{"about":{"site":"https://codewithpapers.app","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.","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"},"url":"/paper/data-augmentation-for-learning-predictive","title":"Data augmentation for learning predictive models on EEG: a systematic comparison","arxiv_id":"2206.14483","date":"2022-06-29","proceeding":null,"authors":["Cédric Rommel","Joseph Paillard","Thomas Moreau","Alexandre Gramfort"],"abstract":"Objective: The use of deep learning for electroencephalography (EEG) classification tasks has been rapidly growing in the last years, yet its application has been limited by the relatively small size of EEG datasets. Data augmentation, which consists in artificially increasing the size of the dataset during training, can be employed to alleviate this problem. While a few augmentation transformations for EEG data have been proposed in the literature, their positive impact on performance is often evaluated on a single dataset and compared to one or two competing augmentation methods. This work proposes to better validate the existing data augmentation approaches through a unified and exhaustive analysis. Approach: We compare quantitatively 13 different augmentations with two different predictive tasks, datasets and models, using three different types of experiments. Main results: We demonstrate that employing the adequate data augmentations can bring up to 45% accuracy improvements in low data regimes compared to the same model trained without any augmentation. Our experiments also show that there is no single best augmentation strategy, as the good augmentations differ on each task. Significance: Our results highlight the best data augmentations to consider for sleep stage classification and motor imagery brain-computer interfaces. More broadly, it demonstrates that EEG classification tasks benefit from adequate data augmentation","url_abs":"https://arxiv.org/abs/2206.14483v2","url_pdf":"https://arxiv.org/pdf/2206.14483v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"data-augmentation-for-learning-predictive","repo_url":"https://github.com/eeg-augmentation-benchmark/eeg-augmentation-benchmark-2022","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"data-augmentation","task_name":"Data Augmentation"},{"task_slug":"eeg-1","task_name":"EEG"},{"task_slug":"eeg","task_name":"Electroencephalogram (EEG)"},{"task_slug":"motor-imagery","task_name":"Motor Imagery"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2206.14483","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2206.14483"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/eeg-augmentation-benchmark/eeg-augmentation-benchmark-2022","reach":null}],"summary":{"ran_draft_wrong":2},"by_repo_kind":{"official":{"samples":2,"ran":2,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":0,"samples":[{"code_sha256_prefix":"df206e44560aaec7","entry":"get_augmentation","repo":"eeg-augmentation-benchmark/eeg-augmentation-benchmark-2022","repo_kind":"official","path":"eeg_augmentation_benchmark/param_search_utils.py","file_url":"https://github.com/eeg-augmentation-benchmark/eeg-augmentation-benchmark-2022/blob/HEAD/eeg_augmentation_benchmark/param_search_utils.py","link_basis":"first_harvest_node","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-2-Clause","inline_ok":true,"mcp_get_code":{"code_sha256":"df206e44560aaec7"}},{"code_sha256_prefix":"04e3b1f0f8314d4b","entry":"plot_boxplot","repo":"eeg-augmentation-benchmark/eeg-augmentation-benchmark-2022","repo_kind":"official","path":"plots/plot_learning_curve.py","file_url":"https://github.com/eeg-augmentation-benchmark/eeg-augmentation-benchmark-2022/blob/HEAD/plots/plot_learning_curve.py","link_basis":"first_harvest_node","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"BSD-2-Clause","inline_ok":true,"mcp_get_code":{"code_sha256":"04e3b1f0f8314d4b"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}