{"id":"W3217581544","doi":"10.1049/sil2.12080","title":"BCI‐control and monitoring system for smart home automation using wavelet classifiers","year":2021,"lang":"en","type":"article","venue":"IET Signal Processing","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Brain–computer interface; Computer science; Electroencephalography; Artificial intelligence; Wavelet; Pattern recognition (psychology); Feature extraction; Data acquisition; Interface (matter); Signal processing; Speech recognition; Digital signal processing; Computer hardware","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":[0.0002618408,0.0003587336,0.0003741092,0.0004217552,0.0001797827,0.0004007522,0.0004450933,0.000333757,0.002331605],"category_scores_gemma":[0.0006049951,0.0001244767,0.0002349794,0.0004180002,0.0001091751,0.0003914961,0.0003350565,0.0003445926,0.0008780633],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002665716,"about_ca_system_score_gemma":0.000365463,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001217903,"about_ca_topic_score_gemma":0.0006944994,"domain_scores_codex":[0.9997539,0.00002892965,0.00001967022,0.000058425,0.0001078465,0.00003123122],"domain_scores_gemma":[0.9998193,0.00002528252,0.00002299299,0.00002730612,0.00009169013,0.00001343768],"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":[0.0006488404,0.0003941602,0.004558737,0.0002717968,0.0001161815,0.000424685,0.0001313671,0.03330827,0.2599102,0.002287145,0.01015943,0.6877891],"study_design_scores_gemma":[0.00007611781,0.0004209416,0.008799563,0.00003434385,0.00007334707,0.0003818448,0.00003168101,0.8658023,0.1155132,0.001078736,0.007752279,0.00003559516],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1963405,0.0003900413,0.7875729,0.0003082454,0.0001606936,0.0002853748,0.0003774898,0.008074287,0.006490418],"genre_scores_gemma":[0.8688701,0.0002078875,0.1263024,0.0001340354,0.00003688139,0.0001905677,0.0004016457,0.00008734676,0.003769208],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002331605,"threshold_uncertainty_score":0.007800043,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04484829437629914,"score_gpt":0.2873429248155279,"score_spread":0.2424946304392287,"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."}}