{"id":"W3080447717","doi":"10.21742/ijbsbt.2020.12.1.05","title":"Analyzing Brain Signals to Predict Seizure Events using Machine Learning Techniques","year":2020,"lang":"en","type":"article","venue":"International Journal of Bio-Science and Bio-Technology","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lakehead University","funders":"","keywords":"Random forest; Support vector machine; Computer science; Artificial intelligence; Preprocessor; Machine learning; Electroencephalography; Gradient boosting; Recall; Classifier (UML); Data pre-processing; Pattern recognition (psychology); Boosting (machine learning); Brain waves; Psychology; 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":[0.0007665619,0.0008268101,0.0006456105,0.003546711,0.0002118338,0.0009442061,0.0004178536,0.0005605657,0.000967438],"category_scores_gemma":[0.002347792,0.0001017135,0.0006567442,0.001811417,0.0001615439,0.0007724491,0.000256389,0.0004992197,0.0009601667],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001727682,"about_ca_system_score_gemma":0.0002991548,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001150486,"about_ca_topic_score_gemma":0.001744909,"domain_scores_codex":[0.9994769,0.00009105026,0.00006479079,0.000100193,0.0002102861,0.00005680776],"domain_scores_gemma":[0.999221,0.0004002362,0.0001082009,0.00005651075,0.000189736,0.00002433148],"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":[0.0003629648,0.0004401518,0.04489079,0.0006701008,0.0002703106,0.0004968874,0.0001154777,0.06515573,0.03686329,0.001503285,0.005588899,0.8436421],"study_design_scores_gemma":[0.0000447619,0.001084143,0.08419055,0.0002691886,0.0002137292,0.001592081,0.0004133217,0.8506502,0.04723268,0.004342007,0.009877741,0.00008947988],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4291931,0.01006997,0.5441417,0.001023922,0.0004527779,0.0003796598,0.00456204,0.004279991,0.005896926],"genre_scores_gemma":[0.7953389,0.003957867,0.1920207,0.0001608867,0.0002358125,0.0001966818,0.006198114,0.00007311082,0.001817902],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003546711,"threshold_uncertainty_score":0.00405401,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02816655157756353,"score_gpt":0.3157220271700018,"score_spread":0.2875554755924382,"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."}}