{"id":"W2108774690","doi":"10.1109/titb.2011.2171703","title":"A Channel Differential EZW Coding Scheme for EEG Data Compression","year":2011,"lang":"en","type":"article","venue":"IEEE Transactions on Information Technology in Biomedicine","topic":"Advanced Data Compression Techniques","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Electroencephalography; Cluster analysis; Wavelet; Data compression; Channel (broadcasting); Scalability; Coding (social sciences); Differential coding; Encoding (memory); SIGNAL (programming language); Compression (physics); Pattern recognition (psychology); Speech recognition; Algorithm; Artificial intelligence; Decoding methods; Mathematics; Statistics; Psychology; Telecommunications","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":[0.0001927629,0.0003844053,0.0002399368,0.0004130131,0.0001841484,0.0002214637,0.0005195311,0.0003141311,0.001446464],"category_scores_gemma":[0.0006495784,0.00007729699,0.0001800076,0.0005966776,0.0002585211,0.0005809587,0.0004937245,0.0004251226,0.0003770694],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001935336,"about_ca_system_score_gemma":0.0002779878,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000527152,"about_ca_topic_score_gemma":0.001006865,"domain_scores_codex":[0.9998523,0.00002511163,0.000009464852,0.00002130746,0.00007935336,0.00001247038],"domain_scores_gemma":[0.9998261,0.00005070226,0.00001602838,0.00004862401,0.00005068055,0.000007982669],"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":[0.0003186026,0.0000665348,0.0003490826,0.0001539714,0.00002356594,0.0001731624,0.00008375329,0.03256924,0.2401117,0.0415048,0.003071392,0.6815742],"study_design_scores_gemma":[0.00007735484,0.0002934039,0.001245526,0.00004571179,0.00004226469,0.000817657,0.00004382519,0.7761807,0.188869,0.01241326,0.01992474,0.00004652065],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02035976,0.0004190864,0.9772992,0.0001402019,0.00008460593,0.00005912893,0.00008463078,0.0002363527,0.001317155],"genre_scores_gemma":[0.3089286,0.0009407211,0.6837934,0.0002198551,0.0001251514,0.0001924279,0.000493696,0.00005188904,0.005254204],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001446464,"threshold_uncertainty_score":0.004838884,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05245130010806143,"score_gpt":0.299531608179741,"score_spread":0.2470803080716796,"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."}}