There is a recent increase in the use of multivariate analysis and pattern classification in prediction and real-time feedback of brain states from functional imaging signals and mapping of spatio-temporal patterns of brain activity. Here we present MANAS, a generalized software toolbox for performing online and offline classification of fMRI signals. MANAS has been developed using MATLAB, LIBSVM, and SVMlight packages to achieve a cross-platform environment. MANAS is targeted for neuroscience investigations and brain rehabilitation applications, based on neurofeedback and brain-computer interface (BCI) paradigms. MANAS provides two different approaches for real-time classification: subject dependent and subject independent classification. In this article, we present the methodology of real-time subject dependent and subject independent pattern classification of fMRI signals; the MANAS software architecture and subsystems; and finally demonstrate the use of the system with experimental results.

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This paper explains a current sensing technique suitable for digital controller implementation. The technique uses only one analog comparator to estimate the DC as well as ripple of the inductor current of a regulated DC-DC converter. For systems where input voltage variation is anticipated, a low speed ADC can be used to take care of the variation in input voltage for inductor current estimation. The proposed technique is experimentally demonstrated for buck and boost converters and can be easily extended for other converters. A lab prototype proves the operation of the sensing technique using a DSP (TMS-F28335) based digital voltage mode control. The verification of the proposed method to implement hysteretic current mode control using PSPICE simulation is also provided to validate its effectiveness.