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. 2016 Apr 23;16(4):590.
doi: 10.3390/s16040590.

Unsupervised Event Characterization and Detection in Multichannel Signals: An EEG application

Angel Mur  1 , Raquel Dormido  2 , Jesús Vega  3 , Natividad Duro  4 , Sebastian Dormido-Canto  5
Affiliations

Unsupervised Event Characterization and Detection in Multichannel Signals: An EEG application

Angel Mur et al. Sensors (Basel). .

Abstract

In this paper, we propose a new unsupervised method to automatically characterize and detect events in multichannel signals. This method is used to identify artifacts in electroencephalogram (EEG) recordings of brain activity. The proposed algorithm has been evaluated and compared with a supervised method. To this end an example of the performance of the algorithm to detect artifacts is shown. The results show that although both methods obtain similar classification, the proposed method allows detecting events without training data and can also be applied in signals whose events are unknown a priori. Furthermore, the proposed method provides an optimal window whereby an optimal detection and characterization of events is found. The detection of events can be applied in real-time.

Keywords: EEG; artifacts; event characterization; event detection; unsupervised classification.

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Figures

Figure 1
Figure 1
Different windows along the MC signal X.
Figure 2
Figure 2
The channel CH1(t) of the 64-channel EEG.
Figure 3
Figure 3
Groups of intervals found using the unsupervised classification (UMED) and a PCA. The LwH has 155 samples. The first two principal components contain 55% of the full information. The different clusters are characterized but not identified.
Figure 4
Figure 4
Groups of intervals found using the unsupervised classification (UMED) and a PCA. The Lw has 128 samples. The first two principal components contain 53% of the full information. The different clusters are characterized but not identified.
Figure 5
Figure 5
Groups of intervals found using the supervised classification (SM) [8] and the first two principal components of the Figure 4. The Lw has 128 samples. The first two principal components contain 53% of the full information. The different clusters are identified. Using the Table 1: NN intervals are in G1+2, the EB in G3, the EUM in G4, the ELM in G5, the JM in G6, and the JC in G7.
Figure 6
Figure 6
Events detected between the samples 46,389 and 46,653 using a LwH = 155, Lw = 128 and Lw = 165. The signal is a portion of an EEG channel.
See this image and copyright information in PMC

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