Zobrazeno 1 - 8
of 8
pro vyhledávání: '"Farid Oveisi"'
Autor:
Farid Oveisi, Abbas Erfanian
Publikováno v:
EURASIP Journal on Advances in Signal Processing, Vol 2008 (2008)
This paper presents a novel approach for efficient feature extraction using mutual information (MI). In terms of mutual information, the optimal feature extraction is creating a feature set from the data which jointly have the largest dependency on t
Externí odkaz:
https://doaj.org/article/a1c38aaf3be5437895b7f970bb329573
Publikováno v:
Independent Component Analysis for Audio and Biosignal Applications
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8029a2046eeee153b39aacea8d5bbf80
http://www.intechopen.com/articles/show/title/nonlinear-independent-component-analysis-for-eeg-based-brain-computer-interface-systems
http://www.intechopen.com/articles/show/title/nonlinear-independent-component-analysis-for-eeg-based-brain-computer-interface-systems
Publikováno v:
Recent Advances in Brain-Computer Interface Systems
Dimensionality reduction of the raw input variable space is an essential preprocessing step in the classification process. In general, it is desirable to keep the dimensionality of the input features as small as possible to reduce the computational c
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::87c2a245a16cf4a131d99ecbeef38dd3
https://doi.org/10.5772/13935
https://doi.org/10.5772/13935
Autor:
Farid Oveisi
Publikováno v:
ICASSP
One of the preprocessors can be used to improve the performance of brain-computer interface (BCI) systems is independent component analysis (ICA). ICA is a signal processing technique in which observed random data are transformed into components that
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::5563d7462db12b6f9fa945b37c99d732
https://doi.org/10.1109/icassp.2009.4959595
https://doi.org/10.1109/icassp.2009.4959595
Autor:
Farid Oveisi
Publikováno v:
2009 4th International IEEE/EMBS Conference on Neural Engineering.
A typical goal in signal processing is to find a representation in which certain attributes of the signal are made explicit. The most important variables for identifying signal certain attributes are time and features extracted from the signal. In th
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::82deeb18b2632ba5822b40e45e7344cd
https://doi.org/10.1109/ner.2009.5109292
https://doi.org/10.1109/ner.2009.5109292
Autor:
Farid Oveisi
Publikováno v:
2009 4th International IEEE/EMBS Conference on Neural Engineering.
Always, one of the issues in the brain-computer interface (BCI) is to extract components from raw EEG data that have more information in order to separate task-related potentials from other neural and artifactual EEG sources. In this paper, a new met
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::3bc5fbeff07ff15594c911d8c56b5899
https://doi.org/10.1109/ner.2009.5109297
https://doi.org/10.1109/ner.2009.5109297
Autor:
Abbas Erfanian, Farid Oveisi
Publikováno v:
EURASIP Journal on Advances in Signal Processing, Vol 2008 (2008)
This paper presents a novel approach for efficient feature extraction using mutual information (MI). In terms of mutual information, the optimal feature extraction is creating a feature set from the data which jointly have the largest dependency on t
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::514d3100d110dd69b700e1a11f2f97bc
https://doaj.org/article/a1c38aaf3be5437895b7f970bb329573
https://doaj.org/article/a1c38aaf3be5437895b7f970bb329573
Autor:
Farid Oveisi, Abbas Erfanian
Publikováno v:
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference. 2007
This paper presents a novel algorithm for efficient feature extraction using mutual information (MI). In terms of mutual information, the optimal feature extraction is creating a new feature set from the data which jointly have largest dependency on
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::51f1cc3440b0c21d08a6e6a3ff17c9ee
https://pubmed.ncbi.nlm.nih.gov/18003147
https://pubmed.ncbi.nlm.nih.gov/18003147