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.

Vibration-based health monitoring (VBHM) has successfully been used to assess the structural damage to bridges, buildings, aircraft, and rotating machinery. There is significant incentive to apply VBHM techniques to the damage detection and conditional assessment of earth structures (geostructures), e.g., foundations, dams, embankments, and tunnels, to improve design, construction, and performance. An experimental program was carried out to explore the efficacy of VBHM of earth structures. A vibratory roller compactor, instrumented with triaxial accelerometers to continuously measure drum and frame vibrations, was operated on a number of underlying material structures with varying properties. Time-domain and frequency-domain analyses of the coupled machine/earth structure response were performed to glean machine vibration features that reflect changes in underlying structural properties. Results illustrate that drum and frame acceleration amplitudes were fairly insensitive to changes in underlying media stiffness; however, drum acceleration frequency components (harmonics) were found to be sensitive to changes in underlying media and changes in soil properties during compaction. The strata underlying the soil undergoing compaction was found to strongly affect drum vibration characteristics.