Alertness staging based on improved self-organizing map

Abstract

In order to classify the alertness status, 19 channels of electroencephalogram (EEG) signals from 5 subjects were acquired during daytime nap. Ten different types of features (including time domain features, frequency domain features and nonlinear features) were extracted from EEG signals, and an improved self-organizing map (ISOM) neuron network was proposed, which successfully identify three different brain status of the subjects: awareness, drowsiness and sleep. Compared with traditional SOM, the experiment results show that the ISOM generates much better classification accuracy, reaching as high as 89.59%.

Keywords

electroencephalogram (EEG) / improved self-organizing map (ISOM) / alertness staging

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