{"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-1/papers/17","list_of":"/task/eeg-1","task":"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":25,"rows_per_page":100,"rows":[1601,1700],"of":2431,"counts":{"archive_papers_tagged":2431,"with_a_code_link":585,"where_syntology_ran_a_sample":64,"not_listed_spam_title":0,"listed":2431,"listed_where_code_ran":64,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":49,"every_run_a_failure_of_syntologys_instrument":15,"listed_with_a_run_with_no_instrument_failure":49,"listed_every_run_a_failure_of_syntologys_instrument":15,"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-1","prev":"/task/eeg-1/papers/16","next":"/task/eeg-1/papers/18","papers":[{"url":null,"slug":"progressive-graph-convolution-network-for-eeg","title":"Progressive Graph Convolution Network for EEG Emotion Recognition","date":"2021-12-14","arxiv_id":"2112.09069","repositories_listed":0,"syntology":null},{"url":null,"slug":"differential-eeg-characteristics-during","title":"Differential EEG Characteristics during Working Memory Encoding and Re-encoding","date":"2021-12-13","arxiv_id":"2112.06464","repositories_listed":0,"syntology":null},{"url":null,"slug":"overview-of-the-mediaeval-2021-predicting","title":"Overview of The MediaEval 2021 Predicting Media Memorability Task","date":"2021-12-11","arxiv_id":"2112.05982","repositories_listed":0,"syntology":null},{"url":null,"slug":"dripp-driven-point-processes-to-model-stimuli-1","title":"DriPP: Driven Point Processes to Model Stimuli Induced Patterns in M/EEG Signals","date":"2021-12-08","arxiv_id":"2112.06652","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-open-world-eeg-decoding-via-deep","title":"Toward Open-World Electroencephalogram Decoding Via Deep Learning: A Comprehensive Survey","date":"2021-12-08","arxiv_id":"2112.06654","repositories_listed":0,"syntology":null},{"url":null,"slug":"extracting-seizure-onset-from-surface-eeg","title":"Extracting seizure onset from surface EEG with Independent Component Analysis: insights from simultaneous scalp and intracerebral EEG","date":"2021-12-02","arxiv_id":"2112.01092","repositories_listed":0,"syntology":null},{"url":null,"slug":"comparison-of-inverse-problem-linear-and-non","title":"Comparison of inverse problem linear and non-linear methods for localization source: a combined TMS-EEG study","date":"2021-11-30","arxiv_id":"2112.00139","repositories_listed":0,"syntology":null},{"url":null,"slug":"automated-detection-of-patients-in-hospital","title":"Automated Detection of Patients in Hospital Video Recordings","date":"2021-11-28","arxiv_id":"2111.14270","repositories_listed":0,"syntology":null},{"url":null,"slug":"are-brain-computer-interfaces-feasible-with","title":"Are Brain-Computer Interfaces Feasible with Integrated Photonic Chips?","date":"2021-11-22","arxiv_id":"2112.01249","repositories_listed":0,"syntology":null},{"url":null,"slug":"structure-preserving-graph-kernel-for-brain","title":"Structure-Preserving Graph Kernel for Brain Network Classification","date":"2021-11-21","arxiv_id":"2111.10803","repositories_listed":0,"syntology":null},{"url":null,"slug":"novel-eeg-based-schizophrenia-detection-with","title":"Novel EEG based Schizophrenia Detection with IoMT Framework for Smart Healthcare","date":"2021-11-19","arxiv_id":"2111.11298","repositories_listed":0,"syntology":null},{"url":null,"slug":"reconsidering-spatial-priors-in-eeg-source","title":"Reconsidering Spatial Priors In EEG Source Estimation: Does White Matter Contribute to EEG Rhythms?","date":"2021-11-17","arxiv_id":"2111.08939","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-time-series-scale-mixture-model-of-eeg-with","title":"A Time-Series Scale Mixture Model of EEG with a Hidden Markov Structure for Epileptic Seizure Detection","date":"2021-11-12","arxiv_id":"2111.06526","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-novel-tsk-fuzzy-system-incorporating-multi","title":"A Novel TSK Fuzzy System Incorporating Multi-view Collaborative Transfer Learning for Personalized Epileptic EEG Detection","date":"2021-11-11","arxiv_id":"2111.08457","repositories_listed":0,"syntology":null},{"url":null,"slug":"benefit-aware-early-prediction-of-health","title":"Benefit-aware Early Prediction of Health Outcomes on Multivariate EEG Time Series","date":"2021-11-11","arxiv_id":"2111.06032","repositories_listed":0,"syntology":null},{"url":null,"slug":"symptoms-of-depersonalisation-derealisation","title":"Symptoms of depersonalisation/derealisation disorder as measured by brain electrical activity: A systematic review","date":"2021-11-11","arxiv_id":"2111.06126","repositories_listed":0,"syntology":null},{"url":null,"slug":"assessing-learned-features-of-deep-learning","title":"Assessing learned features of Deep Learning applied to EEG","date":"2021-11-08","arxiv_id":"2111.04309","repositories_listed":0,"syntology":null},{"url":null,"slug":"automated-human-mind-reading-using-eeg","title":"Automated Human Mind Reading Using EEG Signals for Seizure Detection","date":"2021-11-05","arxiv_id":"2111.03270","repositories_listed":0,"syntology":null},{"url":null,"slug":"epilnet-a-novel-approach-to-iot-based","title":"EpilNet: A Novel Approach to IoT based Epileptic Seizure Prediction and Diagnosis System using Artificial Intelligence","date":"2021-11-05","arxiv_id":"2111.03265","repositories_listed":0,"syntology":null},{"url":null,"slug":"neural-network-based-epileptic-eeg-detection","title":"Neural Network Based Epileptic EEG Detection and Classification","date":"2021-11-05","arxiv_id":"2111.03268","repositories_listed":0,"syntology":null},{"url":null,"slug":"application-of-machine-learning-to-sleep","title":"Application of Machine Learning to Sleep Stage Classification","date":"2021-11-04","arxiv_id":"2111.03085","repositories_listed":0,"syntology":null},{"url":null,"slug":"automatic-sleep-staging-recent-development","title":"Automatic Sleep Staging of EEG Signals: Recent Development, Challenges, and Future Directions","date":"2021-11-03","arxiv_id":"2111.08446","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-neural-networks-on-eeg-signals-to-1","title":"Deep Neural Networks on EEG Signals to Predict Auditory Attention Score Using Gramian Angular Difference Field","date":"2021-10-24","arxiv_id":"2110.12503","repositories_listed":0,"syntology":null},{"url":null,"slug":"high-sensitivity-electric-potential-sensors","title":"High-Sensitivity Electric Potential Sensors for Non-Contact Monitoring of Physiological Signals","date":"2021-10-23","arxiv_id":"2110.12313","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-channel-attention-based-mlp-mixer-network","title":"A channel attention based MLP-Mixer network for motor imagery decoding with EEG","date":"2021-10-21","arxiv_id":"2110.10939","repositories_listed":0,"syntology":null},{"url":null,"slug":"cortical-representations-of-auditory","title":"Cortical representations of Auditory Perception using Graph Independent Component on EEG","date":"2021-10-21","arxiv_id":"2110.12904","repositories_listed":0,"syntology":null},{"url":null,"slug":"eegminer-discovering-interpretable-features","title":"EEGminer: Discovering Interpretable Features of Brain Activity with Learnable Filters","date":"2021-10-19","arxiv_id":"2110.10009","repositories_listed":0,"syntology":null},{"url":null,"slug":"riemannian-classification-of-eeg-signals-with","title":"Riemannian classification of EEG signals with missing values","date":"2021-10-19","arxiv_id":"2110.10011","repositories_listed":0,"syntology":null},{"url":null,"slug":"joint-scsp-lrom-a-novel-approach-to-detect","title":"Joint SCSP-LROM: A novel approach to detect Cerebrovascular Anomalies from EEG signals","date":"2021-10-17","arxiv_id":"2110.08942","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-eeg-signals-codification-using","title":"Towards EEG signals codification using contrastiveloss","date":"2021-10-16","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"web-search-via-an-efficient-and-effective","title":"Web Search via an Efficient and Effective Brain-Machine Interface","date":"2021-10-14","arxiv_id":"2110.07225","repositories_listed":0,"syntology":null},{"url":null,"slug":"end-to-end-translation-of-human-neural","title":"End-to-end translation of human neural activity to speech with a dual-dual generative adversarial network","date":"2021-10-13","arxiv_id":"2110.06634","repositories_listed":0,"syntology":null},{"url":null,"slug":"exploring-multiple-scales-and-bi-hemispheric","title":"Simultaneously exploring multi-scale and asymmetric EEG features for emotion recognition","date":"2021-10-13","arxiv_id":"2110.06462","repositories_listed":0,"syntology":null},{"url":null,"slug":"positional-spectral-temporal-attention-in-3d","title":"Positional-Spectral-Temporal Attention in 3D Convolutional Neural Networks for EEG Emotion Recognition","date":"2021-10-13","arxiv_id":"2110.09955","repositories_listed":0,"syntology":null},{"url":null,"slug":"real-time-eeg-based-emotion-recognition-using","title":"Real-time EEG-based Emotion Recognition using Discrete Wavelet Transforms on Full and Reduced Channel Signals","date":"2021-10-11","arxiv_id":"2110.05635","repositories_listed":0,"syntology":null},{"url":null,"slug":"sensoring-and-application-of-multimodal-data","title":"Sensoring and Application of Multimodal Data for the Detection of Freezing of Gait in Parkinson's Disease","date":"2021-10-09","arxiv_id":"2110.04444","repositories_listed":0,"syntology":null},{"url":null,"slug":"novel-eeg-based-bcis-for-elderly","title":"Novel EEG-based BCIs for Elderly Rehabilitation Enhancement","date":"2021-10-08","arxiv_id":"2110.03966","repositories_listed":0,"syntology":null},{"url":null,"slug":"eeg-functional-connectivity-and-deep-learning","title":"EEG functional connectivity and deep learning for automatic diagnosis of brain disorders: Alzheimer's disease and schizophrenia","date":"2021-10-07","arxiv_id":"2110.06140","repositories_listed":0,"syntology":null},{"url":null,"slug":"time-series-forecasting-using-manifold","title":"Time Series Forecasting Using Manifold Learning","date":"2021-10-07","arxiv_id":"2110.03625","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-case-study-on-profiling-of-an-eeg-based","title":"A case study on profiling of an EEG-based brain decoding interface on Cloud and Edge servers","date":"2021-10-04","arxiv_id":"2110.02785","repositories_listed":0,"syntology":null},{"url":null,"slug":"using-single-trial-representational","title":"Using Single-Trial Representational Similarity Analysis with EEG to track semantic similarity in emotional word processing","date":"2021-10-04","arxiv_id":"2110.03529","repositories_listed":0,"syntology":null},{"url":null,"slug":"soul-an-energy-efficient-unsupervised-online","title":"SOUL: An Energy-Efficient Unsupervised Online Learning Seizure Detection Classifier","date":"2021-10-01","arxiv_id":"2110.02169","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-convolutional-recurrent-neural-network","title":"Deep convolutional recurrent neural network for short-interval EEG motor imagery classification","date":"2021-09-29","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-neural-networks-on-eeg-signals-to","title":"Deep Neural Networks on EEG signals to predict Attention Score using Gramian Angular Difference Field","date":"2021-09-29","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"pdaml-a-pseudo-domain-adaptation-paradigm-for","title":"PDAML: A Pseudo Domain Adaptation Paradigm for Subject-independent EEG-based Emotion Recognition","date":"2021-09-29","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"practical-adversarial-attacks-on-brain","title":"Practical Adversarial Attacks on Brain--Computer Interfaces","date":"2021-09-29","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"recognition-of-eeg-signals-from-imagined","title":"Recognition of EEG Signals from Imagined Vowels Using Deep Learning Methods","date":"2021-09-29","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"time-lapse-learning-to-say-i-don-t-know","title":"TIME-LAPSE: Learning to say “I don't know” through spatio-temporal uncertainty scoring","date":"2021-09-29","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"variational-component-decoder-for-source","title":"Variational Component Decoder for Source Extraction from Nonlinear Mixture","date":"2021-09-29","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"eeg-based-stress-analysis-using-rhythm","title":"EEG based stress analysis using rhythm specific spectral feature for video gameplay","date":"2021-09-27","arxiv_id":"2109.13200","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-efficient-epileptic-seizure-detection","title":"An Efficient Epileptic Seizure Detection Technique using Discrete Wavelet Transform and Machine Learning Classifiers","date":"2021-09-26","arxiv_id":"2109.13811","repositories_listed":0,"syntology":null},{"url":null,"slug":"holistic-semi-supervised-approaches-for-eeg","title":"Holistic Semi-Supervised Approaches for EEG Representation Learning","date":"2021-09-24","arxiv_id":"2109.11732","repositories_listed":0,"syntology":null},{"url":null,"slug":"improving-the-spectral-resolution-of-fmri","title":"Improving the spectral resolution of fMRI signals through the temporal de-correlation approach","date":"2021-09-24","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"eeg-signal-processing-using-wavelets-for","title":"EEG Signal Processing using Wavelets for Accurate Seizure Detection through Cost Sensitive Data Mining","date":"2021-09-22","arxiv_id":"2109.13818","repositories_listed":0,"syntology":null},{"url":null,"slug":"predicting-stress-in-remote-learning-via","title":"Predicting Stress in Remote Learning via Advanced Deep Learning Technologies","date":"2021-09-22","arxiv_id":"2109.11076","repositories_listed":0,"syntology":null},{"url":null,"slug":"why-don-t-you-click-neural-correlates-of-non","title":"Why Don't You Click: Neural Correlates of Non-Click Behaviors in Web Search","date":"2021-09-22","arxiv_id":"2109.10560","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-the-classification-of-error-related","title":"Towards the Classification of Error-Related Potentials using Riemannian Geometry","date":"2021-09-21","arxiv_id":"2109.13085","repositories_listed":0,"syntology":null},{"url":null,"slug":"contrastive-learning-of-subject-invariant-eeg","title":"Contrastive Learning of Subject-Invariant EEG Representations for Cross-Subject Emotion Recognition","date":"2021-09-20","arxiv_id":"2109.09559","repositories_listed":0,"syntology":null},{"url":null,"slug":"functional-switching-among-dynamic-neuronal","title":"Functional switching among dynamic neuronal hub-nodes in the brain induces transition of cognitive states","date":"2021-09-19","arxiv_id":"2109.09224","repositories_listed":0,"syntology":null},{"url":null,"slug":"challenges-of-driver-drowsiness-prediction","title":"Challenges of Driver Drowsiness Prediction: The Remaining Steps to Implementation","date":"2021-09-17","arxiv_id":"2109.08355","repositories_listed":0,"syntology":null},{"url":null,"slug":"seizure-pathways-and-seizure-durations-can","title":"Seizure pathways and seizure durations can vary independently within individual patients with focal epilepsy","date":"2021-09-14","arxiv_id":"2109.06672","repositories_listed":0,"syntology":null},{"url":null,"slug":"score-it-a-machine-learning-based-tool-for","title":"SCORE-IT: A Machine Learning-based Tool for Automatic Standardization of EEG Reports","date":"2021-09-13","arxiv_id":"2109.05694","repositories_listed":0,"syntology":null},{"url":null,"slug":"eegdnet-fusing-non-local-and-local-self","title":"EEGDnet: Fusing Non-Local and Local Self-Similarity for 1-D EEG Signal Denoising with 2-D Transformer","date":"2021-09-09","arxiv_id":"2109.04235","repositories_listed":0,"syntology":null},{"url":null,"slug":"ganser-a-self-supervised-data-augmentation","title":"GANSER: A Self-supervised Data Augmentation Framework for EEG-based Emotion Recognition","date":"2021-09-07","arxiv_id":"2109.03124","repositories_listed":0,"syntology":null},{"url":null,"slug":"detection-of-epileptic-seizures-on-eeg","title":"Detection of Epileptic Seizures on EEG Signals Using ANFIS Classifier, Autoencoders and Fuzzy Entropies","date":"2021-09-06","arxiv_id":"2109.04364","repositories_listed":0,"syntology":null},{"url":null,"slug":"automatic-diagnosis-of-schizophrenia-using","title":"Automatic Diagnosis of Schizophrenia in EEG Signals Using CNN-LSTM Models","date":"2021-09-02","arxiv_id":"2109.01120","repositories_listed":0,"syntology":null},{"url":null,"slug":"mutualgraphnet-a-novel-model-for-motor","title":"MutualGraphNet: A novel model for motor imagery classification","date":"2021-09-02","arxiv_id":"2109.04361","repositories_listed":0,"syntology":null},{"url":null,"slug":"seizure-classification-of-eeg-based-on","title":"Seizure Classification of EEG based on Wavelet Signal Denoising Using a Novel Channel Selection Algorithm","date":"2021-09-02","arxiv_id":"2109.00852","repositories_listed":0,"syntology":null},{"url":null,"slug":"eeg-connectivity-a-fundamental-guide-and","title":"EEG-connectivity: A fundamental guide and checklist for optimal study design and evaluation","date":"2021-08-31","arxiv_id":"2108.13611","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-electroencephalography-connectome","title":"An Electroencephalography connectome predictive model of major depressive disorder severity","date":"2021-08-25","arxiv_id":"2108.11064","repositories_listed":0,"syntology":null},{"url":null,"slug":"deepsleepnet-lite-a-simplified-automatic","title":"DeepSleepNet-Lite: A Simplified Automatic Sleep Stage Scoring Model with Uncertainty Estimates","date":"2021-08-24","arxiv_id":"2108.10600","repositories_listed":0,"syntology":null},{"url":null,"slug":"eeg-based-classification-of-drivers-attention","title":"EEG-based Classification of Drivers Attention using Convolutional Neural Network","date":"2021-08-23","arxiv_id":"2108.10062","repositories_listed":0,"syntology":null},{"url":null,"slug":"electroencephalogram-signal-processing-with","title":"Electroencephalogram Signal Processing with Independent Component Analysis and Cognitive Stress Classification using Convolutional Neural Networks","date":"2021-08-22","arxiv_id":"2108.09817","repositories_listed":0,"syntology":null},{"url":null,"slug":"using-growth-transform-dynamical-systems-for","title":"Using growth transform dynamical systems for spatio-temporal data sonification","date":"2021-08-21","arxiv_id":"2108.09537","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-end-to-end-deep-learning-approach-for-1","title":"An End-to-End Deep Learning Approach for Epileptic Seizure Prediction","date":"2021-08-17","arxiv_id":"2108.07453","repositories_listed":0,"syntology":null},{"url":null,"slug":"multiscale-wavelet-transfer-entropy-with","title":"Multiscale Wavelet Transfer Entropy with Application to Corticomuscular Coupling Analysis","date":"2021-08-09","arxiv_id":"2108.04152","repositories_listed":0,"syntology":null},{"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}],"record_sha256":"215c320face877888862dc016e1d6ea3ec87054119be530c418837f422268a4e","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}