Online home appliance control using EEG-Based brain-computer interfaces

Brain???computer interfaces (BCIs) allow patients with paralysis to control external devices by mental commands. Recent advances in home automation and the Internet of things may extend the horizon of BCI applications into daily living environments at home. In this study, we developed an online BCI based on scalp electroencephalography (EEG) to control home appliances. The BCI users controlled TV channels, a digital door-lock system, and an electric light system in an unshielded environment. The BCI was designed to harness P300 andN200 components of event-related potentials (ERPs). On average, the BCI users could control TV channels with an accuracy of 83.0% ?? 17.9%, the digital door-lock with 78.7% ?? 16.2% accuracy, and the light with 80.0% ?? 15.6% accuracy, respectively. Our study demonstrates a feasibility to control multiple home appliances using EEG-based BCIs

Strong and Reversible Adhesion of Interlocked 3D-Microarchitectures

Diverse physical interlocking devices have recently been developed based on one-dimensional (1D), high-aspect-ratio inorganic and organic nanomaterials. Although these 1D nanomaterial-based interlocking devices can provide reliable and repeatable shear adhesion, their adhesion in the normal direction is typically very weak. In addition, the high-aspect-ratio, slender structures are mechanically less durable. In this study, we demonstrate a highly flexible and robust interlocking system that exhibits strong and reversible adhesion based on physical interlocking between three-dimensional (3D) microscale architectures. The 3D microstructures have protruding tips on their cylindrical stems, which enable tight mechanical binding between the microstructures. Based on the unique 3D architectures, the interlocking adhesives exhibit remarkable adhesion strengths in both the normal and shear directions. In addition, their adhesion is highly reversible due to the robust mechanical and structural stability of the microstructures. An analytical model is proposed to explain the measured adhesion behavior, which is in good agreement with the experimental results

Exploring the impacts of implicit context association and arithmetic booster in impulsivity reduction

People have a higher preference for immediate over delayed rewards, and it is suggested that such an impulsive tendency is governed by one???s ability to simulate future rewards. Consistent with this view, recent studies have shown that enforcing individuals to focus on episodic future thoughts reduces their impulsivity. Inspired by these reports, we hypothesized that administration of a simple cognitive task linked to future thinking might effectively modulate individuals??? delay discounting. Specifically, we used one associative memory task and one working memory task that each of which was administered to intervene acquired amount of information and individuals??? ability to construct a coherent future event, respectively. To measure whether each type of cognitive task reduces individuals??? impulsivity, a classic intertemporal choice task was used to quantify individuals??? baseline and post-intervention impulsivity. Across two experiments and data from 216 healthy young adult participants, we observed that the impacts of intervention tasks were inconsistent. Still, we observed a significant task repetition effect, such that participants showed more patient choices at the second impulsivity assessment. In conclusion, there was no clear evidence supporting that our suggested intervention tasks reduce individuals??? impulsivity, while the current results call attention to the importance of taking into account task repetition effects in studying the impacts of cognitive training and intervention

Meta-heuristic approach for high-demand facility locations considering traffic congestion and greenhouse gas emission

Large facilities in urban areas, such as storage facilities, distribution centers, schools, department stores, or public service centers, typically generate high volumes of accessing traffic, causing congestion and becoming major sources of greenhouse gas (GHG) emission. In conventional facility-location models, only facility construction costs and fixed transportation costs connecting customers and facilities are included, without consideration of traffic congestion and the subsequent GHG emission costs. This study proposes methods to find high-demand facility locations with incorporation of the traffic congestion and GHG emission costs incurred by both existing roadway traffic and facility users into the total cost. Tabu search and memetic algorithms were developed and tested with a conventional genetic algorithm in a variety of networks to solve the proposed mathematical model. A case study to determine the total number and locations of community service centers under multiple scenarios in Incheon City is then presented. The results demonstrate that the proposed approach can significantly reduce both the transportation and GHG emission costs compared to the conventional facility-location model. This effort will be useful for decision makers and transportation planners in the analysis of network-wise impacts of traffic congestion and vehicle emission when deciding the locations of high demand facilities in urban areas

A Pressure-Insensitive Self-Attachable Flexible Strain Sensor with Bioinspired Adhesive and Active CNT Layers

Flexible tactile sensors are required to maintain conformal contact with target objects and to differentiate different tactile stimuli such as strain and pressure to achieve high sensing performance. However, many existing tactile sensors do not have the ability to distinguish strain from pressure. Moreover, because they lack intrinsic adhesion capability, they require additional adhesive tapes for surface attachment. Herein, we present a self-attachable, pressure-insensitive strain sensor that can firmly adhere to target objects and selectively perceive tensile strain with high sensitivity. The proposed strain sensor is mainly composed of a bioinspired micropillar adhesive layer and a selectively coated active carbon nanotube (CNT) layer. We show that the bioinspired adhesive layer enables strong self-attachment of the sensor to diverse planar and nonplanar surfaces with a maximum adhesion strength of 257 kPa, while the thin film configuration of the patterned CNT layer enables high strain sensitivity (gauge factor (GF) of 2.26) and pressure insensitivity