dorsal/arxiv
View SchemaComplex Independent Component Analysis of Frequency-Domain Electroencephalographic Data
| Authors | Jorn Anemuller, Terrence J. Sejnowski, Scott Makeig |
|---|---|
| Categories | |
| ArXiv ID | q-bio/0310011 |
| URL | https://arxiv.org/abs/q-bio/0310011 |
| DOI | 10.1016/j.neunet.2003.08.003 |
| Journal | Neural Networks, 16:1311-1323, 2003 |
Abstract
Independent component analysis (ICA) has proven useful for modeling brain and electroencephalographic (EEG) data. Here, we present a new, generalized method to better capture the dynamics of brain signals than previous ICA algorithms. We regard EEG sources as eliciting spatio-temporal activity patterns, corresponding to, e.g., trajectories of activation propagating across cortex. This leads to a model of convolutive signal superposition, in contrast with the commonly used instantaneous mixing model. In the frequency-domain, convolutive mixing is equivalent to multiplicative mixing of complex signal sources within distinct spectral bands. We decompose the recorded spectral-domain signals into independent components by a complex infomax ICA algorithm. First results from a visual attention EEG experiment exhibit (1) sources of spatio-temporal dynamics in the data, (2) links to subject behavior, (3) sources with a limited spectral extent, and (4) a higher degree of independence compared to sources derived by standard ICA.
{
"annotation_id": "7c22c3fd-1f79-4e9c-b7de-26bbe1c49c1d",
"date_created": "2026-03-02T18:01:28.595000Z",
"date_modified": "2026-03-02T18:01:28.595000Z",
"file_hash": "137f57f5dc6f9066e133aa04f795eed8ca57c5b86ff0dca89395358c18b4fcb3",
"private": false,
"record": {
"abstract": "Independent component analysis (ICA) has proven useful for modeling brain and\nelectroencephalographic (EEG) data. Here, we present a new, generalized method\nto better capture the dynamics of brain signals than previous ICA algorithms.\nWe regard EEG sources as eliciting spatio-temporal activity patterns,\ncorresponding to, e.g., trajectories of activation propagating across cortex.\nThis leads to a model of convolutive signal superposition, in contrast with the\ncommonly used instantaneous mixing model. In the frequency-domain, convolutive\nmixing is equivalent to multiplicative mixing of complex signal sources within\ndistinct spectral bands. We decompose the recorded spectral-domain signals into\nindependent components by a complex infomax ICA algorithm. First results from a\nvisual attention EEG experiment exhibit (1) sources of spatio-temporal dynamics\nin the data, (2) links to subject behavior, (3) sources with a limited spectral\nextent, and (4) a higher degree of independence compared to sources derived by\nstandard ICA.",
"arxiv_id": "q-bio/0310011",
"authors": [
"Jorn Anemuller",
"Terrence J. Sejnowski",
"Scott Makeig"
],
"categories": [
"q-bio.QM",
"cs.CE",
"physics.data-an",
"q-bio.NC"
],
"doi": "10.1016/j.neunet.2003.08.003",
"journal_ref": "Neural Networks, 16:1311-1323, 2003",
"title": "Complex Independent Component Analysis of Frequency-Domain Electroencephalographic Data",
"url": "https://arxiv.org/abs/q-bio/0310011"
},
"schema_id": "dorsal/arxiv",
"source": {
"execution_id": "5be5bf0b-f93a-4a50-b929-70b4d1240f1e",
"id": "arXiv Dataset IDs",
"type": "Model",
"variant": "snapshot-2026-03-01",
"version": "0.1.0"
},
"user_id": 1000002
}