Brain computer interface (BCI) is an assistive technology, which decodes neurophysiological signals generated by the human brain and translates them into control signals to control external devices, e.g., wheelchairs. One problem challenging noninvasive BCI technologies is the limited control dimensions from decoding movements of, mainly, large body parts, e.g., upper and lower limbs. It has been reported that complicated dexterous functions, i.e., finger movements, can be decoded in electrocorticography (ECoG) signals, while it remains unclear whether noninvasive electroencephalography (EEG) signals also have sufficient information to decode the same type of movements. Phenomena of broadband power increase and low-frequency-band power decrease were observed in EEG in the present study, when EEG power spectra were decomposed by a principal component analysis (PCA). These movement-related spectral structures and their changes caused by finger movements in EEG are consistent with observations in previous ECoG study, as well as the results from ECoG data in the present study. The average decoding accuracy of 77.11% over all subjects was obtained in classifying each pair of fingers from one hand using movement-related spectral changes as features to be decoded using a support vector machine (SVM) classifier. The average decoding accuracy in three epilepsy patients using ECoG data was 91.28% with the similarly obtained features and same classifier. Both decoding accuracies of EEG and ECoG are significantly higher than the empirical guessing level (51.26%) in all subjects (p<0.05). The present study suggests the similar movement-related spectral changes in EEG as in ECoG, and demonstrates the feasibility of discriminating finger movements from one hand using EEG. These findings are promising to facilitate the development of BCIs with rich control signals using noninvasive technologies.

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The present study sought to determine whether supplementation of long-chain polyunsaturated fatty acids (LCPUFA) during the first year of life influenced brain function, structure and metabolism at 9 years of age. Newborns were randomly assigned to consume formula containing either no LCPUFA (control) or formula with 0.64% of total fatty acids as arachidonic acid (ARA; 20:4n6) and variable amounts of docosahexaenoic acid (DHA; 22:6n3) (0.32%, 0.64%, or 0.96% of total fatty acids) from birth to 12 mo. At age 9 years (+/− 0.6) 42 children enrolled in a follow up multimodal magnetic resonance imaging (MRI) study including functional (fMRI, Flanker task), resting state (rsMRI), anatomic, and proton magnetic resonance spectroscopy (1H MRS). fMRI analysis using the Flanker task found that trials requiring greater inhibition elicited greater brain activation in LCPUFA-supplemented children in anterior cingulate cortex (ACC) and parietal regions. rsMRI analysis showed that children in the 0.64% group exhibited greater connectivity between prefrontal and parietal regions compared to all other groups. In addition, voxel-based analysis (VBM) revealed that the 0.32% and 0.64% groups had greater white matter volume in ACC and parietal regions compared to controls and the 0.96% group. Finally, 1H MRS data analysis identified that N-acetylaspartate (NAA) and myo-inositol (mI) were higher in LCPUFA groups compared to the control group. LCPUFA supplementation during infancy has lasting effects on brain structure, function, and neurochemical concentrations in regions associated with attention (parietal) and inhibition (ACC), as well as neurochemicals associated with neuronal integrity (NAA) and brain cell signaling (mI).

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