{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001601479,0.001261761,0.001040105,0.001954193,0.0004090251,0.0009872519,0.0008555843,0.0012097,0.001041592],"category_scores_gemma":[0.004549116,0.0002851134,0.0009962047,0.001241132,0.0004407249,0.0005911089,0.0005778593,0.001169497,0.0004508132],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004025044,"about_ca_system_score_gemma":0.0006775877,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002426805,"about_ca_topic_score_gemma":0.001613004,"domain_scores_codex":[0.9992256,0.0001892042,0.0001041189,0.0002507077,0.0001447592,0.00008566641],"domain_scores_gemma":[0.9987448,0.0007428934,0.0001222936,0.00007675071,0.0002722648,0.00004093417],"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":[0.0005957963,0.0008057224,0.00735158,0.0003034125,0.0002525758,0.0003341863,0.0002453775,0.1306845,0.02378972,0.001272257,0.001795013,0.8325699],"study_design_scores_gemma":[0.00002201263,0.0003090127,0.004571116,0.00002544528,0.00003533068,0.0001143941,0.00008541944,0.9878335,0.005109376,0.001316483,0.0005533263,0.00002461276],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1158034,0.0006770242,0.8783302,0.0003960617,0.0001691279,0.0005816726,0.0004886767,0.002218519,0.001335357],"genre_scores_gemma":[0.6953579,0.0004053463,0.30043,0.0001438134,0.0001112669,0.0009126748,0.0009162434,0.00003607696,0.001686701],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002426805,"threshold_uncertainty_score":0.008469522,"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."}}