{"id":"W2951443289","doi":"10.48550/arxiv.1403.2001","title":"EEG Compression of Scalp Recordings based on Dipole Fitting","year":2014,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Lossless compression; Electroencephalography; Computer science; Compression (physics); Smoothness; Data compression; Dipole; Algorithm; Artificial intelligence; Pattern recognition (psychology); Mathematics; Physics; Mathematical analysis; Psychology; Neuroscience","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0002266504,0.0005890477,0.0003626024,0.0005462061,0.0001336449,0.0003727011,0.0004140597,0.0004557449,0.001572303],"category_scores_gemma":[0.001138645,0.0001254743,0.0003646453,0.000819709,0.0002847549,0.0005892641,0.0004917019,0.000483079,0.0005623798],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000121278,"about_ca_system_score_gemma":0.0001344673,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002897628,"about_ca_topic_score_gemma":0.0002798199,"domain_scores_codex":[0.9997972,0.00004630575,0.0000132351,0.00003990461,0.00009025748,0.00001319553],"domain_scores_gemma":[0.9996858,0.0001321192,0.0000315223,0.00007340983,0.00006681612,0.00001034007],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005600123,0.00007211048,0.001065525,0.0003218232,0.00006844683,0.0006266734,0.0001959761,0.04113023,0.3586476,0.01180763,0.002598152,0.5829058],"study_design_scores_gemma":[0.00007249408,0.0004346465,0.004468736,0.00005203429,0.00008357485,0.002940227,0.00007405331,0.682344,0.2843732,0.008161067,0.01692832,0.0000676815],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02353093,0.0005681631,0.9739884,0.0001086299,0.0001086506,0.0000512263,0.0001031385,0.0006241382,0.0009167389],"genre_scores_gemma":[0.3594858,0.002339791,0.6335514,0.0001146014,0.0003152756,0.0001162671,0.0006531433,0.0002302959,0.003193519],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001572303,"threshold_uncertainty_score":0.005259931,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07618340703538731,"score_gpt":0.210478926766314,"score_spread":0.1342955197309267,"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."}}