{"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/10","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":10,"pages_in_order":17,"rows_per_page":100,"rows":[901,1000],"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/9","next":"/task/eeg/papers/11","papers":[{"url":null,"slug":"single-channel-eeg-based-arousal-level","title":"Single-Channel EEG Based Arousal Level Estimation Using Multitaper Spectrum Estimation at Low-Power Wearable Devices","date":"2021-07-31","arxiv_id":"2108.00216","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-spa-based-manifold-learning-framework-for","title":"A SPA-based Manifold Learning Framework for Motor Imagery EEG Data Classification","date":"2021-07-30","arxiv_id":"2108.00865","repositories_listed":0,"syntology":null},{"url":null,"slug":"eeg-multipurpose-eye-blink-detector-using","title":"EEG multipurpose eye blink detector using convolutional neural network","date":"2021-07-29","arxiv_id":"2107.14235","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-recurrent-semi-supervised-eeg","title":"Deep Recurrent Semi-Supervised EEG Representation Learning for Emotion Recognition","date":"2021-07-28","arxiv_id":"2107.13505","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-multi-objective-evolutionary-algorithm-for","title":"A Multi-objective Evolutionary Algorithm for EEG Inverse Problem","date":"2021-07-21","arxiv_id":"2107.10325","repositories_listed":0,"syntology":null},{"url":null,"slug":"high-frequency-eeg-artifact-detection-with","title":"High Frequency EEG Artifact Detection with Uncertainty via Early Exit Paradigm","date":"2021-07-21","arxiv_id":"2107.10746","repositories_listed":0,"syntology":null},{"url":null,"slug":"classification-of-upper-arm-movements-from","title":"Classification of Upper Arm Movements from EEG signals using Machine Learning with ICA Analysis","date":"2021-07-18","arxiv_id":"2107.08514","repositories_listed":0,"syntology":null},{"url":null,"slug":"sleep-staging-based-on-serialized-dual","title":"Sleep Staging Based on Multi Scale Dual Attention Network","date":"2021-07-18","arxiv_id":"2107.08442","repositories_listed":0,"syntology":null},{"url":null,"slug":"dal-feature-learning-from-overt-speech-to","title":"DAL: Feature Learning from Overt Speech to Decode Imagined Speech-based EEG Signals with Convolutional Autoencoder","date":"2021-07-15","arxiv_id":"2107.07064","repositories_listed":0,"syntology":null},{"url":null,"slug":"motor-imagery-classification-based-on-cnn-gru","title":"Motor Imagery Classification based on CNN-GRU Network with Spatio-Temporal Feature Representation","date":"2021-07-15","arxiv_id":"2107.07062","repositories_listed":0,"syntology":null},{"url":null,"slug":"exploring-brain-connectivity-in-auditory","title":"Complex network modelling of EEG band coupling in dyslexia: an exploratory analysis of auditory processing and diagnosis","date":"2021-06-28","arxiv_id":"2106.14675","repositories_listed":0,"syntology":null},{"url":null,"slug":"cadda-class-wise-automatic-differentiable","title":"CADDA: Class-wise Automatic Differentiable Data Augmentation for EEG Signals","date":"2021-06-25","arxiv_id":"2106.13695","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-natural-brain-machine-interaction","title":"Towards Natural Brain-Machine Interaction using Endogenous Potentials based on Deep Neural Networks","date":"2021-06-25","arxiv_id":"2107.07335","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-signal-representations-for-eeg-cross","title":"Learning Signal Representations for EEG Cross-Subject Channel Selection and Trial Classification","date":"2021-06-20","arxiv_id":"2106.10633","repositories_listed":0,"syntology":null},{"url":null,"slug":"eeg-gnn-graph-neural-networks-for","title":"EEG-GNN: Graph Neural Networks for Classification of Electroencephalogram (EEG) Signals","date":"2021-06-16","arxiv_id":"2106.09135","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-long-term-non-invasive-monitoring-for","title":"Towards Long-term Non-invasive Monitoring for Epilepsy via Wearable EEG Devices","date":"2021-06-15","arxiv_id":"2106.08008","repositories_listed":0,"syntology":null},{"url":null,"slug":"brain2depth-lightweight-cnn-model-for","title":"BRAIN2DEPTH: Lightweight CNN Model for Classification of Cognitive States from EEG Recordings","date":"2021-06-12","arxiv_id":"2106.06688","repositories_listed":0,"syntology":null},{"url":null,"slug":"cross-subject-domain-adaptation-for-multi","title":"Cross-Subject Domain Adaptation for Classifying Working Memory Load with Multi-Frame EEG Images","date":"2021-06-12","arxiv_id":"2106.06769","repositories_listed":0,"syntology":null},{"url":null,"slug":"artifact-detection-and-correction-in-eeg-data","title":"Artifact Detection and Correction in EEG data: A Review","date":"2021-06-10","arxiv_id":"2106.13081","repositories_listed":0,"syntology":null},{"url":null,"slug":"wheelchair-automation-by-a-hybrid-bci-system","title":"Wheelchair automation by a hybrid BCI system using SSVEP and eye blinks","date":"2021-06-10","arxiv_id":"2106.11008","repositories_listed":0,"syntology":null},{"url":null,"slug":"subject-independent-brain-computer-interface","title":"Subject-Independent Brain-Computer Interface for Decoding High-Level Visual Imagery Tasks","date":"2021-06-08","arxiv_id":"2106.04026","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-highly-scalable-repository-of-waveform-and","title":"A highly scalable repository of waveform and vital signs data from bedside monitoring devices","date":"2021-06-07","arxiv_id":"2106.03965","repositories_listed":0,"syntology":null},{"url":null,"slug":"subject-independent-emotion-recognition-using","title":"Subject Independent Emotion Recognition using EEG Signals Employing Attention Driven Neural Networks","date":"2021-06-07","arxiv_id":"2106.03461","repositories_listed":0,"syntology":null},{"url":null,"slug":"eeg-changes-and-motor-deficits-in-parkinson-s","title":"EEG changes and motor deficits in Parkinson's disease patients: Correlation of motor scales and EEG power bands","date":"2021-06-04","arxiv_id":"2106.02387","repositories_listed":0,"syntology":null},{"url":null,"slug":"random-forest-classifier-for-eeg-based","title":"Random Forest classifier for EEG-based seizure prediction","date":"2021-06-02","arxiv_id":"2106.04510","repositories_listed":0,"syntology":null},{"url":null,"slug":"trace-alternant-detector-for-grading-hypoxic","title":"Tracé alternant detector for grading hypoxic-ischemic encephalopathy in neonatal EEG","date":"2021-05-31","arxiv_id":"2106.00061","repositories_listed":0,"syntology":null},{"url":null,"slug":"bioelectrical-brain-activity-can-predict","title":"Bioelectrical brain activity can predict prosocial behavior","date":"2021-05-30","arxiv_id":"2105.14587","repositories_listed":0,"syntology":null},{"url":null,"slug":"generating-ten-bci-commands-using-four-simple","title":"Generating Ten BCI Commands Using Four Simple Motor Imageries","date":"2021-05-30","arxiv_id":"2105.14493","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-learning-for-eeg-seizure-detection-in","title":"Deep Learning for EEG Seizure Detection in Preterm Infants","date":"2021-05-28","arxiv_id":"2106.00611","repositories_listed":0,"syntology":null},{"url":null,"slug":"neonatal-seizure-detection-from-raw-multi","title":"Neonatal seizure detection from raw multi-channel EEG using a fully convolutional architecture","date":"2021-05-28","arxiv_id":"2105.13854","repositories_listed":0,"syntology":null},{"url":null,"slug":"sleeptransformer-automatic-sleep-staging-with","title":"SleepTransformer: Automatic Sleep Staging with Interpretability and Uncertainty Quantification","date":"2021-05-23","arxiv_id":"2105.11043","repositories_listed":0,"syntology":null},{"url":null,"slug":"automated-detection-of-abnormal-eegs-in","title":"Automated Detection of Abnormalities from an EEG Recording of Epilepsy Patients With a Compact Convolutional Neural Network","date":"2021-05-21","arxiv_id":"2105.10358","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-correlation-analysis-for-audio-eeg","title":"Deep Correlation Analysis for Audio-EEG Decoding","date":"2021-05-18","arxiv_id":"2105.08492","repositories_listed":0,"syntology":null},{"url":null,"slug":"predicting-speech-intelligibility-from-eeg","title":"Predicting speech intelligibility from EEG in a non-linear classification paradigm","date":"2021-05-14","arxiv_id":"2105.06844","repositories_listed":0,"syntology":null},{"url":null,"slug":"dyadic-aggregated-autoregressive-dasar-model","title":"Dyadic aggregated autoregressive (DASAR) model for time-frequency representation of biomedical signals","date":"2021-05-13","arxiv_id":"2105.10406","repositories_listed":0,"syntology":null},{"url":null,"slug":"normative-brain-mapping-of-interictal","title":"Normative brain mapping of interictal intracranial EEG to localise epileptogenic tissue","date":"2021-05-10","arxiv_id":"2105.04643","repositories_listed":0,"syntology":null},{"url":null,"slug":"long-term-changes-in-functional-connectivity","title":"Long-term changes in functional connectivity predict responses to intracranial stimulation of the human brain","date":"2021-05-06","arxiv_id":"2105.02805","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-novel-multi-scale-dilated-3d-cnn-for","title":"A Novel Multi-scale Dilated 3D CNN for Epileptic Seizure Prediction","date":"2021-05-05","arxiv_id":"2105.02823","repositories_listed":0,"syntology":null},{"url":null,"slug":"distilling-eeg-representations-via-capsules","title":"Distilling EEG Representations via Capsules for Affective Computing","date":"2021-04-30","arxiv_id":"2105.00104","repositories_listed":0,"syntology":null},{"url":null,"slug":"pseudo-interactive-pattern-search-in","title":"PSEUDo: Interactive Pattern Search in Multivariate Time Series with Locality-Sensitive Hashing and Relevance Feedback","date":"2021-04-30","arxiv_id":"2104.14962","repositories_listed":0,"syntology":null},{"url":null,"slug":"group-feature-learning-and-domain-adversarial","title":"Group Feature Learning and Domain Adversarial Neural Network for aMCI Diagnosis System Based on EEG","date":"2021-04-28","arxiv_id":"2105.06270","repositories_listed":0,"syntology":null},{"url":null,"slug":"algoritmos-de-mineria-de-datos-en-la","title":"Algoritmos de minería de datos en la industria sanitaria","date":"2021-04-19","arxiv_id":"2104.09395","repositories_listed":0,"syntology":null},{"url":null,"slug":"neural-language-models-with-distant","title":"Neural Language Models with Distant Supervision to Identify Major Depressive Disorder from Clinical Notes","date":"2021-04-19","arxiv_id":"2104.09644","repositories_listed":0,"syntology":null},{"url":null,"slug":"orthogonal-features-based-eeg-signals","title":"Orthogonal Features Based EEG Signals Denoising Using Fractional and Compressed One-Dimensional CNN AutoEncoder","date":"2021-04-16","arxiv_id":"2104.08120","repositories_listed":0,"syntology":null},{"url":null,"slug":"decoding-of-the-walking-states-and-step-rates","title":"Decoding of the Walking States and Step Rates from Cortical Electrocorticogram Signals","date":"2021-04-14","arxiv_id":"2104.07062","repositories_listed":0,"syntology":null},{"url":null,"slug":"identification-of-mental-fatigue-in-language","title":"Identification of mental fatigue in language comprehension tasks based on EEG and deep learning","date":"2021-04-14","arxiv_id":"2104.08337","repositories_listed":0,"syntology":null},{"url":null,"slug":"one-class-autoencoder-approach-for-optimal","title":"One-class Autoencoder Approach for Optimal Electrode Set-up Identification in Wearable EEG Event Monitoring","date":"2021-04-09","arxiv_id":"2104.04546","repositories_listed":0,"syntology":null},{"url":null,"slug":"sfe-net-eeg-based-emotion-recognition-with","title":"SFE-Net: EEG-based Emotion Recognition with Symmetrical Spatial Feature Extraction","date":"2021-04-09","arxiv_id":"2104.06308","repositories_listed":0,"syntology":null},{"url":null,"slug":"multimodal-fusion-of-emg-and-vision-for-human","title":"Multimodal Fusion of EMG and Vision for Human Grasp Intent Inference in Prosthetic Hand Control","date":"2021-04-08","arxiv_id":"2104.03893","repositories_listed":0,"syntology":null},{"url":null,"slug":"neurological-status-classification-using","title":"Neurological Status Classification Using Convolutional Neural Network","date":"2021-04-01","arxiv_id":"2104.02058","repositories_listed":0,"syntology":null},{"url":null,"slug":"convolutional-neural-networks-for-sleep-stage","title":"Convolutional Neural Networks for Sleep Stage Scoring on a Two-Channel EEG Signal","date":"2021-03-30","arxiv_id":"2103.16215","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-effect-of-double-biofeedback-on","title":"Does Double Biofeedback Affect Functional Hemispheric Asymmetry and Activity? A Pilot Study","date":"2021-03-30","arxiv_id":"2103.16587","repositories_listed":0,"syntology":null},{"url":null,"slug":"product-semantics-translation-from-brain","title":"Product semantics translation from brain activity via adversarial learning","date":"2021-03-29","arxiv_id":"2103.15602","repositories_listed":0,"syntology":null},{"url":null,"slug":"retrieving-event-related-human-brain-dynamics","title":"Retrieving Event-related Human Brain Dynamics from Natural Sentence Reading","date":"2021-03-29","arxiv_id":"2103.15500","repositories_listed":0,"syntology":null},{"url":null,"slug":"verifying-design-through-generative","title":"Verifying Design through Generative Visualization of Neural Activities","date":"2021-03-28","arxiv_id":"2103.15182","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-koopman-operator-based-model-predictive","title":"Online Learning Koopman operator for closed-loop electrical neurostimulation in epilepsy","date":"2021-03-26","arxiv_id":"2103.14321","repositories_listed":0,"syntology":null},{"url":null,"slug":"source-aware-deep-learning-framework-for-hand","title":"Source Aware Deep Learning Framework for Hand Kinematic Reconstruction using EEG Signal","date":"2021-03-25","arxiv_id":"2103.13862","repositories_listed":0,"syntology":null},{"url":null,"slug":"review-dry-and-non-contact-eeg-electrodes-for","title":"Review Dry and Non-Contact EEG Electrodes for 2010-2021 Years","date":"2021-03-23","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"progress-in-neural-networks-for-eeg-signal-1","title":"Progress in neural networks for EEG signal recognition in 2021","date":"2021-03-19","arxiv_id":"2103.15755","repositories_listed":0,"syntology":null},{"url":null,"slug":"ga-for-feature-selection-of-eeg-heterogeneous","title":"Genetic algorithm for feature selection of EEG heterogeneous data","date":"2021-03-12","arxiv_id":"2103.07117","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-multiway-canonical-correlation-analysis","title":"Deep Multiway Canonical Correlation Analysis for Multi-Subject EEG Normalization","date":"2021-03-11","arxiv_id":"2103.06478","repositories_listed":0,"syntology":null},{"url":null,"slug":"model-agnostic-meta-learning-for-eeg-motor","title":"Model-Agnostic Meta-Learning for EEG Motor Imagery Decoding in Brain-Computer-Interfacing","date":"2021-03-10","arxiv_id":"2103.08664","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-real-time-decoding-of-bimanual-grip","title":"CNNATT: Deep EEG & fNIRS Real-Time Decoding of bimanual forces","date":"2021-03-09","arxiv_id":"2103.05334","repositories_listed":0,"syntology":null},{"url":null,"slug":"hemcnn-deep-learning-enables-decoding-of","title":"HemCNN: Deep Learning enables decoding of fNIRS cortical signals in hand grip motor tasks","date":"2021-03-09","arxiv_id":"2103.05338","repositories_listed":0,"syntology":null},{"url":null,"slug":"inter-subject-deep-transfer-learning-for","title":"Inter-subject Deep Transfer Learning for Motor Imagery EEG Decoding","date":"2021-03-09","arxiv_id":"2103.05351","repositories_listed":0,"syntology":null},{"url":null,"slug":"prefrontal-cortex-functional-connectivity","title":"Prefrontal cortex functional connectivity based on simultaneous record of electrical and hemodynamic responses associated with mental stress","date":"2021-03-08","arxiv_id":"2103.04636","repositories_listed":0,"syntology":null},{"url":null,"slug":"ensemble-approach-for-detection-of-depression","title":"Ensemble approach for detection of depression using EEG features","date":"2021-03-07","arxiv_id":"2103.08467","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-pilot-study-on-visually-stimulated","title":"A Pilot Study on Visually Stimulated Cognitive Tasks for EEG-Based Dementia Recognition","date":"2021-03-05","arxiv_id":"2103.03854","repositories_listed":0,"syntology":null},{"url":null,"slug":"adaptive-gaussian-fuzzy-classifier-for-real","title":"Adaptive Gaussian Fuzzy Classifier for Real-Time Emotion Recognition in Computer Games","date":"2021-03-05","arxiv_id":"2103.03488","repositories_listed":0,"syntology":null},{"url":null,"slug":"scrib-set-classifier-with-class-specific-risk","title":"SCRIB: Set-classifier with Class-specific Risk Bounds for Blackbox Models","date":"2021-03-05","arxiv_id":"2103.03945","repositories_listed":0,"syntology":null},{"url":null,"slug":"auditory-attention-decoding-from-eeg-using","title":"Auditory Attention Decoding from EEG using Convolutional Recurrent Neural Network","date":"2021-03-03","arxiv_id":"2103.02183","repositories_listed":0,"syntology":null},{"url":null,"slug":"decoding-event-related-potential-from-ear-eeg","title":"Decoding Event-related Potential from Ear-EEG Signals based on Ensemble Convolutional Neural Networks in Ambulatory Environment","date":"2021-03-03","arxiv_id":"2103.02197","repositories_listed":0,"syntology":null},{"url":null,"slug":"real-time-vigilance-detection-using-frontal","title":"Real Time Vigilance Detection using Frontal EEG","date":"2021-03-03","arxiv_id":"2103.02169","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-spatial-selective-auditory-attention-of","title":"The Spatial Selective Auditory Attention of Cochlear Implant Users in Different Conversational Sound Levels","date":"2021-03-03","arxiv_id":"2103.02703","repositories_listed":0,"syntology":null},{"url":"/paper/decoding-and-interpreting-cortical-signals","slug":"decoding-and-interpreting-cortical-signals","title":"Decoding and interpreting cortical signals with a compact convolutional neural network","date":"2021-03-02","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"dm-algorithms-in-healthindustry","title":"DM algorithms in healthindustry","date":"2021-03-02","arxiv_id":"2103.01888","repositories_listed":0,"syntology":null},{"url":null,"slug":"neuronal-heterogeneity-modulates-phase","title":"Neuronal heterogeneity modulates phase-synchronization between unidirectionally coupled populations with excitation-inhibition balance","date":"2021-03-02","arxiv_id":"2103.01790","repositories_listed":0,"syntology":null},{"url":null,"slug":"clpvg-circular-limited-penetrable-visibility","title":"CLPVG: Circular limited penetrable visibility graph as a new network model for time series","date":"2021-03-01","arxiv_id":"2104.13772","repositories_listed":0,"syntology":null},{"url":null,"slug":"brain-signals-to-rescue-aphasia-apraxia-and","title":"Brain Signals to Rescue Aphasia, Apraxia and Dysarthria Speech Recognition","date":"2021-02-28","arxiv_id":"2103.00383","repositories_listed":0,"syntology":null},{"url":null,"slug":"unsupervised-domain-adaptation-for-cross","title":"Unsupervised Domain Adaptation for Cross-Subject Few-Shot Neurological Symptom Detection","date":"2021-02-28","arxiv_id":"2103.00606","repositories_listed":0,"syntology":null},{"url":null,"slug":"update-on-the-multimodal-pathophysiological","title":"Update on the multimodal pathophysiological dataset of gradual cerebral ischemia in a cohort of juvenile pigs: auditory, sensory and high-frequency sensory evoked potentials","date":"2021-02-28","arxiv_id":"2103.00640","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-new-neuromorphic-computing-approach-for","title":"A New Neuromorphic Computing Approach for Epileptic Seizure Prediction","date":"2021-02-25","arxiv_id":"2102.12773","repositories_listed":0,"syntology":null},{"url":null,"slug":"dynamic-graph-modeling-of-simultaneous-eeg","title":"Dynamic Graph Modeling of Simultaneous EEG and Eye-tracking Data for Reading Task Identification","date":"2021-02-21","arxiv_id":"2102.11922","repositories_listed":0,"syntology":null},{"url":null,"slug":"edge-sparse-basis-network-an-deep-learning","title":"Edge Sparse Basis Network: A Deep Learning Framework for EEG Source Localization","date":"2021-02-18","arxiv_id":"2102.09188","repositories_listed":0,"syntology":null},{"url":null,"slug":"analysis-of-eeg-data-using-complex-geometric","title":"Analysis of EEG data using complex geometric structurization","date":"2021-02-17","arxiv_id":"2102.09061","repositories_listed":0,"syntology":null},{"url":null,"slug":"decoding-eeg-brain-activity-for-multi-modal","title":"Decoding EEG Brain Activity for Multi-Modal Natural Language Processing","date":"2021-02-17","arxiv_id":"2102.08655","repositories_listed":0,"syntology":null},{"url":null,"slug":"eeg-based-texture-roughness-classification-in","title":"EEG-based Texture Roughness Classification in Active Tactile Exploration with Invariant Representation Learning Networks","date":"2021-02-17","arxiv_id":"2102.08976","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-the-use-of-generative-deep-neural-networks","title":"On the use of generative deep neural networks to synthesize artificial multichannel EEG signals","date":"2021-02-16","arxiv_id":"2102.08061","repositories_listed":0,"syntology":null},{"url":null,"slug":"orthogonal-features-based-eeg-signal","title":"Orthogonal Features-based EEG Signal Denoising using Fractionally Compressed AutoEncoder","date":"2021-02-16","arxiv_id":"2102.08083","repositories_listed":0,"syntology":null},{"url":null,"slug":"geometric-feature-performance-under","title":"Geometric feature performance under downsampling for EEG classification tasks","date":"2021-02-15","arxiv_id":"2102.07669","repositories_listed":0,"syntology":null},{"url":null,"slug":"eegs-disclose-significant-brain-activity","title":"EEGs disclose significant brain activity correlated with synaptic fickleness","date":"2021-02-14","arxiv_id":"2102.07036","repositories_listed":0,"syntology":null},{"url":null,"slug":"mind-the-beat-detecting-audio-onsets-from-eeg","title":"Mind the beat: detecting audio onsets from EEG recordings of music listening","date":"2021-02-12","arxiv_id":"2102.06393","repositories_listed":0,"syntology":null},{"url":null,"slug":"end-to-end-learnable-eeg-channel-selection","title":"End-to-end learnable EEG channel selection for deep neural networks with Gumbel-softmax","date":"2021-02-11","arxiv_id":"2102.09050","repositories_listed":0,"syntology":null},{"url":null,"slug":"boosting-template-based-ssvep-decoding-by","title":"Boosting Template-based SSVEP Decoding by Cross-domain Transfer Learning","date":"2021-02-10","arxiv_id":"2102.05194","repositories_listed":0,"syntology":null},{"url":null,"slug":"common-spatial-generative-adversarial","title":"Common Spatial Generative Adversarial Networks based EEG Data Augmentation for Cross-Subject Brain-Computer Interface","date":"2021-02-08","arxiv_id":"2102.04456","repositories_listed":0,"syntology":null},{"url":null,"slug":"eegfusenet-hybrid-unsupervised-deep-feature","title":"EEGFuseNet: Hybrid Unsupervised Deep Feature Characterization and Fusion for High-Dimensional EEG with An Application to Emotion Recognition","date":"2021-02-07","arxiv_id":"2102.03777","repositories_listed":0,"syntology":null},{"url":null,"slug":"few-shot-time-series-segmentation-using","title":"Few-shot time series segmentation using prototype-defined infinite hidden Markov models","date":"2021-02-07","arxiv_id":"2102.03885","repositories_listed":0,"syntology":null},{"url":null,"slug":"scalingnet-extracting-features-from-raw-eeg","title":"ScalingNet: extracting features from raw EEG data for emotion recognition","date":"2021-02-07","arxiv_id":"2105.13987","repositories_listed":0,"syntology":null},{"url":null,"slug":"coherence-of-working-memory-study-between","title":"Coherence of Working Memory Study Between Deep Neural Network and Neurophysiology","date":"2021-02-06","arxiv_id":"2102.10994","repositories_listed":0,"syntology":null},{"url":null,"slug":"machine-learning-applications-on-neuroimaging","title":"Machine Learning Applications on Neuroimaging for Diagnosis and Prognosis of Epilepsy: A Review","date":"2021-02-05","arxiv_id":"2102.03336","repositories_listed":0,"syntology":null}],"record_sha256":"ee54d343b37970f7b0d88424b01860d17df70e3a984d9e9d137a226ac43ccad5","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}