Alertness Staging Based on Improved Self-Organizing Map

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%.