| 摘要 |
Abstract Human identification becomes huge demand for various applications in particular for the security related areas, such as identification for a network security. Electroencephalogram (EEG) signals are confidential and hard to imitate, since EEG signals are a reflection of individual-dependent inner mental tasks. Generally speaking, EEG signal has several advantages, such as (i) it is confidential as it corresponds to a mental task, (ii) it is very difficult to mimic and (iii) it is almost impossible to steal as the brain activity is sensitive to the stress and the mood of the person. Hence, an aggressor cannot force the person to reproduce his/her mental pass-phrase. In this patent we proposed a novel algorithm to create a spatial pattern of EEG signals obtained from the open public database such that the proposed algorithm can be used to identify people by EEG signals. In our EEG signal processing, we have analyzed 64-electrode EEG samples for two databases, one is for 45 people and calculate the equivalent root mean square (rms) values for each electrode signal over 1 second period, by which created a 64-value input for each subject. With this neural network (NN) model, our analysis clearly showed that our designed classifier is able to identify all the 45 people correctly (successful rate of 100%) with a mean square error of 2.0334x 10-7 and the same algorithm applying to the 2 "d database with 116 out of 122 people can be fully identified (successful rate of 95.1%) with a mean square error value of 0.00186. We deeply believe that a low complexity, high resolution, effective and efficient is very attractive for the real life applications especially for network security in the foreseeable future. Keywords: biometric nature, security system, neural network, EEG, signal processing Figure 1: 64 vectors transformed from corresponding 64 electrodes Final Mean Square Error =0.0027151 - Train Test Best C> 102 10 - L LI U 200 400 600 800 1000 1200 Epochs Figure 2: Mean square errors for the second database, 122 people (subjects) during the training |
