{"id":"W4406779914","doi":"10.18280/isi.300120","title":"Hybrid Optimized Techniques for EEG Based Mild Cognitive Impairment Detection Using Time Domain Feature Extraction","year":2025,"lang":"en","type":"article","venue":"Ingénierie des systèmes d information","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Electroencephalography; Feature extraction; Computer science; Cognitive impairment; Time domain; Pattern recognition (psychology); Extraction (chemistry); Cognition; Artificial intelligence; Frequency domain; Feature (linguistics); Speech recognition; Psychology; Chromatography; Computer vision; Neuroscience; Chemistry","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002648879,0.0006384221,0.000442299,0.0007599649,0.0001643385,0.0005028122,0.0004043042,0.0003630129,0.002053118],"category_scores_gemma":[0.000647618,0.0001579588,0.0005756977,0.0008309341,0.0000985177,0.0004112763,0.0003264258,0.000324,0.0006389849],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001332786,"about_ca_system_score_gemma":0.0003719539,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002144092,"about_ca_topic_score_gemma":0.004567794,"domain_scores_codex":[0.999851,0.00002627054,0.00001170882,0.00003129967,0.00005432519,0.00002535049],"domain_scores_gemma":[0.9998125,0.00006506727,0.00002221393,0.00001425743,0.00007823011,0.000007777785],"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":[0.0005977463,0.000184331,0.002616697,0.0001918628,0.0001864434,0.0001515557,0.00006274309,0.02197739,0.1781706,0.0008950006,0.002137534,0.7928281],"study_design_scores_gemma":[0.00006376437,0.0004513672,0.03618832,0.00004666477,0.000322477,0.0007733424,0.0001074063,0.8409253,0.1128689,0.002058969,0.006116572,0.00007689846],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07384213,0.001056219,0.9222395,0.0001093216,0.00006598092,0.0000619594,0.0004294009,0.0008008478,0.001394598],"genre_scores_gemma":[0.5082374,0.001004037,0.4830887,0.0001024721,0.00009233493,0.0001471572,0.001038107,0.0001456015,0.006144267],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002144092,"threshold_uncertainty_score":0.006868362,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0154060375489498,"score_gpt":0.275564887659514,"score_spread":0.2601588501105642,"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."}}