{"id":"W4402632568","doi":"10.1007/978-3-031-71602-7_15","title":"Multi-modal Decoding of Reach-to-Grasping from EEG and EMG via Neural Networks","year":2024,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"Concordia University; Mila - Quebec Artificial Intelligence Institute","funders":"","keywords":"Computer science; Neural decoding; Decoding methods; Electroencephalography; Modal; Artificial neural network; Speech recognition; Artificial intelligence; Pattern recognition (psychology); Neuroscience; Telecommunications; Psychology","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":[0.0002413046,0.0006178313,0.0002469495,0.0003471175,0.0001394059,0.0007868827,0.0003796447,0.0005547043,0.003709674],"category_scores_gemma":[0.0008260561,0.0002145158,0.00036292,0.0005703529,0.0001715603,0.0005960509,0.0003366469,0.0004698495,0.001187523],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001558567,"about_ca_system_score_gemma":0.0002203178,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001450593,"about_ca_topic_score_gemma":0.003664072,"domain_scores_codex":[0.9999177,0.00001261615,0.00000529363,0.00002454946,0.00002780152,0.00001211615],"domain_scores_gemma":[0.9998933,0.00006241622,0.000009496986,0.000008281262,0.00002117318,0.000005182895],"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":[0.0001910064,0.0000641658,0.0006341524,0.0002421061,0.00007607054,0.0001029558,0.00008662486,0.02780415,0.2058685,0.002379738,0.002075515,0.7604749],"study_design_scores_gemma":[0.00002041763,0.0001377681,0.01779178,0.0001107693,0.0000821931,0.000485406,0.0000720013,0.8643686,0.1017517,0.01125924,0.003874187,0.00004585393],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0668501,0.002152553,0.9178033,0.0002945548,0.0001851331,0.0001025353,0.0007182116,0.001765627,0.01012796],"genre_scores_gemma":[0.7179951,0.002364789,0.2634808,0.0001305634,0.0001979884,0.0001315306,0.0008906712,0.0002810876,0.01452756],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003709674,"threshold_uncertainty_score":0.0124101,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02954715782613378,"score_gpt":0.2754148170381356,"score_spread":0.2458676592120018,"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."}}