{"id":"W3175078852","doi":"10.1609/aaai.v35i1.16168","title":"Riemannian Embedding Banks for Common Spatial Patterns with EEG-based SPD Neural Networks","year":2021,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"Kootenay Association for Science & Technology","funders":"Ministry of Science and ICT, South Korea; National Research Foundation of Korea; National Research Foundation","keywords":"Embedding; Artificial intelligence; Riemannian geometry; Computer science; Riemannian manifold; Manifold (fluid mechanics); Artificial neural network; Pattern recognition (psychology); Generalization; Deep learning; Nonlinear dimensionality reduction; Feature (linguistics); Feature vector; Context (archaeology); Metric (unit); Mathematics; Algorithm; Pure mathematics; Dimensionality reduction; Mathematical analysis; Geography","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":[0.001467798,0.001383982,0.001211763,0.0008363724,0.0004171722,0.00094668,0.001978156,0.001269253,0.002533679],"category_scores_gemma":[0.004794041,0.0008450618,0.001177918,0.001237902,0.0009609404,0.002313513,0.00263704,0.002112246,0.001228875],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007791262,"about_ca_system_score_gemma":0.0009761931,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004367735,"about_ca_topic_score_gemma":0.005330046,"domain_scores_codex":[0.9991603,0.0002621471,0.00007451601,0.0002308673,0.0002060444,0.00006606255],"domain_scores_gemma":[0.9987161,0.0004508334,0.000149064,0.0002724647,0.0003386796,0.00007284379],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000158706,0.0001402368,0.001621808,0.0001630802,0.0001930243,0.0001712815,0.0002250275,0.5373303,0.01112794,0.02737175,0.00546165,0.4160352],"study_design_scores_gemma":[0.00000302199,0.00001432797,0.00008555115,0.000003134306,0.000004359312,0.00001573186,0.000005521777,0.9956329,0.0005176293,0.003404976,0.0003083613,0.000004285846],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.008412656,0.0002617229,0.9900563,0.0001611885,0.00003389345,0.00003502792,0.00004893641,0.0004307372,0.0005595023],"genre_scores_gemma":[0.375641,0.0006608745,0.6176909,0.0003962548,0.0001006932,0.0003881176,0.0006459096,0.0002024245,0.004273866],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004367735,"threshold_uncertainty_score":0.008684635,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0723957588379945,"score_gpt":0.3127454600403223,"score_spread":0.2403497012023278,"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."}}