Visually impaired people and grocery shopping in store: First evidence from brain oscillations electroencephalogram

Grocery shopping represents a challenging task for visually impaired (VI), but the neuroscientific literature on the consumption patterns of this group is still scarce. The aim of the study was to analyse the relationship between explicit consumer experience and neuropsychological measures. A group of VI and sighted explored and manipulated three different product categories inside the supermarket, while EEG, behavioral and self-report data were collected. Electroencephalogram (EEG) findings showed a generalized delta band activity in pasta compared to frozen food and it was interpreted as higher emotional activation probably required by selecting the correct stimuli in a multisensory environment. A delta band activation was also found in frontal area in VI compared to control and it was supposed to be an index of greater cognitive control. Finally, higher delta band activity in parieto-occipital and temporal areas were related to greater sense of disorientation. In conclusion, it was found that VI experience grocery shopping more stressfully and with greater cognitive effort (parieto-occipital area) than people without visual disability. In general, VI use the sense of touch (temporal area) more and have more difficulties in orienting themselves internally in the store. The results could encourage the use of tactile touchpoints, braille maps, or an initial guided exploration of the supermarket, to allow the VI to memorize the internal layout of the different product categories and allow them to shop independently. Another suggestion would be to make products within the same product category more distinctive, perhaps by adding additional tactile information.     

Effect of correlated color temperature and illuminance on sustained attention collected via electroencephalography signals

This study aimed to explore the differences in the subjects' sustained attention under the impact of nine lighting conditions consisting of the combination of three commonly used correlated color temperature (CCT) (3300 K, 4300 K, and 5300 K) and illuminance levels (300 lx, 500 lx, and 750 lx) to provide guidance on the adjustment of CCT and illuminance level parameters for indoor lighting. We selected 24 physically and mentally healthy university students (12 male and 12 female) as the experimental subjects. The subjects were required to perform sustained attention to response task (SART) activities under the nine different lighting conditions and collected the alpha (α) waves (8–12 Hz) from the electroencephalography signals. Subsequently, the mean power spectral density of the α waves and various SART parameters were analyzed and tested. Finally, the effects of different CCT and illuminance levels on the subjects' attention were compared. With the increase in CCT, the attention level tended to increase linearly, whereas the attention level was the lowest at 300 lx and the highest at 500 lx and appeared as an inverted “U” shape. The subjects' attention level was the highest at the combination of CCT of 5300 K and illuminance level of 500 lx and the lowest at the combination of CCT of 3300 K and illuminance level of 500 lx. These results provide important data to elucidate the impact of lighting condition on attention.     

基于改进原型网络的P300脑电信号检测

从脑电信号中检测P300电位是实现P300脑机接口的关键. 由于不同个体间的脑电信号存在较大差异, 现有的基于深度学习的P300检测方法均需要大量的脑电数据来训练模型. 对于小样本的患者数据, 至今仍没有令人满意的解决方案. 本文提出了一种改进的适用于小样本P300脑电信号检测的原型网络方法. 该模型通过卷积神经网络提...     

Cognitive neuroscience and robotics: Advancements and future research directions

In recent years, brain-based technologies that capitalise on human abilities to facilitate human–system/robot interactions have been actively explored, especially in brain robotics. Brain–computer interfaces, as applications of this conception, have set a path to convert neural activities recorded by sensors from the human scalp via electroencephalography into valid commands for robot control and task execution. Thanks to the advancement of sensor technologies, non-invasive and invasive sensor headsets have been designed and developed to achieve stable recording of brainwave signals. However, robust and accurate extraction and interpretation of brain signals in brain robotics are critical to reliable task-oriented and opportunistic applications such as brainwave-controlled robotic interactions. In response to this need, pervasive technologies and advanced analytical approaches to translating and merging critical brain functions, behaviours, tasks, and environmental information have been a focus in brain-controlled robotic applications. These methods are composed of signal processing, feature extraction, representation of neural activities, command conversion and robot control. Artificial intelligence algorithms, especially deep learning, are used for the classification, recognition, and identification of patterns and intent underlying brainwaves as a form of electroencephalography. Within the context, this paper provides a comprehensive review of the past and the current status at the intersection of robotics, neuroscience, and artificial intelligence and highlights future research directions.