http://rdf.ncbi.nlm.nih.gov/pubchem/patent/CN-115005839-A
Outgoing Links
| Predicate | Object |
|---|---|
| assignee | http://rdf.ncbi.nlm.nih.gov/pubchem/patentassignee/MD5_d6a6f422b091ba12ea61d4adbf1b0e8e |
| classificationCPCInventive | http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/A61B5-369 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/A61B5-4094 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/A61B5-7267 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N3-08 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N3-049 |
| classificationIPCInventive | http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06N3-04 http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/A61B5-369 http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06N3-08 |
| filingDate | 2022-06-09-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| inventor | http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_4549245cd87b48f67703abdf3cc348fe http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_56020fde2ffa16d2816a760929663c29 http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_9dd2fed4f9d4fbca62c7d6d20117ba25 |
| publicationDate | 2022-09-06-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| publicationNumber | CN-115005839-A |
| titleOfInvention | EEG signal self-supervised representation learning method, system and storage medium |
| abstract | The invention provides an electroencephalogram signal self-supervised representation learning method, system and storage medium, which belong to the technical field of signal processing and pattern recognition. Use the encoder to obtain the local implicit representation and self-context representation of the multi-channel EEG signal data in each time period, and obtain the global representation to calculate the loss of the instantaneous time-shift prediction task; obtain the segment representation according to the self-context representation of each time period, and predict The correlation probability between different channels in different time periods is calculated, and the loss of the delay time-shift prediction task is calculated; the local latent representation of each time period is randomly replaced, and the new self-context representation is calculated according to the replaced new local latent representation. The new self-context representation predicts whether the local latent representation corresponding to each original channel is replaced by other channels, and calculates the loss of the replacement discriminant learning prediction task; through three self-supervised tasks, the self-supervised representation learning on the EEG signal data is realized, and the The learned representation is used for seizure prediction applications. |
| priorityDate | 2022-06-09-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| type | http://data.epo.org/linked-data/def/patent/Publication |
Incoming Links
| Predicate | Subject |
|---|---|
| isDiscussedBy | http://rdf.ncbi.nlm.nih.gov/pubchem/substance/SID419582621 http://rdf.ncbi.nlm.nih.gov/pubchem/compound/CID60825 |
Total number of triples: 21.