{"id":"W2077991879","doi":"10.1016/j.jneumeth.2015.01.010","title":"Exceeding chance level by chance: The caveat of theoretical chance levels in brain signal classification and statistical assessment of decoding accuracy","year":2015,"lang":"en","type":"article","venue":"Journal of Neuroscience Methods","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":636,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Montréal","funders":"Canada Research Chairs; Agence Nationale de la Recherche","keywords":"Decoding methods; Sample size determination; Naive Bayes classifier; Computer science; Artificial intelligence; Linear discriminant analysis; Support vector machine; Pattern recognition (psychology); Statistical hypothesis testing; Type I and type II errors; Binomial distribution; Magnetoencephalography; Multiclass classification; Statistics; Mathematics; Electroencephalography; Algorithm; Psychology","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":[],"consensus_categories":[],"category_scores_codex":[0.00632756,0.0001824523,0.0004514164,0.000208981,0.0001054202,0.00009961882,0.0008164245,0.00006259718,0.000007794791],"category_scores_gemma":[0.007689964,0.0001224501,0.00005931139,0.0006444482,0.001201094,0.000563169,0.0001893462,0.0005045632,1.541928e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006552604,"about_ca_system_score_gemma":0.0002031519,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003909162,"about_ca_topic_score_gemma":6.013527e-7,"domain_scores_codex":[0.996142,0.001369368,0.0008925735,0.0003983973,0.0008448954,0.0003528141],"domain_scores_gemma":[0.993559,0.004937202,0.0009320096,0.0002042062,0.0001935815,0.0001739914],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00003487953,0.00009198707,0.0006451295,0.00002410233,0.000001280948,0.000008591311,0.0007288589,0.0001077886,0.9589941,0.02043626,0.0001024601,0.01882455],"study_design_scores_gemma":[0.0006626228,0.000813883,0.05140885,0.0001957218,0.00001173496,0.0002426604,0.0004004065,0.1686678,0.7695748,0.007615697,0.0002273102,0.0001785211],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4729372,0.00007445797,0.5242436,0.001981576,0.0004710659,0.0001353938,0.00002262719,0.000004532611,0.0001295391],"genre_scores_gemma":[0.9410347,0.00007555746,0.05819613,0.0005887438,0.00006814793,0.000004315874,1.052926e-7,0.00001089015,0.00002135422],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.4680976,"threshold_uncertainty_score":0.920616,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2833158072225691,"score_gpt":0.4870726816101474,"score_spread":0.2037568743875783,"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."}}