{
  "annotation_id": "80777beb-7554-4c86-851d-02ac5cf1564c",
  "date_created": "2026-03-02T18:00:28.689000Z",
  "date_modified": "2026-03-02T18:00:28.689000Z",
  "file_hash": "19c45e14332406cf890adab6838e6faa68242ece7bfa97f319a45c1e68aa03e2",
  "private": false,
  "record": {
    "abstract": "We present a new wavelet based method for the denoising of {\\it event related\npotentials} ERPs), employing techniques recently developed for the paradigm of\ndeterministic chaotic systems. The denoising scheme has been constructed to be\nappropriate for short and transient time sequences using circular state space\nembedding. Its effectiveness was successfully tested on simulated signals as\nwell as on ERPs recorded from within a human brain. The method enables the\nstudy of individual ERPs against strong ongoing brain electrical activity.",
    "arxiv_id": "physics/0001069",
    "authors": [
      "A. Effern",
      "K. Lehnertz",
      "T. Schreiber",
      "T. Grunwald",
      "P. David",
      "C. E. Elger"
    ],
    "categories": [
      "physics.data-an",
      "physics.med-ph",
      "q-bio"
    ],
    "doi": "10.1016/S1386-9477(00)00111-9",
    "title": "Nonlinear denoising of transient signals with application to event   related potentials",
    "url": "https://arxiv.org/abs/physics/0001069"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "518828c2-957d-4147-bfa1-57c550e88f9e",
    "id": "arXiv Dataset IDs",
    "type": "Model",
    "variant": "snapshot-2026-03-01",
    "version": "0.1.0"
  },
  "user_id": 1000002
}