{"id":"W2964576881","doi":"10.1609/aaai.v33i01.330110019","title":"Hierarchical Deep Feature Learning for Decoding Imagined Speech from EEG","year":2019,"lang":"en","type":"article","venue":"","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":43,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Artificial intelligence; Electroencephalography; Convolutional neural network; Deep learning; Feature (linguistics); Recurrent neural network; Pattern recognition (psychology); Speech recognition; Decoding methods; Channel (broadcasting); Artificial neural network; Algorithm; 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":[0.0004197077,0.000804555,0.0004953208,0.0004851262,0.0001926563,0.0004399511,0.000833399,0.00049716,0.001951557],"category_scores_gemma":[0.001158019,0.0002822487,0.0005691545,0.0004612078,0.0002352672,0.0009154733,0.0008431833,0.0008151414,0.0007068908],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003997985,"about_ca_system_score_gemma":0.0007176464,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004022138,"about_ca_topic_score_gemma":0.008054342,"domain_scores_codex":[0.9997943,0.00003637475,0.00001279012,0.00005861902,0.0000548912,0.00004296294],"domain_scores_gemma":[0.9997614,0.00009330235,0.00002821761,0.00003821682,0.00006230962,0.00001644419],"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":[0.0002679548,0.0002524145,0.001856786,0.0001459881,0.0001245162,0.0001891958,0.00009853191,0.2239683,0.07526422,0.009150906,0.004837393,0.6838438],"study_design_scores_gemma":[0.000007699335,0.00004894949,0.0006275503,0.000006654131,0.00001427748,0.00002903823,0.000009544119,0.9850338,0.01020268,0.00341446,0.0005969482,0.000008323153],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04335466,0.0003285328,0.9525005,0.0001567765,0.00005464566,0.00004135013,0.0004295934,0.001779529,0.00135454],"genre_scores_gemma":[0.6470082,0.0002638118,0.3472044,0.000133058,0.00006856983,0.0001212349,0.001585315,0.0000982892,0.003517085],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004022138,"threshold_uncertainty_score":0.007997453,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01818404080653754,"score_gpt":0.2748584138029164,"score_spread":0.2566743729963789,"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."}}