RT-based Concealed Information Test (CIT) has been suggested to detect lying with high accuracy. However, because previous research have been conducted with a minimum of five probes, limited evidence is available to determine whether or not the RT-based CIT is also useful in precisely detecting lies with less than five probes. In this study, the accuracy of an RT-based Concealed Information Test (CIT) was examined by varying the numbers of probes used for the test. Results suggested that the RT-based CIT produces accurate lie detecting outcomes as the number of probes increases from a single probe to three probes. When five probes were used, however, the accuracy level did not improve from the level achieved with three probes. Interestingly, the accuracy decreased when the stimuli for the RT-based CIT were constructed with numeric elements of an event such as the amount of money. Further discussed are possible explanations in regards to the differences observed in the RT-based CIT accuracy rates dependent on the numbers of probes.Based on the Experiment 1, in the second experiment we measured the accuracy of the RT-based CIT was measured using two probes.

The most crucial feature of human computer interaction is computers and computer-based applications to infer the emotional states of humans or others human agents based on covert and/or overt signals of those emotional states. In emotion recognition, bio-signals reflect sequences of neural activity induced by emotional events and also, have many technical advantages. The aim of this study is to classify six emotions (joy, sadness, anger, fear, surprise, and neutral) that human have often experienced in real life from multi-channel bio-signals using machine learning algorithms. We have measured physiological responses of three-hundred participants for acquisition of bio-signals such as electrodermal activity, electrocardiograph, skin temperature, and photoplethysmograph during six emotions induction. Also, for emotion classification, we have extracted eighteen features from the signals and performed emotion classification using five algorithms, linear discriminant analysis, Naïve Bayes, classification and regression tree, selforganization map and support vector machine. The used algorithms were evaluated by only training, 10-fold crossvalidation and repeated random sub-sampling validation. We have obtained recognition accuracy from 42.4 to 100% for only training and 39.2 to 53.9% for testing. Also, the result for testing showed that an accuracy of emotion recognition by Naïve Bayes and linear discriminant analysis were highest (53.9%, 52.7%) and was lowest by support vector machine (39.2%). This means that Naïve Bayes is the best emotion recognition algorithm for basic emotions. To apply to real system, we have to discuss in the view point of testing and this means that it needs to apply various methodologies for the accuracy improvement of emotion recognition in the future analysis.

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In most implementation of an ERP-based speller, standard row-column paradigm (RCP) was used. However, RCP is susceptible to adjacency-distraction errors because items in the same row or column of the target flash at the time of a half when the target item flashes. The adjacency-distraction errors could be reduced if the number of flanking items that flash with the target is diminished. This study presents a novel P300-based stimulus presentation called row-column-diagonal paradigm (RCDP) where characters on the main diagonal and the anti-diagonal in the matrix flash in addition to characters on the row and columns. In RCDP, items in the same row, column, main diagonal, and anti-diagonal of the target flashes at the time of a quarter when the target item flashes. Using a 6×6 matrix of alphanumeric characters and keyboard commands, ten college students used RCP and RCDP. Stepwise linear discriminant analysis (SWLDA) for the EEG signals recorded in calibration phases was used to calculate discrimininant function. By applying the discrimininant function to electroencephalography (EEG) signal recorded in the test phase, the probability whether the item was the target or not was evaluated. Average accuracy was 76.6% in RCP while 84.0% in RCDP. With RCP, most errors were occurred in the same row or column of the target; on the other hand, with RCDP in the same row, column, main diagonal, or anti-diagonal of the target. These findings indicate how RCDP reduces adjacency-distraction errors and might be able to contribute to develop more advanced stimulus presentation paradigm.