{"id":"W3204409915","doi":"10.1109/icassp43922.2022.9746528","title":"Holistic Semi-Supervised Approaches for EEG Representation Learning","year":2022,"lang":"en","type":"article","venue":"ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Electroencephalography; Artificial intelligence; Computer science; Machine learning; Class (philosophy); Task (project management); Supervised learning; Representation (politics); Pattern recognition (psychology); Semi-supervised learning; Field (mathematics); Artificial neural network; Mathematics; Psychology; Engineering","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0006718413,0.0003986074,0.0003968433,0.000369766,0.001127471,0.0007531325,0.0009138744,0.0001060819,0.001081649],"category_scores_gemma":[0.0006310113,0.0004089327,0.0001438135,0.000374825,0.0002387758,0.0003916482,0.0003601942,0.001068224,0.00001954526],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002156988,"about_ca_system_score_gemma":0.0002227119,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002166477,"about_ca_topic_score_gemma":0.000002881581,"domain_scores_codex":[0.9960091,0.000318741,0.0006206596,0.001211,0.001301614,0.000538851],"domain_scores_gemma":[0.9981005,0.0007838277,0.0004255133,0.0002455681,0.0002507256,0.0001938554],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001000927,0.0007130817,0.0003473763,0.000223195,0.0000875008,0.0002234543,0.001909136,0.03875022,0.7621747,0.006365764,0.005784707,0.18242],"study_design_scores_gemma":[0.001101465,0.0006915943,0.0001121329,0.00008652265,0.00005294393,0.0001905444,0.002675404,0.9611725,0.02322904,0.007125846,0.003003023,0.0005589654],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.722819,0.0003128093,0.2457584,0.007410401,0.003902979,0.001785099,0.001078926,0.0006787478,0.01625363],"genre_scores_gemma":[0.9917665,0.00006891936,0.001672175,0.0009684892,0.0004381579,0.000249392,0.0001356483,0.00005980392,0.004640979],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9224223,"threshold_uncertainty_score":0.9998363,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1683268527922888,"score_gpt":0.3447223670880592,"score_spread":0.1763955142957704,"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."}}