The concealed information test (CIT) is widely used for detecting deception in criminal investigations, primarily leveraging the P300 component of electroencephalogram (EEG) signals. However, the traditional bootstrapped amplitude difference (BAD) method struggles to accurately differentiate deceptive individuals from innocent ones when irrelevant stimuli carry familiarity or inherent meaning, thus limiting its practical applicability in real-world investigations. This study aimed to enhance the deception detection capability of the P300-based CIT, particularly under conditions of low stimulus heterogeneity. To closely simulate realistic investigative scenarios, we designed a realistic mock-crime setup in which participants were familiarized with all CIT stimuli except the target stimulus. EEG data acquired during CIT sessions were analyzed using the BAD method, machine learning algorithms, and deep learning (DL) methods (ShallowNet and EEGNet). Among these techniques, EEGNet demonstrated the highest deception detection accuracy at 86.67%, when employing our proposed data augmentation approach. Overall, DL methods could significantly improve the accuracy of deception detection under challenging conditions of low stimulus heterogeneity by effectively capturing subtle cognitive responses not accessible through handcrafted features. To the best of our knowledge, this is the first study that employed DL approaches for subject-independent deception classification using the CIT paradigm.

Augmented reality (AR) technology can elevate user experiences by facilitating intuitive and immersive communication. Commercial AR devices typically rely on hand-held controllers or cameras to generate control commands; however, neither of these approaches is feasible when hand usage is limited. To address this limitation, we introduce a novel stand-alone AR system“, Bionic AR.” This system integrates a head-mounted display (HMD) with biosignal-based human–computer interface (HCI) technology. By embedding passive dry electrodes into the proposed AR headset in contact areas such as the occipital area, brow ridge, and cheeks, the system can simultaneously record electroencephalography (EEG), electrooculography (EOG), and facial electromyography (fEMG) data. The acquired multimodal biosignals were utilized to implement a steady-state visual evoked potential-based brain–computer interface (BCI), an eye-writing pattern recognition system, a facial expression recognition system, and a silent speech recognition system. In real-time experiments, each HCI module achieved accuracies exceeding 98%, underscoring the feasibility and practical potential of the proposed Bionic AR system.

A driver's emotional state can affect driving performance. According to the studies on the driving performance based on the circumplex (arousal-valence) model of affect, negative emotions such as anger and sadness can severely hinder safe driving. In this study, we developed a system to modulate drivers’ emotions to designated emotional states by manipulating in-vehicle environments, such as ambient lighting, background music, scent, ventilation, and rear curtains. The proposed system, named the “mood-modulator” system, consists of four different modes, designed to induce different emotional states. The feasibility of the “mood-modulator” system was evaluated using electroencephalogram (EEG) and photoplethysmogram (PPG) signals recorded from 48 drivers in an actual car environment. In the experiments, negative emotions were induced for each participant using short movie clips. Then, one of the four modes (different in-vehicle environments) was executed, during which both EEG and PPG data were acquired. We quantitatively evaluated whether each mode could effectively induce targeted emotional valence using machine learning classifier models, individually constructed from EEG data recorded during calibration sessions. The modulation of emotional arousal by each mode was also assessed using heart rate and respiration rate extracted from the PPG data. Our results demonstrated that the four modes could effectively increase the participant's emotional valence and modulate emotional arousal state to the intended direction. To the best of our knowledge, this is the first study to quantitatively evaluate a system that modulates a driver's emotional state using biosignals recorded in an actual car.

One of the most important issues in current brain-computer interface (BCI) research is the prediction of a user's BCI performance prior to the main BCI session because it would be useful to reduce the time required to determine the BCI paradigm best suited to that user. In electroencephalography (EEG)-BCI research, whether a user has low BCI performance toward a specific BCI paradigm has been estimated using a variety of resting-state EEG features. However, no previous study has attempted to predict the performance of near-infrared spectroscopy (NIRS)-BCI using resting-state NIRS data recorded before the main BCI experiment. In this study, we investigated whether the performance of an NIRS-BCI discriminating a mental arithmetic task from the baseline state could be predicted using resting-state functional connectivity (RSFC) of the prefrontal cortex. The investigation of NIRS signals recorded from 29 participants revealed that the RSFC between bilateral channels in the prefrontal area was negatively correlated with subsequent BCI performance (e.g. a fitted line for the RSFC between L2 and R2 channels explains 41% of BCI performance variation). We expect that our indicator can be used to predict BCI performance of an individual user prior to the main NIRS-BCI experiments, thereby facilitating implementation of more efficient NIRS-BCI systems.

Brain–computer interface (BCI) technology has been studied with the fundamental goal of helping disabled people communicate with the outside world using brain signals. In particular, a large body of research has been reported in the electroencephalography (EEG)-based BCI research field during recent years. To provide a thorough summary of recent research trends in EEG-based BCIs, the present study reviewed BCI research articles published from 2007 to 2011 and investigated (a) the number of published BCI articles, (b) BCI paradigms, (c) aims of the articles, (d) target applications, (e) feature types, (f) classification algorithms, (g) BCI system types, and (h) nationalities of the author. The detailed survey results are presented and discussed one by one. [Supplemental materials are available for this article. Go to the publisher's online edition of International Journal of Human-Computer Interaction to view the free supplemental file: Supplementary Tables.pdf.]