{"about":{"non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","site":"https://codewithpapers.app","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page","syntology":{"site":"https://syntology.ai","developers":"https://syntology.ai/developers","mcp":{"server":"https://syntology.ai/mcp","transport":"streamable-http","server_card":"https://syntology.ai/.well-known/mcp/server-card.json","auth":{"type":"trial token, no account","trial_token":"https://syntology.ai/api/oauth/trial/token","method":"POST","docs":"https://syntology.ai/developers"}},"have":"https://syntology.ai/api/graph/have?x=<method, arXiv id or title> (free, answers coverage only)","paper_base":"https://syntology.ai/paper/","atlas_base":"https://app.syntology.ai/?focus="},"machine_readable":[{"url":"https://codewithpapers.app/llms.txt","what":"the machine catalog: every machine-readable file, counted"},{"url":"https://codewithpapers.app/index/manifest.json","what":"paper-to-code index by arXiv id, with Syntology's counts"},{"url":"https://codewithpapers.app/search/manifest.json","what":"site search index (titles, authors) and its files"},{"url":"https://codewithpapers.app/download","what":"bulk files: Syntology's layer, described there"},{"url":"https://codewithpapers.app/build_manifest.json","what":"the build record: inputs, counts, exclusions, probes"}]},"url":"/task/eeg/papers/17","list_of":"/task/eeg","task":"Electroencephalogram (EEG)","archive":{"snapshot":"2025-07-28"},"key_notes":{"n_ran_checked":"legacy name, kept unchanged so existing readers do not break: it counts the samples that ran with no instrument failure (honoured, violated, and ran with no contract checked); it does not mean a contract was checked, and the pages print it as 'K with no instrument failure', not 'K checked'","n_constructed":"a sub-count of the samples that ran, never subtracted from them and never a failure: an executed sample whose run returned an instance of its own class (fixture_out_type equals the entry name): the run built an object and did not compute a result (Syntology's RAN record, counts.constructed)"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"archive","order_definition":"repositories listed in the archive (most first), then date (newest first), then slug","page":17,"pages_in_order":17,"rows_per_page":100,"rows":[1601,1655],"of":1655,"counts":{"archive_papers_tagged":1655,"with_a_code_link":378,"where_syntology_ran_a_sample":36,"not_listed_spam_title":0,"listed":1655,"listed_where_code_ran":36,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":27,"every_run_a_failure_of_syntologys_instrument":9,"listed_with_a_run_with_no_instrument_failure":27,"listed_every_run_a_failure_of_syntologys_instrument":9,"filter":{"states":["a run with no instrument failure","any run, instrument failures included"],"default":"a run with no instrument failure","note":"on the 'only where code ran' pages the default hides, in the browser, the rows where every run was a failure of Syntology's instrument; the second state shows them again. Rows are hidden, never re-ordered; these twins list every row"}},"definition":"distinct papers the archive tags; 'where Syntology ran a sample' counts papers with at least one harvested sample that ran, which is not a correctness claim"},"first_page":"/task/eeg","prev":"/task/eeg/papers/16","next":null,"papers":[{"url":null,"slug":"tensor-analysis-and-fusion-of-multimodal","title":"Tensor Analysis and Fusion of Multimodal Brain Images","date":"2015-06-19","arxiv_id":"1506.06040","repositories_listed":0,"syntology":null},{"url":null,"slug":"head-related-impulse-response-cues-for","title":"Head-related Impulse Response Cues for Spatial Auditory Brain-computer Interface","date":"2015-06-14","arxiv_id":"1506.04374","repositories_listed":0,"syntology":null},{"url":"/paper/investigating-critical-frequency-bands-and","slug":"investigating-critical-frequency-bands-and","title":"Investigating critical frequency bands and channels for EEG-based emotion recognition with deep neural networks","date":"2015-05-08","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"exploring-eeg-for-object-detection-and","title":"Exploring EEG for Object Detection and Retrieval","date":"2015-04-09","arxiv_id":"1504.02356","repositories_listed":0,"syntology":null},{"url":null,"slug":"two-step-input-spatial-auditory-bci-for","title":"Two-step Input Spatial Auditory BCI for Japanese Kana Characters","date":"2015-03-10","arxiv_id":"1503.02903","repositories_listed":0,"syntology":null},{"url":null,"slug":"supervised-logeuclidean-metric-learning-for","title":"Supervised LogEuclidean Metric Learning for Symmetric Positive Definite Matrices","date":"2015-02-12","arxiv_id":"1502.03505","repositories_listed":0,"syntology":null},{"url":null,"slug":"backward-renormalization-priors-and-the","title":"Backward Renormalization Priors and the Cortical Source Localization Problem with EEG or MEG","date":"2015-02-11","arxiv_id":"1502.03481","repositories_listed":0,"syntology":null},{"url":null,"slug":"difficulties-applying-recent-blind-source","title":"Difficulties applying recent blind source separation techniques to EEG and MEG","date":"2015-01-21","arxiv_id":"1501.05068","repositories_listed":0,"syntology":null},{"url":null,"slug":"sparse-bayesian-learning-for-eeg-source","title":"Sparse Bayesian Learning for EEG Source Localization","date":"2015-01-19","arxiv_id":"1501.04621","repositories_listed":0,"syntology":null},{"url":null,"slug":"detecting-epileptic-seizures-from-eeg-data","title":"Detecting Epileptic Seizures from EEG Data using Neural Networks","date":"2014-12-19","arxiv_id":"1412.6502","repositories_listed":0,"syntology":null},{"url":null,"slug":"covariance-shrinkage-for-autocorrelated-data","title":"Covariance shrinkage for autocorrelated data","date":"2014-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"using-convolutional-neural-networks-to-2","title":"Using Convolutional Neural Networks to Recognize Rhythm ￼Stimuli from Electroencephalography Recordings","date":"2014-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"canonical-polyadic-decomposition-with","title":"Canonical Polyadic Decomposition with Auxiliary Information for Brain Computer Interface","date":"2014-10-23","arxiv_id":"1410.6313","repositories_listed":0,"syntology":null},{"url":null,"slug":"artifact-reduction-in-multichannel-pervasive","title":"Artifact reduction in multichannel pervasive EEG using hybrid WPT-ICA and WPT-EMD signal decomposition techniques","date":"2014-10-20","arxiv_id":"1410.5801","repositories_listed":0,"syntology":null},{"url":null,"slug":"classification-of-autism-spectrum-disorder","title":"Classification of Autism Spectrum Disorder Using Supervised Learning of Brain Connectivity Measures Extracted from Synchrostates","date":"2014-10-20","arxiv_id":"1410.7795","repositories_listed":0,"syntology":null},{"url":null,"slug":"prediction-of-synchrostate-transitions-in-eeg","title":"Prediction of Synchrostate Transitions in EEG Signals Using Markov Chain Models","date":"2014-10-20","arxiv_id":"1410.5362","repositories_listed":0,"syntology":null},{"url":null,"slug":"feature-learning-from-incomplete-eeg-with","title":"Feature Learning from Incomplete EEG with Denoising Autoencoder","date":"2014-10-03","arxiv_id":"1410.0818","repositories_listed":0,"syntology":null},{"url":null,"slug":"identification-of-dynamic-functional-brain","title":"Identification of Dynamic functional brain network states Through Tensor Decomposition","date":"2014-10-02","arxiv_id":"1410.0446","repositories_listed":0,"syntology":null},{"url":null,"slug":"object-segmentation-in-images-using-eeg","title":"Object Segmentation in Images using EEG Signals","date":"2014-08-19","arxiv_id":"1408.4363","repositories_listed":0,"syntology":null},{"url":null,"slug":"new-crossover-operators-for-multiple-subset","title":"New crossover operators for multiple subset selection tasks","date":"2014-08-06","arxiv_id":"1408.1297","repositories_listed":0,"syntology":null},{"url":null,"slug":"dimensionality-reduction-for-time-series-data","title":"Dimensionality reduction for time series data","date":"2014-06-14","arxiv_id":"1406.3711","repositories_listed":0,"syntology":null},{"url":null,"slug":"spatial-neural-networks-and-their-functional","title":"Spatial Neural Networks and their Functional Samples: Similarities and Differences","date":"2014-05-03","arxiv_id":"1405.0573","repositories_listed":0,"syntology":null},{"url":null,"slug":"new-functions-for-a-multipurpose-multimodal","title":"New functions for a multipurpose multimodal tool for phonetic and linguistic analysis of very large speech corpora","date":"2014-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"spatiotemporal-sparse-bayesian-learning-with","title":"Spatiotemporal Sparse Bayesian Learning with Applications to Compressed Sensing of Multichannel Physiological Signals","date":"2014-04-21","arxiv_id":"1404.5122","repositories_listed":0,"syntology":null},{"url":null,"slug":"human-brain-distinctiveness-based-on-eeg","title":"Human brain distinctiveness based on EEG spectral coherence connectivity","date":"2014-03-23","arxiv_id":"1403.6384","repositories_listed":0,"syntology":null},{"url":null,"slug":"sleep-analytics-and-online-selective-anomaly","title":"Sleep Analytics and Online Selective Anomaly Detection","date":"2014-03-02","arxiv_id":"1403.0156","repositories_listed":0,"syntology":null},{"url":null,"slug":"modeling-the-complex-dynamics-and-changing","title":"Modeling the Complex Dynamics and Changing Correlations of Epileptic Events","date":"2014-02-27","arxiv_id":"1402.6951","repositories_listed":0,"syntology":null},{"url":null,"slug":"automated-classification-of-lr-hand-movement","title":"Automated Classification of L/R Hand Movement EEG Signals using Advanced Feature Extraction and Machine Learning","date":"2013-12-10","arxiv_id":"1312.2877","repositories_listed":0,"syntology":null},{"url":null,"slug":"robust-spatial-filtering-with-beta-divergence","title":"Robust Spatial Filtering with Beta Divergence","date":"2013-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"brains-and-pseudorandom-generators","title":"Brains and pseudorandom generators","date":"2013-11-26","arxiv_id":"1311.6531","repositories_listed":0,"syntology":null},{"url":null,"slug":"compressed-sensing-for-energy-efficient","title":"Compressed Sensing for Energy-Efficient Wireless Telemonitoring: Challenges and Opportunities","date":"2013-11-15","arxiv_id":"1311.3995","repositories_listed":0,"syntology":null},{"url":null,"slug":"spatial-spectral-boosting-analysis-for-stroke","title":"Spatial-Spectral Boosting Analysis for Stroke Patients' Motor Imagery EEG in Rehabilitation Training","date":"2013-10-23","arxiv_id":"1310.6288","repositories_listed":0,"syntology":null},{"url":null,"slug":"frequency-recognition-in-ssvep-based-bci","title":"Frequency Recognition in SSVEP-based BCI using Multiset Canonical Correlation Analysis","date":"2013-08-26","arxiv_id":"1308.5609","repositories_listed":0,"syntology":null},{"url":null,"slug":"energy-distribution-of-eeg-signals-eeg-signal","title":"Energy Distribution of EEG Signals: EEG Signal Wavelet-Neural Network Classifier","date":"2013-07-30","arxiv_id":"1307.7897","repositories_listed":0,"syntology":null},{"url":null,"slug":"transmodal-analysis-of-neural-signals","title":"Transmodal Analysis of Neural Signals","date":"2013-07-08","arxiv_id":"1307.2150","repositories_listed":0,"syntology":null},{"url":null,"slug":"classifying-single-trial-eeg-during-motor","title":"Classifying Single-Trial EEG during Motor Imagery with a Small Training Set","date":"2013-06-14","arxiv_id":"1306.3474","repositories_listed":0,"syntology":null},{"url":null,"slug":"testing-hypotheses-by-regularized-maximum","title":"Testing Hypotheses by Regularized Maximum Mean Discrepancy","date":"2013-05-02","arxiv_id":"1305.0423","repositories_listed":0,"syntology":null},{"url":null,"slug":"jitter-adaptive-dictionary-learning","title":"Jitter-Adaptive Dictionary Learning - Application to Multi-Trial Neuroelectric Signals","date":"2013-01-16","arxiv_id":"1301.3611","repositories_listed":0,"syntology":null},{"url":null,"slug":"transferring-subspaces-between-subjects-in","title":"Transferring Subspaces Between Subjects in Brain-Computer Interfacing","date":"2012-09-18","arxiv_id":"1209.4115","repositories_listed":0,"syntology":null},{"url":"/paper/decoding-finger-flexion-from-band-specific","slug":"decoding-finger-flexion-from-band-specific","title":"Decoding finger flexion from band-specific ECoG signals in humans","date":"2012-06-28","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"compressed-sensing-of-eeg-for-wireless","title":"Compressed Sensing of EEG for Wireless Telemonitoring with Low Energy Consumption and Inexpensive Hardware","date":"2012-06-13","arxiv_id":"1206.3493","repositories_listed":0,"syntology":null},{"url":null,"slug":"la-mie-de-pain-nest-pas-une-amie-une-etude","title":"La mie de pain n'est pas une amie: une \\'etude EEG sur la perception de diff\\'erences infra-phon\\'emiques en situation de variations (Robustness of fine acoustic cues and Speech variability: a Mismatch Negativity study) [in French]","date":"2012-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-using-eeg-to-improve-asr-accuracy","title":"Towards Using EEG to Improve ASR Accuracy","date":"2012-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"traitement-audiovisuel-lors-dune-tache-de","title":"Traitement audiovisuel lors d'une t\\^ache de discrimination syllabique : une \\'etude EEG/IRMf simultan\\'ee (Audiovisual processing in syllabic discrimination task: a simultaneous fMRI-EEG study) [in French]","date":"2012-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"maximum-covariance-unfolding-manifold","title":"Maximum Covariance Unfolding : Manifold Learning for Bimodal Data","date":"2011-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"a-biologically-plausible-model-for-rapid","title":"A Biologically Plausible Model for Rapid Natural Scene Identification","date":"2009-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"optimizing-multi-class-spatio-spectral","title":"Optimizing Multi-Class Spatio-Spectral Filters via Bayes Error Estimation for EEG Classification","date":"2009-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"subject-independent-eeg-based-bci-decoding","title":"Subject independent EEG-based BCI decoding","date":"2009-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"effects-of-stimulus-type-and-of-error","title":"Effects of Stimulus Type and of Error-Correcting Code Design on BCI Speller Performance","date":"2008-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"estimating-the-location-and-orientation-of","title":"Estimating the Location and Orientation of Complex, Correlated Neural Activity using MEG","date":"2008-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"estimating-vector-fields-using-sparse-basis","title":"Estimating vector fields using sparse basis field expansions","date":"2008-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"playing-pinball-with-non-invasive-bci","title":"Playing Pinball with non-invasive BCI","date":"2008-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"understanding-brain-connectivity-patterns","title":"Understanding Brain Connectivity Patterns during Motor Imagery for Brain-Computer Interfacing","date":"2008-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"eeg-based-brain-computer-interaction-improved","title":"EEG-Based Brain-Computer Interaction: Improved Accuracy by Automatic Single-Trial Error Detection","date":"2007-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"second-order-bilinear-discriminant-analysis","title":"Second Order Bilinear Discriminant Analysis for single trial EEG analysis","date":"2007-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null}],"record_sha256":"07cd405a6b7d8a1beea629f79b7fc24a2d1cad0e25f27bd71ee329a819cb9781","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}