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    <responseDate>2026-10-15T15:31:15Z</responseDate>
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            <header>
                <identifier>oai:data.mendeley.com/3kd62t4dt3.1</identifier>
                <datestamp>2026-06-30T04:11:48Z</datestamp>
            </header>
            <metadata><oai_dc:dc xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">
    <dc:creator id="https://orcid.org/0009-0000-6134-8041">Csanády, Bálint</dc:creator>
    <dc:title>Continuous EEG Recordings During Coherent and Scene-Scrambled Film Viewing</dc:title>
    <dc:publisher>Mendeley Data</dc:publisher>
    <dc:description>This dataset contains continuous EEG recordings collected during naturalistic film viewing for a study of transition-related potentials (TRPs) and narrative comprehension. Thirty-four participants watched two short narrative films, The Art of Love and City Lights. Each film had a coherent original version and an incoherent scene-scrambled version. The experiment used a counterbalanced crossover design: each participant watched one coherent film and one incoherent film.

EEG was recorded with a BioSemi ActiveTwo system at 512 Hz using a 32-channel scalp montage, with additional electrodes recorded during acquisition. The dataset includes continuous EEG recordings, condition labels, participant/session identifiers, and frame-accurate annotations of cinematic cuts. These cut annotations allow extraction of cut-locked EEG responses and comparison of TRPs across coherent and incoherent narrative contexts.

The dataset supports analyses of naturalistic EEG, event-locked responses during continuous stimulation, narrative-context effects, and EEG-based detection of cinematic transitions. It was used to evaluate whether cut-locked TRPs exhibit ERP-like temporal structure, whether they are modulated by narrative coherence, and whether cut-related EEG signatures can be recovered from group-averaged continuous EEG using deep neural networks.

The data are intended for research on naturalistic cognition, EEG event detection, film comprehension, narrative processing, and transition-related potentials.

Folder structure:

EEG_Cut_dataset/
├── recordings/
│   ├── art_movies/
│   │   ├── art_l/
│   │   │   └── BioSemi .bdf EEG recordings for The Art of Love, linear/coherent condition
│   │   └── art_nl/
│   │       └── BioSemi .bdf EEG recordings for The Art of Love, non-linear/incoherent condition
│   └── city_movies/
│       ├── city_l/
│       │   └── BioSemi .bdf EEG recordings for City Lights, linear/coherent condition
│       └── city_nl/
│           └── BioSemi .bdf EEG recordings for City Lights, non-linear/incoherent condition
└── cuts/
    ├── all/
    │   └── CSV files containing all cinematic cut annotations for each film-condition combination
    └── matched/
        └── CSV files containing matched cut annotations for each film-condition combination

EEG files are named using the participant identifier and condition label, for example &quot;01_city_l.bdf&quot; or &quot;16_art_l.bdf&quot;.</dc:description>
    <dc:subject>Electrocardiography</dc:subject>
    <dc:subject>Film (Media)</dc:subject>
    <dc:contributor id="https://orcid.org/0000-0001-5190-4294">Volosin, Márta</dc:contributor>
    <dc:contributor id="https://orcid.org/0000-0003-3288-6005">Zipernovszky, Orsolya Papp</dc:contributor>
    <dc:contributor id="https://orcid.org/0000-0003-3004-2669">Kovács, András Bálint</dc:contributor>
    <dc:contributor id="https://orcid.org/0009-0004-9403-7652">Vedres, Péter</dc:contributor>
    <dc:type>Dataset</dc:type>
    <dc:identifier>doi:10.17632/3kd62t4dt3.1</dc:identifier>
    <dc:identifier>oai:data.mendeley.com/3kd62t4dt3.1</dc:identifier>
    <dc:rights>Creative Commons Attribution 4.0 International</dc:rights>
    <dc:rights>http://creativecommons.org/licenses/by/4.0</dc:rights>
    <dc:relation>https://data.mendeley.com/datasets/3kd62t4dt3</dc:relation>
    <dc:date>2026-06-30T04:11:48Z</dc:date>
</oai_dc:dc></metadata>
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