{"id":"W2894215128","doi":"10.1117/1.nph.5.3.035009","title":"Functional near-infrared spectroscopy-based affective neurofeedback: feedback effect, illiteracy phenomena, and whole-connectivity profiles","year":2018,"lang":"en","type":"article","venue":"Neurophotonics","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"Coordenação de Aperfeiçoamento de Pessoal de Nível Superior; Fundação de Amparo à Pesquisa do Estado de São Paulo","keywords":"Neurofeedback; Functional near-infrared spectroscopy; Biofeedback; Computer science; Mahalanobis distance; Cognitive psychology; Auditory feedback; Psychology; Electroencephalography; Physical medicine and rehabilitation; Artificial intelligence; Cognition; Medicine; Neuroscience","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":[0.0003894624,0.0002850428,0.0001915345,0.0002582358,0.0001040659,0.0001741273,0.0001426916,0.000270968,0.001378102],"category_scores_gemma":[0.001966626,0.00007872775,0.0001062721,0.0001000796,0.0002429316,0.000243682,0.0001742133,0.0001232865,0.0001290212],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001219834,"about_ca_system_score_gemma":0.00009183349,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003703091,"about_ca_topic_score_gemma":0.0006691075,"domain_scores_codex":[0.9998347,0.00004437382,0.0000130292,0.00003786762,0.00005376077,0.00001631111],"domain_scores_gemma":[0.9994024,0.000340737,0.0001189464,0.00003448825,0.00005872605,0.00004475677],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.003818377,0.0004150346,0.07412089,0.0003117806,0.00009909987,0.0003946214,0.0005657933,0.001372669,0.8204598,0.0002271096,0.0002404805,0.09797434],"study_design_scores_gemma":[0.00006332366,0.002483274,0.8969896,0.00002540067,0.000136962,0.001290753,0.000192597,0.0122394,0.08538252,0.0006646179,0.0004995915,0.00003199504],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9942432,0.0002370443,0.004793184,0.00003838848,0.000007251813,0.00002674428,0.00005223756,0.0000287614,0.0005731339],"genre_scores_gemma":[0.998442,0.0000536272,0.001251076,0.00001637415,0.000005562725,0.00001546392,0.00004213171,0.00000571832,0.0001680449],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001378102,"threshold_uncertainty_score":0.004610181,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01737877837918723,"score_gpt":0.2476996237366511,"score_spread":0.2303208453574639,"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."}}