Li‐Wei Ko

Zehong Cao, Chin-Teng Lin, Kuan-Lin Lai et al.

Inherent fuzzy entropy is an objective measurement of electroencephalography (EEG) complexity, reflecting the robustness of brain systems. In this study, we present a novel application of multi-scale relative inherent fuzzy entropy using repetitive steady-state visual evoked potentials (SSVEPs) to investigate EEG complexity change between two migraine phases, i.e. inter-ictal (baseline) and pre-ictal (before migraine attacks) phases. We used a wearable headband EEG device with O1, Oz, O2 and Fpz electrodes to collect EEG signals from 80 participants (40 migraine patients and 40 healthy controls [HCs]) under the following two conditions: during resting state and SSVEPs with five 15-Hz photic stimuli. We found a significant enhancement in occipital EEG entropy with increasing stimulus times in both HCs and patients in the inter-ictal phase but a reverse trend in patients in the pre-ictal phase. In the 1st SSVEP , occipital EEG entropy of the HCs was significantly higher than that of patents in the pre-ictal phase (FDR-adjusted p < 0.05). Regarding the transitional variance of EEG entropy between the 1st and 5th SSVEPs, patients in the pre-ictal phase exhibited significantly lower values than patients in the inter-ictal phase (FDR-adjusted p < 0.05). Furthermore, in the classification model, the AdaBoost ensemble learning showed an accuracy of 81% and AUC of 0.87 for classifying inter-ictal and pre-ictal phases. In contrast, there were no differences in EEG entropy among groups or sessions by using other competing entropy models, including approximate entropy, sample entropy and fuzzy entropy on the same dataset. In conclusion, inherent fuzzy entropy offers novel applications in visual stimulus environments and may have the potential to provide a pre-ictal alert to migraine patients.

Avinash Kumar Singh, Hsiang-Ting Chen, Jung-Tai King et al.

As virtual reality (VR) emerges as a mainstream platform, designers have started to experiment new interaction techniques to enhance the user experience. This is a challenging task because designers not only strive to provide designs with good performance but also carefully ensure not to disrupt users' immersive experience. There is a dire need for a new evaluation tool that extends beyond traditional quantitative measurements to assist designers in the design process. We propose an EEG-based experiment framework that evaluates interaction techniques in VR by measuring intentionally elicited cognitive conflict. Through the analysis of the feedback-related negativity (FRN) as well as other quantitative measurements, this framework allows designers to evaluate the effect of the variables of interest. We studied the framework by applying it to the fundamental task of 3D object selection using direct 3D input, i.e. tracked hand in VR. The cognitive conflict is intentionally elicited by manipulating the selection radius of the target object. Our first behavior experiment validated the framework in line with the findings of conflict-induced behavior adjustments like those reported in other classical psychology experiment paradigms. Our second EEG-based experiment examines the effect of the appearance of virtual hands. We found that the amplitude of FRN correlates with the level of realism of the virtual hands, which concurs with the Uncanny Valley theory.