{"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/2","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":2,"pages_in_order":25,"rows_per_page":100,"rows":[101,200],"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","next":"/task/eeg-1/papers/3","papers":[{"url":"/paper/seizuretransformer-scaling-u-net-with","slug":"seizuretransformer-scaling-u-net-with","title":"SeizureTransformer: Scaling U-Net with Transformer for Simultaneous Time-Step Level Seizure Detection from Long EEG Recordings","date":"2025-04-01","arxiv_id":"2504.00336","repositories_listed":1,"syntology":null},{"url":"/paper/pieeg-kit-bioscience-lab-in-home-for-your","slug":"pieeg-kit-bioscience-lab-in-home-for-your","title":"PiEEG kit - bioscience Lab in home for your Brain and Body","date":"2025-03-19","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/eeg-clip-learning-eeg-representations-from","slug":"eeg-clip-learning-eeg-representations-from","title":"EEG-CLIP : Learning EEG representations from natural language descriptions","date":"2025-03-18","arxiv_id":"2503.16531","repositories_listed":1,"syntology":null},{"url":"/paper/is-limited-participant-diversity-impeding-eeg","slug":"is-limited-participant-diversity-impeding-eeg","title":"Is Limited Participant Diversity Impeding EEG-based Machine Learning?","date":"2025-03-11","arxiv_id":"2503.13497","repositories_listed":1,"syntology":{"n":8,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":0,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/is-limited-participant-diversity-impeding-eeg#ran","syntology_url":"https://syntology.ai/paper/2503.13497","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2503.13497"}},"official":{"repos":["bomatter/participant-diversity-paper"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/mptsnet-integrating-multiscale-periodic-local","slug":"mptsnet-integrating-multiscale-periodic-local","title":"MPTSNet: Integrating Multiscale Periodic Local Patterns and Global Dependencies for Multivariate Time Series Classification","date":"2025-03-07","arxiv_id":"2503.05582","repositories_listed":1,"syntology":null},{"url":"/paper/spatial-distillation-based-distribution","slug":"spatial-distillation-based-distribution","title":"Spatial Distillation based Distribution Alignment (SDDA) for Cross-Headset EEG Classification","date":"2025-03-07","arxiv_id":"2503.05349","repositories_listed":1,"syntology":null},{"url":"/paper/frequency-based-alignment-of-eeg-and-audio","slug":"frequency-based-alignment-of-eeg-and-audio","title":"Frequency-Based Alignment of EEG and Audio Signals Using Contrastive Learning and SincNet for Auditory Attention Detection","date":"2025-03-06","arxiv_id":"2503.04156","repositories_listed":1,"syntology":null},{"url":"/paper/pieeg-kit-bioscience-lab-in-home-for-your-1","slug":"pieeg-kit-bioscience-lab-in-home-for-your-1","title":"PiEEG kit -- bioscience Lab in home for your Brain and Body","date":"2025-03-05","arxiv_id":"2503.13482","repositories_listed":1,"syntology":null},{"url":"/paper/a-novel-fourier-adjacency-transformer-for","slug":"a-novel-fourier-adjacency-transformer-for","title":"A novel Fourier Adjacency Transformer for advanced EEG emotion recognition","date":"2025-02-28","arxiv_id":"2503.13465","repositories_listed":1,"syntology":{"n":14,"n_ran":11,"n_constructed":0,"n_ran_checked":11,"n_instrument":0,"n_unverified":3,"n_honours":1,"n_violates":0,"n_no_contract":10,"n_pointer_only":0,"phrase":"11 ran (of which 0 constructed an object rather than computing a result; 11 with no instrument failure: 1 honoured, 0 violated, 10 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/a-novel-fourier-adjacency-transformer-for#ran","syntology_url":"https://syntology.ai/paper/2503.13465","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2503.13465"}},"official":{"repos":["YanhaoHuang23/FAT"],"state":"official (archive's flag): 11 ran","n_ran":11,"n_constructed":0,"n_ran_no_instrument_failure":11,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/deep-learning-powered-electrical-brain","slug":"deep-learning-powered-electrical-brain","title":"Deep Learning-Powered Electrical Brain Signals Analysis: Advancing Neurological Diagnostics","date":"2025-02-24","arxiv_id":"2502.17213","repositories_listed":1,"syntology":null},{"url":"/paper/dimension-reduction-methods-persistent","slug":"dimension-reduction-methods-persistent","title":"Dimension reduction methods, persistent homology and machine learning for EEG signal analysis of Interictal Epileptic Discharges","date":"2025-02-18","arxiv_id":"2502.12814","repositories_listed":1,"syntology":null},{"url":"/paper/multi-view-contrastive-network-mcnet-for","slug":"multi-view-contrastive-network-mcnet-for","title":"MVCNet: Multi-View Contrastive Network for Motor Imagery Classification","date":"2025-02-18","arxiv_id":"2502.17482","repositories_listed":1,"syntology":null},{"url":"/paper/toward-foundational-model-for-sleep-analysis","slug":"toward-foundational-model-for-sleep-analysis","title":"Toward Foundational Model for Sleep Analysis Using a Multimodal Hybrid Self-Supervised Learning Framework","date":"2025-02-18","arxiv_id":"2502.17481","repositories_listed":1,"syntology":null},{"url":"/paper/mc2sleepnet-multi-modal-cross-masking-with","slug":"mc2sleepnet-multi-modal-cross-masking-with","title":"MC2SleepNet: Multi-modal Cross-masking with Contrastive Learning for Sleep Stage Classification","date":"2025-02-13","arxiv_id":"2502.17470","repositories_listed":1,"syntology":null},{"url":"/paper/cssstn-a-class-sensitive-subject-to-subject","slug":"cssstn-a-class-sensitive-subject-to-subject","title":"CSSSTN: A Class-sensitive Subject-to-subject Semantic Style Transfer Network for EEG Classification in RSVP Tasks","date":"2025-02-12","arxiv_id":"2502.17468","repositories_listed":1,"syntology":null},{"url":"/paper/from-brainwaves-to-brain-scans-a-robust","slug":"from-brainwaves-to-brain-scans-a-robust","title":"From Brainwaves to Brain Scans: A Robust Neural Network for EEG-to-fMRI Synthesis","date":"2025-02-11","arxiv_id":"2502.08025","repositories_listed":1,"syntology":null},{"url":"/paper/retrieving-filter-spectra-in-cnn-for","slug":"retrieving-filter-spectra-in-cnn-for","title":"Retrieving Filter Spectra in CNN for Explainable Sleep Stage Classification","date":"2025-02-10","arxiv_id":"2502.06478","repositories_listed":1,"syntology":null},{"url":"/paper/the-case-for-cleaner-biosignals-high-fidelity","slug":"the-case-for-cleaner-biosignals-high-fidelity","title":"The Case for Cleaner Biosignals: High-fidelity Neural Compressor Enables Transfer from Cleaner iEEG to Noisier EEG","date":"2025-02-10","arxiv_id":"2502.17462","repositories_listed":1,"syntology":{"n":8,"n_ran":6,"n_constructed":0,"n_ran_checked":6,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":0,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/the-case-for-cleaner-biosignals-high-fidelity#ran","syntology_url":"https://syntology.ai/paper/2502.17462","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2502.17462"}},"official":{"repos":["ibm/eeg-ieeg-brain-compressor"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/protecting-intellectual-property-of-eeg-based","slug":"protecting-intellectual-property-of-eeg-based","title":"Protecting Intellectual Property of EEG-based Neural Networks with Watermarking","date":"2025-02-09","arxiv_id":"2502.05931","repositories_listed":1,"syntology":null},{"url":"/paper/decoding-human-attentive-states-from-spatial","slug":"decoding-human-attentive-states-from-spatial","title":"Decoding Human Attentive States from Spatial-temporal EEG Patches Using Transformers","date":"2025-02-06","arxiv_id":"2502.03736","repositories_listed":1,"syntology":null},{"url":"/paper/fine-tuning-strategies-for-continual-online","slug":"fine-tuning-strategies-for-continual-online","title":"Fine-Tuning Strategies for Continual Online EEG Motor Imagery Decoding: Insights from a Large-Scale Longitudinal Study","date":"2025-02-05","arxiv_id":"2502.06828","repositories_listed":1,"syntology":null},{"url":"/paper/incepformernet-a-multi-scale-multi-head","slug":"incepformernet-a-multi-scale-multi-head","title":"IncepFormerNet: A multi-scale multi-head attention network for SSVEP classification","date":"2025-02-04","arxiv_id":"2502.13972","repositories_listed":1,"syntology":null},{"url":"/paper/lead-large-foundation-model-for-eeg-based","slug":"lead-large-foundation-model-for-eeg-based","title":"LEAD: Large Foundation Model for EEG-Based Alzheimer's Disease Detection","date":"2025-02-02","arxiv_id":"2502.01678","repositories_listed":1,"syntology":null},{"url":"/paper/milmer-a-framework-for-multiple-instance","slug":"milmer-a-framework-for-multiple-instance","title":"Milmer: a Framework for Multiple Instance Learning based Multimodal Emotion Recognition","date":"2025-02-01","arxiv_id":"2502.00547","repositories_listed":1,"syntology":null},{"url":"/paper/wearanize-a-multimodal-dataset-for-evaluating","slug":"wearanize-a-multimodal-dataset-for-evaluating","title":"Wearanize+: A Multimodal Dataset for Evaluating Wearable Technologies in Sleep Research","date":"2025-01-30","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/synthetic-data-generation-by-supervised","slug":"synthetic-data-generation-by-supervised","title":"Synthetic Data Generation by Supervised Neural Gas Network for Physiological Emotion Recognition Data","date":"2025-01-19","arxiv_id":"2501.16353","repositories_listed":1,"syntology":null},{"url":"/paper/cueless-eeg-imagined-speech-for-subject","slug":"cueless-eeg-imagined-speech-for-subject","title":"Cueless EEG imagined speech for subject identification: dataset and benchmarks","date":"2025-01-16","arxiv_id":"2501.09700","repositories_listed":1,"syntology":null},{"url":"/paper/multi-task-deep-learning-for-sleep-event","slug":"multi-task-deep-learning-for-sleep-event","title":"Multi-task deep-learning for sleep event detection and stage classification","date":"2025-01-16","arxiv_id":"2501.09519","repositories_listed":1,"syntology":null},{"url":"/paper/on-the-challenges-of-detecting-mci-using-eeg","slug":"on-the-challenges-of-detecting-mci-using-eeg","title":"On the challenges of detecting MCI using EEG in the wild","date":"2025-01-15","arxiv_id":"2501.17871","repositories_listed":1,"syntology":null},{"url":"/paper/estimating-musical-surprisal-in-audio","slug":"estimating-musical-surprisal-in-audio","title":"Estimating Musical Surprisal in Audio","date":"2025-01-13","arxiv_id":"2501.07474","repositories_listed":1,"syntology":null},{"url":"/paper/enhancing-listened-speech-decoding-from-eeg","slug":"enhancing-listened-speech-decoding-from-eeg","title":"Enhancing Listened Speech Decoding from EEG via Parallel Phoneme Sequence Prediction","date":"2025-01-08","arxiv_id":"2501.04844","repositories_listed":1,"syntology":null},{"url":"/paper/improving-ssvep-bci-spellers-with-data","slug":"improving-ssvep-bci-spellers-with-data","title":"Improving SSVEP BCI Spellers With Data Augmentation and Language Models","date":"2024-12-28","arxiv_id":"2412.20052","repositories_listed":1,"syntology":null},{"url":"/paper/eeg-reptile-an-automatized-reptile-based-meta","slug":"eeg-reptile-an-automatized-reptile-based-meta","title":"EEG-Reptile: An Automatized Reptile-Based Meta-Learning Library for BCIs","date":"2024-12-27","arxiv_id":"2412.19725","repositories_listed":1,"syntology":null},{"url":"/paper/real-time-classification-of-eeg-signals-using","slug":"real-time-classification-of-eeg-signals-using","title":"Real-time classification of EEG signals using Machine Learning deployment","date":"2024-12-27","arxiv_id":"2412.19515","repositories_listed":1,"syntology":null},{"url":"/paper/neural-mcrl-neural-multimodal-contrastive","slug":"neural-mcrl-neural-multimodal-contrastive","title":"Neural-MCRL: Neural Multimodal Contrastive Representation Learning for EEG-based Visual Decoding","date":"2024-12-23","arxiv_id":"2412.17337","repositories_listed":1,"syntology":null},{"url":"/paper/markovtype-a-markov-decision-process-strategy","slug":"markovtype-a-markov-decision-process-strategy","title":"MarkovType: A Markov Decision Process Strategy for Non-Invasive Brain-Computer Interfaces Typing Systems","date":"2024-12-20","arxiv_id":"2412.15862","repositories_listed":1,"syntology":null},{"url":"/paper/predicting-artificial-neural-network","slug":"predicting-artificial-neural-network","title":"Predicting Artificial Neural Network Representations to Learn Recognition Model for Music Identification from Brain Recordings","date":"2024-12-20","arxiv_id":"2412.15560","repositories_listed":1,"syntology":null},{"url":"/paper/cae-t-a-channelwise-autoencoder-with","slug":"cae-t-a-channelwise-autoencoder-with","title":"CwA-T: A Channelwise AutoEncoder with Transformer for EEG Abnormality Detection","date":"2024-12-19","arxiv_id":"2412.14522","repositories_listed":1,"syntology":null},{"url":"/paper/cognitioncapturer-decoding-visual-stimuli","slug":"cognitioncapturer-decoding-visual-stimuli","title":"CognitionCapturer: Decoding Visual Stimuli From Human EEG Signal With Multimodal Information","date":"2024-12-13","arxiv_id":"2412.10489","repositories_listed":1,"syntology":null},{"url":"/paper/improve-impact-of-mobile-phones-on-remote","slug":"improve-impact-of-mobile-phones-on-remote","title":"A multimodal dataset for understanding the impact of mobile phones on remote online virtual education","date":"2024-12-13","arxiv_id":"2412.14195","repositories_listed":1,"syntology":null},{"url":"/paper/motor-imagery-classification-for-asynchronous","slug":"motor-imagery-classification-for-asynchronous","title":"Motor Imagery Classification for Asynchronous EEG-Based Brain-Computer Interfaces","date":"2024-12-12","arxiv_id":"2412.09006","repositories_listed":1,"syntology":null},{"url":"/paper/toward-foundation-model-for-multivariate","slug":"toward-foundation-model-for-multivariate","title":"Toward Foundation Model for Multivariate Wearable Sensing of Physiological Signals","date":"2024-12-12","arxiv_id":"2412.09758","repositories_listed":1,"syntology":null},{"url":"/paper/adversarial-filtering-based-evasion-and","slug":"adversarial-filtering-based-evasion-and","title":"Adversarial Filtering Based Evasion and Backdoor Attacks to EEG-Based Brain-Computer Interfaces","date":"2024-12-10","arxiv_id":"2412.07231","repositories_listed":1,"syntology":null},{"url":"/paper/cbramod-a-criss-cross-brain-foundation-model","slug":"cbramod-a-criss-cross-brain-foundation-model","title":"CBraMod: A Criss-Cross Brain Foundation Model for EEG Decoding","date":"2024-12-10","arxiv_id":"2412.07236","repositories_listed":1,"syntology":{"n":5,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 1 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/cbramod-a-criss-cross-brain-foundation-model#ran","syntology_url":"https://syntology.ai/paper/2412.07236","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2412.07236"}},"official":{"repos":["wjq-learning/cbramod"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/t-time-test-time-information-maximization","slug":"t-time-test-time-information-maximization","title":"T-TIME: Test-Time Information Maximization Ensemble for Plug-and-Play BCIs","date":"2024-12-10","arxiv_id":"2412.07228","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":3,"n_no_contract":0,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 3 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/t-time-test-time-information-maximization#ran","syntology_url":"https://syntology.ai/paper/2412.07228","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2412.07228"}},"official":{"repos":["sylyoung/DeepTransferEEG"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/federated-motor-imagery-classification-for","slug":"federated-motor-imagery-classification-for","title":"Federated Motor Imagery Classification for Privacy-Preserving Brain-Computer Interfaces","date":"2024-12-02","arxiv_id":"2412.01079","repositories_listed":1,"syntology":null},{"url":"/paper/the-more-the-better-evaluating-the-role-of","slug":"the-more-the-better-evaluating-the-role-of","title":"The more, the better? Evaluating the role of EEG preprocessing for deep learning applications","date":"2024-11-27","arxiv_id":"2411.18392","repositories_listed":1,"syntology":null},{"url":"/paper/enhanced-cross-dataset-electroencephalogram","slug":"enhanced-cross-dataset-electroencephalogram","title":"Enhanced Cross-Dataset Electroencephalogram-based Emotion Recognition using Unsupervised Domain Adaptation","date":"2024-11-19","arxiv_id":"2411.12852","repositories_listed":1,"syntology":null},{"url":"/paper/a-hybrid-artificial-intelligence-system-for","slug":"a-hybrid-artificial-intelligence-system-for","title":"A Hybrid Artificial Intelligence System for Automated EEG Background Analysis and Report Generation","date":"2024-11-15","arxiv_id":"2411.09874","repositories_listed":1,"syntology":null},{"url":"/paper/pfml-self-supervised-learning-of-time-series","slug":"pfml-self-supervised-learning-of-time-series","title":"PFML: Self-Supervised Learning of Time-Series Data Without Representation Collapse","date":"2024-11-15","arxiv_id":"2411.10087","repositories_listed":1,"syntology":null},{"url":"/paper/eeg-dcnet-a-fast-and-accurate-mi-eeg-dilated-1","slug":"eeg-dcnet-a-fast-and-accurate-mi-eeg-dilated-1","title":"EEG-DCNet: A Fast and Accurate MI-EEG Dilated CNN Classification Method","date":"2024-11-12","arxiv_id":"2411.17705","repositories_listed":1,"syntology":null},{"url":"/paper/flextime-filterbank-learning-to-explain-time","slug":"flextime-filterbank-learning-to-explain-time","title":"FLEXtime: Filterbank learning to explain time series","date":"2024-11-06","arxiv_id":"2411.05841","repositories_listed":1,"syntology":null},{"url":"/paper/elliptical-wishart-distributions-information","slug":"elliptical-wishart-distributions-information","title":"Elliptical Wishart distributions: information geometry, maximum likelihood estimator, performance analysis and statistical learning","date":"2024-11-05","arxiv_id":"2411.02726","repositories_listed":1,"syntology":null},{"url":"/paper/alignment-based-adversarial-training-abat-for","slug":"alignment-based-adversarial-training-abat-for","title":"Alignment-Based Adversarial Training (ABAT) for Improving the Robustness and Accuracy of EEG-Based BCIs","date":"2024-11-04","arxiv_id":"2411.02094","repositories_listed":1,"syntology":null},{"url":"/paper/enhancing-neural-network-interpretability-1","slug":"enhancing-neural-network-interpretability-1","title":"Enhancing Neural Network Interpretability with Feature-Aligned Sparse Autoencoders","date":"2024-11-02","arxiv_id":"2411.01220","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/enhancing-neural-network-interpretability-1#ran","syntology_url":"https://syntology.ai/paper/2411.01220","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2411.01220"}},"official":{"repos":["luke-marks0/mutual-feature-regularization"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/bsd-a-bayesian-framework-for-parametric","slug":"bsd-a-bayesian-framework-for-parametric","title":"BSD: a Bayesian framework for parametric models of neural spectra","date":"2024-10-28","arxiv_id":"2410.20896","repositories_listed":1,"syntology":null},{"url":"/paper/neugpt-unified-multi-modal-neural-gpt","slug":"neugpt-unified-multi-modal-neural-gpt","title":"NeuGPT: Unified multi-modal Neural GPT","date":"2024-10-28","arxiv_id":"2410.20916","repositories_listed":1,"syntology":null},{"url":"/paper/eeg-dif-early-warning-of-epileptic-seizures","slug":"eeg-dif-early-warning-of-epileptic-seizures","title":"EEG-DIF: Early Warning of Epileptic Seizures through Generative Diffusion Model-based Multi-channel EEG Signals Forecasting","date":"2024-10-22","arxiv_id":"2410.17343","repositories_listed":1,"syntology":null},{"url":"/paper/evaluation-of-p300-speller-performance-using","slug":"evaluation-of-p300-speller-performance-using","title":"Evaluation Of P300 Speller Performance Using Large Language Models Along With Cross-Subject Training","date":"2024-10-19","arxiv_id":"2410.15161","repositories_listed":1,"syntology":null},{"url":"/paper/automatic-classification-of-sleep-stages-from","slug":"automatic-classification-of-sleep-stages-from","title":"Automatic Classification of Sleep Stages from EEG Signals Using Riemannian Metrics and Transformer Networks","date":"2024-10-18","arxiv_id":"2410.19819","repositories_listed":1,"syntology":null},{"url":"/paper/how-eeg-preprocessing-shapes-decoding","slug":"how-eeg-preprocessing-shapes-decoding","title":"How EEG preprocessing shapes decoding performance","date":"2024-10-18","arxiv_id":"2410.14453","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/how-eeg-preprocessing-shapes-decoding#ran","syntology_url":"https://syntology.ai/paper/2410.14453","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2410.14453"}},"official":{"repos":["kesslerr/m4d"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/nssi-net-multi-concept-generative-adversarial","slug":"nssi-net-multi-concept-generative-adversarial","title":"NSSI-Net: Multi-Concept Generative Adversarial Network for Non-Suicidal Self-Injury Detection Using High-Dimensional EEG Signals in a Semi-Supervised Learning Framework","date":"2024-10-16","arxiv_id":"2410.12159","repositories_listed":1,"syntology":null},{"url":"/paper/darnet-dual-attention-refinement-network-with","slug":"darnet-dual-attention-refinement-network-with","title":"DARNet: Dual Attention Refinement Network with Spatiotemporal Construction for Auditory Attention Detection","date":"2024-10-15","arxiv_id":"2410.11181","repositories_listed":1,"syntology":{"n":6,"n_ran":6,"n_constructed":0,"n_ran_checked":6,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":6,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/darnet-dual-attention-refinement-network-with#ran","syntology_url":"https://syntology.ai/paper/2410.11181","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2410.11181"}},"official":{"repos":["fchest/darnet"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/slimseiz-efficient-channel-adaptive-seizure","slug":"slimseiz-efficient-channel-adaptive-seizure","title":"SlimSeiz: Efficient Channel-Adaptive Seizure Prediction Using a Mamba-Enhanced Network","date":"2024-10-13","arxiv_id":"2410.09998","repositories_listed":1,"syntology":null},{"url":"/paper/thought2text-text-generation-from-eeg-signal","slug":"thought2text-text-generation-from-eeg-signal","title":"Thought2Text: Text Generation from EEG Signal using Large Language Models (LLMs)","date":"2024-10-10","arxiv_id":"2410.07507","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/thought2text-text-generation-from-eeg-signal#ran","syntology_url":"https://syntology.ai/paper/2410.07507","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2410.07507"}},"official":{"repos":["abhijitmishra/Thought2Text"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/closed-loop-phase-selection-in-eeg-tms-using","slug":"closed-loop-phase-selection-in-eeg-tms-using","title":"Closed-Loop phase selection in EEG-TMS using Bayesian Optimization","date":"2024-10-08","arxiv_id":"2410.05747","repositories_listed":1,"syntology":null},{"url":"/paper/neurobolt-resting-state-eeg-to-fmri-synthesis","slug":"neurobolt-resting-state-eeg-to-fmri-synthesis","title":"NeuroBOLT: Resting-state EEG-to-fMRI Synthesis with Multi-dimensional Feature Mapping","date":"2024-10-07","arxiv_id":"2410.05341","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/neurobolt-resting-state-eeg-to-fmri-synthesis#ran","syntology_url":"https://syntology.ai/paper/2410.05341","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2410.05341"}},"official":{"repos":["soupeeli/NeuroBOLT"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/learning-cortico-muscular-dependence-through","slug":"learning-cortico-muscular-dependence-through","title":"Learning Cortico-Muscular Dependence through Orthonormal Decomposition of Density Ratios","date":"2024-10-04","arxiv_id":"2410.14697","repositories_listed":1,"syntology":null},{"url":"/paper/the-effect-of-acute-stress-on-the","slug":"the-effect-of-acute-stress-on-the","title":"The Effect of Acute Stress on the Interpretability and Generalization of Schizophrenia Predictive Machine Learning Models","date":"2024-10-04","arxiv_id":"2410.19739","repositories_listed":1,"syntology":null},{"url":"/paper/bayes-catsi-a-variational-bayesian-approach","slug":"bayes-catsi-a-variational-bayesian-approach","title":"Bayes-CATSI: A variational Bayesian deep learning framework for medical time series data imputation","date":"2024-10-01","arxiv_id":"2410.01847","repositories_listed":1,"syntology":null},{"url":"/paper/necomimi-neural-cognitive-multimodal-eeg","slug":"necomimi-neural-cognitive-multimodal-eeg","title":"NECOMIMI: Neural-Cognitive Multimodal EEG-informed Image Generation with Diffusion Models","date":"2024-10-01","arxiv_id":"2410.00712","repositories_listed":1,"syntology":null},{"url":"/paper/swim-short-window-cnn-integrated-with-mamba","slug":"swim-short-window-cnn-integrated-with-mamba","title":"SWIM: Short-Window CNN Integrated with Mamba for EEG-Based Auditory Spatial Attention Decoding","date":"2024-09-30","arxiv_id":"2409.19884","repositories_listed":1,"syntology":null},{"url":"/paper/a-survey-of-spatio-temporal-eeg-data-analysis","slug":"a-survey-of-spatio-temporal-eeg-data-analysis","title":"A Survey of Spatio-Temporal EEG data Analysis: from Models to Applications","date":"2024-09-26","arxiv_id":"2410.08224","repositories_listed":1,"syntology":null},{"url":"/paper/eegunity-open-source-tool-in-facilitating","slug":"eegunity-open-source-tool-in-facilitating","title":"EEGUnity: Open-Source Tool in Facilitating Unified EEG Datasets Towards Large-Scale EEG Model","date":"2024-09-24","arxiv_id":"2410.07196","repositories_listed":1,"syntology":null},{"url":"/paper/eeg-based-decoding-of-selective-visual","slug":"eeg-based-decoding-of-selective-visual","title":"EEG-based Decoding of Selective Visual Attention in Superimposed Videos","date":"2024-09-19","arxiv_id":"2409.12562","repositories_listed":1,"syntology":null},{"url":"/paper/guess-what-i-think-streamlined-eeg-to-image","slug":"guess-what-i-think-streamlined-eeg-to-image","title":"Guess What I Think: Streamlined EEG-to-Image Generation with Latent Diffusion Models","date":"2024-09-17","arxiv_id":"2410.02780","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":1,"n_instrument":2,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":3,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/guess-what-i-think-streamlined-eeg-to-image#ran","syntology_url":"https://syntology.ai/paper/2410.02780","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2410.02780"}},"official":{"repos":["luigisigillo/gwit"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official","unlocated"]}}},{"url":"/paper/an-ultra-low-power-wearable-bmi-system-with","slug":"an-ultra-low-power-wearable-bmi-system-with","title":"An Ultra-Low Power Wearable BMI System with Continual Learning Capabilities","date":"2024-09-16","arxiv_id":"2409.10654","repositories_listed":1,"syntology":null},{"url":"/paper/https-arxiv-org-pdf-2409-07491","slug":"https-arxiv-org-pdf-2409-07491","title":"https://arxiv.org/pdf/2409.07491","date":"2024-09-13","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/phemonet-a-multimodal-network-for","slug":"phemonet-a-multimodal-network-for","title":"PHemoNet: A Multimodal Network for Physiological Signals","date":"2024-09-13","arxiv_id":"2410.00010","repositories_listed":1,"syntology":null},{"url":"/paper/pieeg-16-to-measure-16-eeg-channels-with","slug":"pieeg-16-to-measure-16-eeg-channels-with","title":"PiEEG-16 to Measure 16 EEG Channels with Raspberry Pi for Brain-Computer Interfaces and EEG devices","date":"2024-09-13","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/train-on-request-an-on-device-continual","slug":"train-on-request-an-on-device-continual","title":"Train-On-Request: An On-Device Continual Learning Workflow for Adaptive Real-World Brain Machine Interfaces","date":"2024-09-13","arxiv_id":"2409.09161","repositories_listed":1,"syntology":null},{"url":"/paper/pieeg-16-to-measure-16-eeg-channels-with-1","slug":"pieeg-16-to-measure-16-eeg-channels-with-1","title":"PiEEG-16 to Measure 16 EEG Channels with Raspberry Pi for Brain-Computer Interfaces and EEG devices","date":"2024-09-08","arxiv_id":"2409.07491","repositories_listed":1,"syntology":null},{"url":"/paper/mixnet-joining-force-of-classical-and-modern","slug":"mixnet-joining-force-of-classical-and-modern","title":"MixNet: Joining Force of Classical and Modern Approaches Toward the Comprehensive Pipeline in Motor Imagery EEG Classification","date":"2024-09-06","arxiv_id":"2409.04104","repositories_listed":1,"syntology":null},{"url":"/paper/ctnet-a-convolutional-transformer-network-for","slug":"ctnet-a-convolutional-transformer-network-for","title":"CTNet: A Convolutional Transformer Network for EEG-Based Motor Imagery Classification","date":"2024-08-30","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/efficient-estimation-of-unique-components-in","slug":"efficient-estimation-of-unique-components-in","title":"Efficient Estimation of Unique Components in Independent Component Analysis by Matrix Representation","date":"2024-08-30","arxiv_id":"2408.17118","repositories_listed":1,"syntology":null},{"url":"/paper/classification-of-epileptic-seizures-in-eeg","slug":"classification-of-epileptic-seizures-in-eeg","title":"Classification of epileptic seizures in EEG data based on iterative gated graph convolution network","date":"2024-08-29","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/toward-robust-early-detection-of-alzheimer-s","slug":"toward-robust-early-detection-of-alzheimer-s","title":"Toward Robust Early Detection of Alzheimer's Disease via an Integrated Multimodal Learning Approach","date":"2024-08-29","arxiv_id":"2408.16343","repositories_listed":1,"syntology":null},{"url":"/paper/neurolm-a-universal-multi-task-foundation","slug":"neurolm-a-universal-multi-task-foundation","title":"NeuroLM: A Universal Multi-task Foundation Model for Bridging the Gap between Language and EEG Signals","date":"2024-08-27","arxiv_id":"2409.00101","repositories_listed":1,"syntology":{"n":7,"n_ran":5,"n_constructed":0,"n_ran_checked":3,"n_instrument":2,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":3,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 2 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/neurolm-a-universal-multi-task-foundation#ran","syntology_url":"https://syntology.ai/paper/2409.00101","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2409.00101"}},"official":{"repos":["935963004/neurolm"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/symbolic-dynamics-of-joint-brain-states","slug":"symbolic-dynamics-of-joint-brain-states","title":"Symbolic dynamics of joint brain states during dyadic coordination","date":"2024-08-23","arxiv_id":"2408.13360","repositories_listed":1,"syntology":null},{"url":"/paper/parkinson-s-disease-classification-via-eeg","slug":"parkinson-s-disease-classification-via-eeg","title":"Parkinson's Disease Classification via EEG: All You Need is a Single Convolutional Layer","date":"2024-08-19","arxiv_id":"2408.10457","repositories_listed":1,"syntology":null},{"url":"/paper/adformer-a-multi-granularity-transformer-for","slug":"adformer-a-multi-granularity-transformer-for","title":"ADformer: A Multi-Granularity Transformer for EEG-Based Alzheimer's Disease Assessment","date":"2024-08-17","arxiv_id":"2409.00032","repositories_listed":1,"syntology":null},{"url":"/paper/speed-scalable-preprocessing-of-eeg-data-for","slug":"speed-scalable-preprocessing-of-eeg-data-for","title":"SPEED: Scalable Preprocessing of EEG Data for Self-Supervised Learning","date":"2024-08-15","arxiv_id":"2408.08065","repositories_listed":1,"syntology":null},{"url":"/paper/lipcot-linear-predictive-coding-based","slug":"lipcot-linear-predictive-coding-based","title":"LiPCoT: Linear Predictive Coding based Tokenizer for Self-supervised Learning of Time Series Data via Language Models","date":"2024-08-14","arxiv_id":"2408.07292","repositories_listed":1,"syntology":null},{"url":"/paper/advancing-eeg-based-gaze-prediction-using","slug":"advancing-eeg-based-gaze-prediction-using","title":"Advancing EEG-Based Gaze Prediction Using Depthwise Separable Convolution and Enhanced Pre-Processing","date":"2024-08-06","arxiv_id":"2408.03480","repositories_listed":1,"syntology":null},{"url":"/paper/eegmobile-enhancing-speed-and-accuracy-in-eeg","slug":"eegmobile-enhancing-speed-and-accuracy-in-eeg","title":"EEGMobile: Enhancing Speed and Accuracy in EEG-Based Gaze Prediction with Advanced Mobile Architectures","date":"2024-08-06","arxiv_id":"2408.03449","repositories_listed":1,"syntology":null},{"url":"/paper/effect-of-kernel-size-on-cnn-vision","slug":"effect-of-kernel-size-on-cnn-vision","title":"Effect of Kernel Size on CNN-Vision-Transformer-Based Gaze Prediction Using Electroencephalography Data","date":"2024-08-06","arxiv_id":"2408.03478","repositories_listed":1,"syntology":null},{"url":"/paper/2408-02760","slug":"2408-02760","title":"Classification of Raw MEG/EEG Data with Detach-Rocket Ensemble: An Improved ROCKET Algorithm for Multivariate Time Series Analysis","date":"2024-08-05","arxiv_id":"2408.02760","repositories_listed":1,"syntology":null},{"url":"/paper/single-channel-electroencephalography","slug":"single-channel-electroencephalography","title":"Single-channel electroencephalography decomposition by detector-atom network and its pre-trained model","date":"2024-08-05","arxiv_id":"2408.02185","repositories_listed":1,"syntology":null},{"url":"/paper/feature-interpretability-in-bcis-exploring","slug":"feature-interpretability-in-bcis-exploring","title":"Feature interpretability in BCIs: exploring the role of network lateralization","date":"2024-07-16","arxiv_id":"2407.11617","repositories_listed":1,"syntology":null},{"url":"/paper/enhancing-adhd-diagnosis-with-eeg-the","slug":"enhancing-adhd-diagnosis-with-eeg-the","title":"Refining ADHD diagnosis with EEG: The impact of preprocessing and temporal segmentation on classification accuracy","date":"2024-07-11","arxiv_id":"2407.08316","repositories_listed":1,"syntology":null}],"record_sha256":"c33175173b6341d4769411b500298ca0b2fa09288ea141f12ef42715a49b76c5","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}