{"url":"/dataset/k-emocon","name":"K-EmoCon","full_name":null,"description_markdown":"A multimodal dataset with comprehensive annotations of continuous emotions during naturalistic conversations. The dataset contains multimodal measurements, including audiovisual recordings, EEG, and peripheral physiological signals, acquired with off-the-shelf devices from 16 sessions of approximately 10-minute long paired debates on a social issue. \r\n\r\nSource: [K-EmoCon, a multimodal sensor dataset for continuous emotion recognition in naturalistic conversations](/paper/k-emocon-a-multimodal-sensor-dataset-for)","description_withheld":null,"homepage":"https://zenodo.org/record/3814370","introduced_date":null,"introduced_date_note":null,"introduced_by":{"paper":"/paper/k-emocon-a-multimodal-sensor-dataset-for","title":"K-EmoCon, a multimodal sensor dataset for continuous emotion recognition in naturalistic conversations","first_author":"Cheul Young Park","url":null},"license":{"name":"CC BY 4.0","url":"http://creativecommons.org/licenses/by/4.0/"},"modalities":[],"tasks":[{"name":"Emotion Recognition","url":"/task/emotion-recognition","datasets_with_task":"/datasets/task/emotion-recognition"},{"name":"Electroencephalogram (EEG)","url":"/task/eeg","datasets_with_task":"/datasets/task/eeg"}],"languages":[],"variants":["K-EmoCon"],"data_loaders":[{"repo":"https://github.com/2001926342/MachineLearning_Zhouzhihua_ProblemSets","url":"https://github.com/2001926342","frameworks":["pytorch"]}],"num_papers_in_archive":9,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[],"papers_with_a_benchmark_row":[],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}