<p class="0abstract">This study investigates the capability of electroencephalogram (EEG) signals to be used for biometric identification. In the context of biometric, recently, researchers have been focusing more on biomedical signals to substitute the biometric modalities that are being used nowadays as the signals obtained from our bodies is considered more secure and privacy-compliant. The EEG signals of 6 subjects were collected where the subjects were required to undergo two baseline experiments which are, eyes open (EO) and eyes closed (EC). The signals were processed using a 2nd order Butterworth filter to eliminate the unwanted noise in the signals. Then, Daubechies (db8) wavelet was applied to the signals in the feature extraction stage and from there, Power Spectral Density (PSD) of alpha and beta waves was computed. Finally, the correlation model and Multilayer Perceptron Neural Network (MLPNN) was applied to classify the EEG signals of each subject.  Correlation model has yielded great significant difference of coefficient between autocorrelation and cross-correlation where it gives the coefficient value of 1 for autocorrelation and the coefficient value of less than 0.35 for cross-correlation. On the other hand, the MLPNN model gives an accuracy of 75.8% and 71.5% for classification during EO and EC baseline condition respectively. Therefore, these results support the usability of EEG signals in biometric recognition.</p>

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The problem of getting lost anywhere by pedestrians that uses mobile device will be reduce when 3D representation (Map) of an environment is provided in his/her mobile devices. Over the years, there are growing number of pedestrian navigation aided application for mobile devices, Unfortunately certain issue concerning the use of mobile devices as a navigation aid, in regards to the pedestrian behavior while-on-the-go in a different environment under different condition were still not properly address. Moreover, we don't know whether it safe or not interacting with mobile device while walking around and some other related support. This paper consider pedestrian behavior under different conditions in different environment while on the go and proposed the design of an application in Android platform with 3D representation meant for mobile device as a pedestrian navigation aid and investigates user's satisfactions with the applications interface design quality. The result shows that pedestrians consider interacting with the application as safe while using it in a calm condition in an unknown location and also its best usage is for mostly when it provides an exploration of environment rather route only.

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