{"id":"W4402170837","doi":"10.3390/brainsci14090894","title":"A Machine Learning Approach to Classifying EEG Data Collected with or without Haptic Feedback during a Simulated Drilling Task","year":2024,"lang":"en","type":"article","venue":"Brain Sciences","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Tech University","funders":"Natural Sciences and Engineering Research Council of Canada; University of Ontario Institute of Technology","keywords":"Haptic technology; Task (project management); Electroencephalography; Computer science; Drilling; Artificial intelligence; Machine learning; Psychology; Engineering; 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":"codex-gemma-dda1882f352a","candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0009380935,0.0003180991,0.0003355655,0.0004551781,0.001052182,0.001630181,0.001689412,0.0000655613,0.00003667705],"category_scores_gemma":[0.001190097,0.0002016069,0.00003964467,0.003734987,0.0004578468,0.0009048614,0.0006852873,0.0004349481,0.00005929164],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005643989,"about_ca_system_score_gemma":0.0002689944,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008472414,"about_ca_topic_score_gemma":0.00007484876,"domain_scores_codex":[0.996182,0.0002577135,0.0003579805,0.001698544,0.0007926208,0.0007111638],"domain_scores_gemma":[0.9980976,0.001077659,0.00009467285,0.0004858328,0.00004005121,0.0002041555],"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":[0.0004441505,0.0002125592,0.003306169,0.0004720904,0.00006676784,0.0002913733,0.01254992,0.2041651,0.7718904,0.0005764572,0.001332649,0.004692413],"study_design_scores_gemma":[0.0003876745,0.0003422067,0.0002285189,0.000459763,0.00001508516,0.000250623,0.0004777455,0.9833589,0.008037418,0.00002819129,0.006023597,0.0003902699],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9774745,0.0002365626,0.01454493,0.001533062,0.0003350871,0.0006332201,0.00003064186,0.0008081938,0.004403821],"genre_scores_gemma":[0.9873815,0.00001071052,0.007070781,0.000601601,0.00009647402,0.00001183642,0.000006825316,0.0000386298,0.004781703],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7791938,"threshold_uncertainty_score":0.9994062,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09187721346464019,"score_gpt":0.3217946259806678,"score_spread":0.2299174125160277,"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."}}