Name
Abstract | ||
|---|---|---|
The quality of the non-invasive EEG signals was always affected by the changes in the contact impedances and the artifacts from eye blinking, eye movements and body movements. An effective quality assessment method is needed to assess the qualities of the EEG signals. This paper proposed a novel method to assess the signal quality of EEG signals based on block-based measurements of the fluctuations of the second-order power amplitudes of the EEG signals. The initial signal quality scores were generated by fusion of the mean power amplitudes and the signal fluctuations of the motor imagery state with reference to background idling state. These scores were subsequently mapped to different quality levels by using fuzzy-c means clustering. Experimental results were conducted on the basis of 3 data sets of 15 healthy subjects performing motor imagery of hand movements and idle, for both gel-based and gel-less electrodes. The results obtained demonstrated that the proposed method was capable of evaluating the quality of the EEG signals, as supported by the clear separation of the assigned quality levels between gel-based and gel-less electrodes. This further validated the assumption that generally the quality of the EEG signals acquired based on the gel-based electrodes was better than that of the gel-less electrodes. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/SMC.2014.6974190 | SMC |
Keywords | Field | DocType |
eye,gels,biomechanics,bioelectric potentials,medical signal detection,eye movements,statistics,neurophysiology,biomedical electrodes,statistical analysis,electroencephalography,gel-based electrodes,medical signal processing,fuzzy-c means clustering,second-order power amplitudes,effective quality assessment method,motor imagery state,body movements,contact impedances,noninvasive eeg signal quality,hand movements,eye blinking,gel-less electrodes,noninvasive eeg signal acquisition,block-based measurements | Computer vision,Pattern recognition,Control theory,Signal quality,Computer science,Eye movement,Eye blinking,Artificial intelligence,Electroencephalography,Motor imagery | Conference |
ISSN | Citations | PageRank |
1062-922X | 0 | 0.34 |
References | Authors | |
6 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Huijuan Yang | 1 | 23 | 4.93 |
| Cuntai Guan | 2 | 1298 | 124.69 |
| Kai Keng Ang | 3 | 804 | 64.19 |
| Kok Soon Phua | 4 | 14 | 4.94 |
| Chuanchu Wang | 5 | 93 | 17.16 |