Abstract. The recent challenge in diffusion imaging is to find acquisition schemes and analysis approaches that can represent non-gaussian diffusion profiles in a clinically feasible measurement time. In this work we investigate the effect of b-value and the number of gradient vector directions on Q-ball imaging and the Diffusion Orientation Transform (DOT) in a structured way using computational simulations, hardware crossing-fiber diffusion phantoms, and in-vivo brain scans. We observe that DOT is more robust to noise and independent of the b-value and number of gradients, whereas Q-ball dramatically improves the results for higher b-values and number of gradients and at recovering larger angles of crossing. We also show that Laplace-Beltrami regularization has wide applicability and generally improves the properties of DOT. Knowledge of optimal acquisition schemes for HARDI can improve the utility of diffusion weighted MR imaging in the clinical setting for the diagnosis of white matter diseases and presurgical planning.

Electroencephalogram (EEG) might be the most predictive and reliable physiological indicator of mental fatigue. However, the extraction of key features from massive EEG data for mental fatigue identification remains a challenge. The objective of this study is to identify the key EEG features in relationship to mental fatigue, from a broad pool of EEG features generated by quantitative EEG (qEEG) techniques, using Random Forests (RF), which is a recently developed machine learning algorithm. The method is applied to key EEG feature extraction for 5-level mental fatigue identification using the five subjects' EEG data recorded in 25-hour fatigue experiments. RF produces significant feature reduction with little compromise of the classification performance. The identified key EEG features also indicate that electrode locations in frontal and occipital regions of the brain are most important for adequate representation of the deactivation of functional lobes of the brain, which is consistent with the anatomical areas known to be involved in mental fatigue. It is also interesting to discover that the four frequency bands are all important for the mental fatigue identification.

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