Name
Abstract | ||
|---|---|---|
In previous EEG preprocessing algorithms of brain-computer interface, there are problems such as huge amounts of processing EEG data and the ignorance of EEG's variance from person to person. This paper has proposed a novel method of preprocessing algorithm based on integration of multi-domain, using Fisher distance to select the sampling electrodes and spatial preprocessing is added to the traditional algorithm which is just based on time-frequency domain. It is proved to be effective and practical in overcoming the above drawbacks with experiments of EEG collection. The experiment results show that the proposed method can reduce more than 96.9% of the total processing EEG data and decrease 95.8% of the BCI system's total running time while remain almost equal classification accuracy, contributes to the online application. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/FSKD.2012.6233913 | FSKD |
Keywords | Field | DocType |
image movement processing,bci,biomedical electrodes,fisher distance,integration of multi-domain introduction,electroencephalography,brain-computer interfaces,data analysis,data processing,eeg preprocessing algorithm,preprocess,image sampling,sampling electrode,eeg collection,spatial preprocessing,brain-computer interface,medical image processing,multidomain integration,time-frequency domain,time-frequency analysis,brain computer interface,classification algorithms,wavelet transforms,brain computer interfaces,time frequency analysis,time frequency,electrodes,feature extraction | Data processing,Pattern recognition,Computer science,Brain–computer interface,Feature extraction,Preprocessor,Artificial intelligence,Time–frequency analysis,Statistical classification,Machine learning,Electroencephalography,Wavelet transform | Conference |
Volume | Issue | ISBN |
null | null | 978-1-4673-0025-4 |
Citations | PageRank | References |
0 | 0.34 | 1 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yu Ji | 1 | 118 | 12.16 |
| Jizhong Shen | 2 | 58 | 12.28 |
| Pan Wang | 3 | 37 | 13.87 |
| Jin-He Shi | 4 | 2 | 0.71 |