<span>Extreme fears towards ghosts and entities are defined as phasmaphobia. Those diagnosed with phasmophobia symptoms should control their own fears to avoid phasmaphobia attack. In this work, we present the development of phasmophobia detection electroencephalogram database (PDED).</span><span>PDED</span><span>consists</span><span>of</span><span>an</span><span>average</span><span>of</span><span>45</span><span>minutes electroencephalography (EEG) recordings from eight electrodes situated on the frontal lobe of the brain area. A real-time fear assessment was conducted simultaneously with the EEG recording by the participant. Five different stimuli were used to induce fear in our experiment. 599 EEG epochs related to fear were extracted based on the timestamp recorded by each individual. Asymmetry relation ratio (ARR) techniques were used on these EEG to detect the presence of fear. The quality of long duration of EEG recording from PDED in recognizing fear was thoroughly presented based on ARR. In this study, 91.5% of fear emotion managed to be detected from these epochs. Using PDED, it is also proven that the changes of ARR reflected positive correlation towards the changes of the level of fear. Analysis using emotion recognition rate (ERR) curves indicated that, two electrodes, namely F7 and F8, were sufficient to recognized 88% of fear from the recordings.</span>

Biometric authentication is a process of identity verification once an identity is claimed by an individual. It uses unique features on the human body. Footprints are a new biometric feature that has sparked interest among researchers, as this feature is universal, easy to extract and has not changed throughout time. The focus of researchers in this field is to improve the recognition rate. Various techniques have been developed for this purpose, but the accuracy percentage is at 98% with an equal error rate (EER) of 6.1%. This paper proposes the use of a new technique called SqueezeNet in classifying footprint images. SqueezeNet belongs to the convolutional neural network (CNN) family. In this study, 300 footprint images were used from 15 individuals. The 70% of these images were used to train the proposed SqueezeNet network, while the rest were used for testing. At the end of this simulation, SqueezeNet has achieved an accuracy of 98.67% with an EER of 2.1%.