Using electroencephalograph signals for task classification and activity recognition
US7580742B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Feb 7, 2006 |
| Grant date | Aug 25, 2009 |
| Priority date | — |
| Expiry date | Jun 17, 2027 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG16H50/70
- WIPO fieldMedical technology
- WIPO sectorInstruments
Abstract
A method for classifying brain states in electroencephalograph (EEG) signals comprising building a classifier model and classifying brain states using the classifier model is described. Brain states are determined. Labeled EEG data is collected and divided into overlapping time windows. The time dimension is removed from each time window. Features are generated by computing the base features; combining the base features to form a larger feature set; pruning the large feature set; and further pruning the feature set for a particular machine learning technique. Brain states in unlabeled EEG data are classified using the classifier model by dividing the unlabeled EEG data into overlapping time windows and removing the time dimension from each time window. Features required by the classifier model are generated. Artifacts in the labeled and unlabeled EEG data comprise cognitive artifacts and non-cognitive artifacts.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.