Resolution and sensitivity of the eyes of the Asian honeybees Apis florea, Apis cerana and Apis dorsata

Bees of the genus Apis are important foragers of nectar and pollen resources. Although the European honeybee, Apis mellifera, has been well studied with respect to its sensory abilities, learning behaviour and role as pollinators, much less is known about the other Apis species. We studied the anatomical spatial resolution and absolute sensitivity of the eyes of three sympatric species of Asian honeybees, Apis cerana, Apis florea and Apis dorsata and compared them with the eyes of A. mellifera. Of these four species, the giant honeybee A. dorsata (which forages during moonlit nights) has the lowest spatial resolution and the most sensitive eyes, followed by A. mellifera, A. cerana and the dwarf honeybee, A. florea (which has the smallest acceptance angles and the least sensitive eyes). Moreover, unlike the strictly diurnal A. cerana and A. florea, A. dorsata possess large ocelli, a feature that it shares with all dim-light bees. However, the eyes of the facultatively nocturnal A. dorsata are much less sensitive than those of known obligately nocturnal bees such as Megalopta genalis in Panama and Xylocopa tranquebarica in India. The differences in sensitivity between the eyes of A. dorsata and other strictly diurnal Apis species cannot alone explain why the former is able to fly, orient and forage at half-moon light levels. We assume that additional neuronal adaptations, as has been proposed for A. mellifera, M. genalis and X. tranquebarica, might exist in A. dorsata.

A Bacterial Foraging Optimization and Learning Automata Based Feature Selection for Motor Imagery EEG Classification

Selection of relevant features is an open problem in Brain-computer interfacing (BCI) research. Sometimes, features extracted from brain signals are high dimensional which in turn affects the accuracy of the classifier. Selection of the most relevant features improves the performance of the classifier and reduces the computational cost of the system. In this study, we have used a combination of Bacterial Foraging Optimization and Learning Automata to determine the best subset of features from a given motor imagery electroencephalography (EEG) based BCI dataset. Here, we have employed Discrete Wavelet Transform to obtain a high dimensional feature set and classified it by Distance Likelihood Ratio Test. Our proposed feature selector produced an accuracy of 80.291% in 216 seconds.