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Multimodal neuroimaging, such as combined electroencephalography (EEG) and functional magnetic resonance imaging (fMRI), are being increasingly used to investigate the human brain in healthy and diseased conditions. However, certain neuroimaging data are typically acquired in different body positions, e.g., supine fMRI and upright EEG, overlooking the effect of body position on signal characteristics. In the current study we examined EEG signals in three different positions, i.e., supine, standing and sitting, in patients with a balance disorder called mal de debarquement syndrome (MdDS). Individuals with MdDS experience a chronic illusion of self-motion triggered by prolonged exposure to passive motion, such as from sea or air travel. The degree of perception of rocking dizziness is modulated by body position, suggesting a physiological effect related to body positions. In the present study, EEG features were quantified as peak frequency, peak amplitude, and average amplitude of the alpha band due to its strongest signal characteristics compared to other frequencies. The effect of body position was examined in EEG features from data acquired before and after the individuals received treatment with repetitive transcranial magnetic stimulation. Our results indicate a significant effect of body positions on the EEG signals in MdDS.

A suboptimal solution to constrained linear time varying quadratic regulation (CLTVQR) is proposed. In a neighborhood of the origin, the problem is formulated as a min-max LQR based on polytopic inclusion of the dynamics in this neighborhood. Outside this neighborhood, the control moves are obtained by solving a constrained finite horizon optimization problem. The main contribution is to obtain a cost value arbitrarily close (but not equal) to that of the optimal CLTVQR. The suboptimal CLTVQR preserves the feasibility of the optimal CLTVQR if and only if the min-max LQR exists feasible solution. By mild modification, this suboptimal method can be applied to nonlinear systems.