":"","keywords":"Electroencephalography; Computer science; Set (abstract data type); Filter (signal processing); Mechanism (biology); Data set; Component (thermodynamics); Artificial intelligence; Human communication; Speech recognition; Pattern recognition (psychology); Machine learning; Psychology; Computer vision; Communication; Neuroscience","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001091271,0.0004161147,0.0002434753,0.001138343,0.0002138388,0.000882357,0.0004800144,0.000544031,0.001333157],"category_scores_gemma":[0.005806142,0.0001195865,0.0002195808,0.0006798694,0.001045603,0.00119435,0.0004108072,0.0003155991,0.0002792261],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002460646,"about_ca_system_score_gemma":0.0001976937,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004426012,"about_ca_topic_score_gemma":0.0002375071,"domain_scores_codex":[0.9989146,0.0005949028,0.00003537825,0.00009710754,0.0003353024,0.00002275981],"domain_scores_gemma":[0.9985679,0.0009274709,0.0001478757,0.000137131,0.0001858212,0.00003376128],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0008969081,0.0001330821,0.01825805,0.001021661,0.000358165,0.00070064,0.0007939375,0.07776835,0.1739899,0.1168041,0.003100196,0.6061751],"study_design_scores_gemma":[0.0002610317,0.002859662,0.07691244,0.0005730188,0.0003257915,0.005474564,0.001021605,0.5808696,0.113648,0.1682968,0.04939928,0.0003582869],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1737974,0.004746831,0.8030441,0.0006088534,0.0003115926,0.0001798082,0.0002653104,0.0008995797,0.01614651],"genre_scores_gemma":[0.8617625,0.001274732,0.135446,0.00007118213,0.000103049,0.00009763667,0.00007829617,0.00004505348,0.00112156],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001333157,"threshold_uncertainty_score":0.005771279,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03402277975464196,"score_gpt":0.3068043360108999,"score_spread":0.2727815562562579,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}