In recent years, deep learning methods have shown promising capabilities for extracting informative and discriminative features from electroencephalography (EEG) data. However, several studies have reported that the feature selection process followed by feature extraction can be beneficial to achieve further performance improvement. Even though a recent work achieved promising results by using the single-agent reinforcement learning (RL)-based framework to select task-relevant features in the temporal domain, it still failed to consider other significant features in the spatial–spectral domain. To overcome such limitations, we propose a cooperative multiagent RL-based framework (MARS) that performs feature selection in both the spatial–spectral and temporal domains simultaneously for a motor imagery (MI)-EEG classification task. In this framework, we enable our RL agents to collaborate with each other as a team to solve a complex multiobjective feature selection problem. Furthermore, we adopt a counterfactual advantage function to overcome the free-rider problem, which is associated with the credit assignment issue in multiagent cases. To assess the MARS framework, we conduct extensive experiments with two public MI datasets under subject-dependent and subject-independent scenarios and we apply the MARS to different backbone networks. The experimental results demonstrate that our MARS outperforms other competing methods in terms of mean accuracy and achieves statistically significant improvements.

Currently, the expectations for autonomous vehicles (AVs) are increasing. However, it is expected to take at least a decade to develop a fully AV, where human intervention is completely unrequired. By then, human driving is required if necessary. Currently, when the AV hands over control to the driver, a safe driving environment can be created only if it is possible to determine whether the driver is in an abnormal state. Unfortunately, according to the sensory conflict theory, the risk of motion sickness (MS) is higher in AV than in ordinary vehicles. This is because neither passengers nor drivers can predict the movement path of the vehicle under AV, so there is more dissonance between vision and perception. Because the technology to remove MS when it occurs has not yet been developed, the best way to maintain the driver’s good condition is to quickly predict MS through the driver’s bio-signals and establish a system to prevent MS through advanced driver assistance systems. It is necessary to quickly predict early MS and provide feedback before it becomes severe. In this study, we collected dry electroencephalogram (EEG) data to predict MS in a real-world driving environment. For MS-based feature extraction, a normalized sample covariance matrix-based feature representation method was used, and they were classified using convolutional neural networks. As a result, we achieved 89.05% (±5.76) accuracy when averaging all four experimental sessions we conducted. We expect our proposed model to be a useful indicator for resolving MS issues in AV environments.

Most artificial intelligence (AI) based learning systems act in virtual or laboratory environments. Here we demonstrate an AI-based curling robot system named `Curly' that competes on a real-world curling ice sheet. Curly encompasses (1) an AI-based curling strategy and simulation engine under consideration of the high `icy' uncertainty, (2) the thrower robot enabled by autonomous driving with traction control, and (3) the skip robot that allows to recognize the curling field and stone configuration based on vision technology. The Curly performed well both: in classical game situations and when interacting with human opponents, namely, the top-ranked Korean amateur high school curling team.

Recently, steady-state visual evoked potential (SSVEP)-based brain-machine interface (BMI) speller systems have shown a great performance increase with high information transfer rate (ITR) and short response time. In previous BMI speller systems, however, users should utilize the systems at fixed viewing distance environment for evoking SSVEP signals because a variation of the SSVEP signals according to changes of viewing distance was not considered during system design process. For a real-world application of BMI speller, reliable speller systems which are robust to various viewing distance environment are needed. In this study, hence, we investigate the effects of viewing distance on SSVEP by changing distance between a user and visual stimuli. Here, we used four visual stimuli which have different frequencies using LED monitor. In the subsequent analysis, we present classification results with several methods. Our analysis and results show a possibility that SSVEP under various viewing distance environment could be facilitated.

We developed and studied a Steady-State Visual Evoked Potential (SSVEP) based BCI system using a high frequency visual stimuli (>25Hz) design for reducing visual fatigue. Existing SSVEP based BCI designs primarily use low frequency visual stimuli (<;20Hz) for eliciting relatively higher SSVEP signal, while the low frequency stimuli can provocate photosensitivity epileptic seizure. On the other hand, high frequency stimuli are visually more comfortable and cause less visual fatigue and seizure. To detect the weak high frequency SSVEP signal, we used multi-channel EEG and introduced canonical correlation analysis to identify the elicited SSVEP frequency. We designed and built a 30-character SSVEP BCI speller system without calibration and evaluated the performance metrics including classification accuracy and subjective fatigue ratings, in both high-frequency and low frequency SSVEP modes. The result indicates that the high frequency stimuli system archieved higher classification accuracy (averaged 80% in the 30-class classification) comparable to that by the low frequency system. Moreover, no subjects rated the visual feeling as unacceptable or uncomfortable with the high frequency system.