Brain-Computer Interface Based on Generation of Visual Images

This paper examines the task of recognizing EEG patterns that correspond to performing three mental tasks: relaxation and imagining of two types of pictures: faces and houses. The experiments were performed using two EEG headsets: BrainProducts ActiCap and Emotiv EPOC. The Emotiv headset becomes widely used in consumer BCI application allowing for conducting large-scale EEG experiments in the future. Since classification accuracy significantly exceeded the level of random classification during the first three days of the experiment with EPOC headset, a control experiment was performed on the fourth day using ActiCap. The control experiment has shown that utilization of high-quality research equipment can enhance classification accuracy (up to 68% in some subjects) and that the accuracy is independent of the presence of EEG artifacts related to blinking and eye movement. This study also shows that computationally-inexpensive Bayesian classifier based on covariance matrix analysis yields similar classification accuracy in this problem as a more sophisticated Multi-class Common Spatial Patterns (MCSP) classifier

A comparison of regression techniques for a two-dimensional sensorimotor rhythm-based brain-computer interface.

International audiencePeople can learn to control electroencephalogram (EEG) features consisting of sensorimotor-rhythm amplitudes and use this control to move a cursor in one, two or three dimensions to a target on a video screen. This study evaluated several possible alternative models for translating these EEG features into two-dimensional cursor movement by building an offline simulation using data collected during online performance. In offline comparisons, support-vector regression (SVM) with a radial basis kernel produced somewhat better performance than simple multiple regression, the LASSO or a linear SVM. These results indicate that proper choice of a translation algorithm is an important factor in optimizing brain-computer interface (BCI) performance, and provide new insight into algorithm choice for multidimensional movement control

Lipases and carboxylesterases affect moth sex pheromone compounds involved in interspecific mate recognition

Moth sex pheromones are a classical model for studying sexual selection. Females typically produce a species-specific pheromone blend that attracts males. Revealing the enzymes involved in the interspecific variation in blend composition is key for understanding the evolution of these sexual communication systems. The nature of the enzymes involved in the variation of acetate esters, which are prominent compounds in moth pheromone blends, remains unclear. We identify enzymes involved in acetate degradation using two closely related moth species: Heliothis (Chloridea) subflexa and H. (C.) virescens, which have different quantities of acetate esters in their sex pheromone. Through comparative transcriptomic analyses and CRISPR/Cas9 knockouts, we show that two lipases and two esterases from H. virescens reduce the levels of pheromone acetate esters when expressed in H. subflexa females. Together, our results show that lipases and carboxylesterases are involved in tuning Lepidoptera pheromones composition.</p