{"paper":{"title":"Beyond Flickering: Introducing Code-Modulated Motion Visual Evoked Potentials for Brain-Computer Interfacing","license":"http://creativecommons.org/licenses/by/4.0/","headline":"Code-modulated motion visual evoked potentials enable functional brain-computer interfacing by replacing flicker with pseudo-random object motion.","cross_cats":[],"primary_cat":"q-bio.NC","authors_text":"Hanneke Scheppink, Ivan Volosyak, Jordy Thielen, Rainer Herpers","submitted_at":"2026-05-15T09:56:16Z","abstract_excerpt":"A code-modulated motion visual evoked potential (c-MVEP) for brain-computer interfacing (BCI) is presented in this study. This paradigm uses pseudo-random sequences to visually stimulate objects using motion as an alternative to flickering. In an offline experiment of this study, EEG data were recorded and compared during sequential stimulation of a single object under four conditions: c-MVEP, code-modulated visual evoked potential (c-VEP), steady-state motion visual evoked potential (SSMVEP), and steady-state visual evoked potential (SSVEP). c-MVEP showed similar time-domain characteristics a"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"The c-MVEP BCI reached a mean accuracy of 85.67% with an average selection time of 2.61s, which was significantly lower than c-VEP (97.81%; 1.15s) and SSVEP (93.42%; 1.94s), but significantly higher than SSMVEP (64.91%; 4.18s).","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"That the pseudo-random motion sequences produce sufficiently distinct and stable EEG responses across users to support reliable online classification without extensive per-user calibration or post-hoc data selection.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"Introduces c-MVEP paradigm using motion stimulation, achieving 85.67% accuracy in online 4-class BCI with comparable SNR to c-VEP but different spatial distribution.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"Code-modulated motion visual evoked potentials enable functional brain-computer interfacing by replacing flicker with pseudo-random object motion.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"562eb6b8435ec014c3b1cebba263ee622443785562de757d8377b74559cae46e"},"source":{"id":"2605.15801","kind":"arxiv","version":1},"verdict":{"id":"4f398597-ad14-4201-be54-db1fcd7890c7","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-19T19:16:11.792249Z","strongest_claim":"The c-MVEP BCI reached a mean accuracy of 85.67% with an average selection time of 2.61s, which was significantly lower than c-VEP (97.81%; 1.15s) and SSVEP (93.42%; 1.94s), but significantly higher than SSMVEP (64.91%; 4.18s).","one_line_summary":"Introduces c-MVEP paradigm using motion stimulation, achieving 85.67% accuracy in online 4-class BCI with comparable SNR to c-VEP but different spatial distribution.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"That the pseudo-random motion sequences produce sufficiently distinct and stable EEG responses across users to support reliable online classification without extensive per-user calibration or post-hoc data selection.","pith_extraction_headline":"Code-modulated motion visual evoked potentials enable functional brain-computer interfacing by replacing flicker with pseudo-random object motion."},"integrity":{"clean":false,"summary":{"advisory":1,"critical":0,"by_detector":{"doi_compliance":{"total":1,"advisory":1,"critical":0,"informational":0}},"informational":0},"endpoint":"/pith/2605.15801/integrity.json","findings":[{"note":"DOI in the printed bibliography is fragmented by whitespace or line breaks. 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