{"id":"W2097326220","doi":"10.1109/icassp.2005.1416311","title":"A Hybrid Genetic Algorithm Approach for Improving the Performance of the LF-ASD Brain Computer Interface","year":2006,"lang":"en","type":"article","venue":"","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":34,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Genetic algorithm; Interface (matter); Brain–computer interface; Algorithm; Parallel computing; Machine learning; Neuroscience; Psychology; Electroencephalography","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":[],"consensus_categories":[],"category_scores_codex":[0.0002435761,0.000181848,0.000168735,0.00003739655,0.0002383276,0.000104501,0.0009791494,0.00003364705,0.000009702791],"category_scores_gemma":[0.00002614953,0.00009255402,0.0001309979,0.0001455371,0.0002111046,0.000115244,0.0003373436,0.0001602389,0.000004879849],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002106601,"about_ca_system_score_gemma":0.00003137502,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006397696,"about_ca_topic_score_gemma":0.000002233767,"domain_scores_codex":[0.9985948,0.0001028817,0.0003252008,0.000409776,0.0002383376,0.0003289753],"domain_scores_gemma":[0.9989455,0.0003682496,0.0001517089,0.0004631462,0.00004553154,0.00002583249],"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":[0.00006719039,0.0005355784,0.0009423693,0.0003959193,0.00002708736,0.000002438617,0.0009215425,0.05214465,0.5719707,0.00136772,0.02820593,0.3434189],"study_design_scores_gemma":[0.0001965012,0.0001121517,0.0005990318,0.000009252528,0.000004928253,0.00003681884,0.00001280012,0.5543738,0.443554,0.00004973689,0.0009645601,0.00008647681],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5536557,0.00003514161,0.4445536,0.0004262528,0.0002861767,0.0004709928,0.00001287431,0.00004494718,0.0005143387],"genre_scores_gemma":[0.9484918,0.000001314217,0.04824648,0.001164948,0.0002315124,0.00003538308,8.864144e-7,0.00001931374,0.001808365],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5022292,"threshold_uncertainty_score":0.3774244,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01383907665366764,"score_gpt":0.2284114323335737,"score_spread":0.214572355679906,"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."}}