Decoding motor commands from noninvasively measured neural signals has become important in brain-computer interface (BCI) research. Applications of BCI include neurorehabilitation after stroke and control of limb prostheses. Until now, most studies have tested simple movement trajectories in two dimensions by using constant velocity profiles. However, most real-world scenarios require much more complex movement trajectories and velocity profiles. In this study, we decoded motor commands in three dimensions from electroencephalography (EEG) recordings while the subjects either executed or observed/imagined complex upper limb movement trajectories. We compared the accuracy of simple linear methods and nonlinear methods. In line with previous studies our results showed that linear decoders are an efficient and robust method for decoding motor commands. However, while we took the same precautions as previous studies to suppress eye-movement related EEG contamination, we found that subtracting residual electro-oculogram activity from the EEG data resulted in substantially lower motor decoding accuracy for linear decoders. This effect severely limits the transfer of previous results to practical applications in which neural activation is targeted. We observed that nonlinear methods showed no such drop in decoding performance. Our results demonstrate that eye-movement related contamination of brain signals constitutes a severe problem for decoding motor signals from EEG data. These results are important for developing accurate decoders of motor signal from neural signals for use with BCI-based neural prostheses and neurorehabilitation in real-world scenarios.

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Nanopatterning provides facile process to well-arrayed mesoporous inorganic oxide films at low cost by using readily available pastes and elastomeric nanostamps. The fabricated nanopattern boosted the light-harvesting efficiency of dye-sensitized solar cells (DSSCs) by a light-trapping technique. The iodine-free solid-state DSSCs showed a 40 % increase in the current density and high efficiency (7.03 %).

To sustainably satisfy the growing demand for energy, organic carbonyl compounds (OCCs) are being widely studied as electrode active materials for batteries owing to their high capacity, flexible structure, low cost, environmental friendliness, renewability, and universal applicability. However, their high solubility in electrolytes, limited active sites, and low conductivity are obstacles in increasing their usage. Here, the nucleophilic addition reaction of aromatic carbonyl compounds (ACCs) is first used to explain the electrochemical behavior of carbonyl compounds during charge–discharge, and the relationship of the molecular structure and electrochemical properties of ACCs are discussed. Strategies for molecular structure modifications to improve the performance of ACCs, i.e., the capacity density, cycle life, rate performance, and voltage of the discharge platform, are also elaborated. ACCs, as electrode active materials in aqueous solutions, will become a future research hotspot. ACCs will inevitably become sustainable green materials for batteries with high capacity density and high power density.