Electroencephalographic Signal Processing and Classification Techniques for Noninvasive Motor Imagery Based Brain Computer Interface

In motor imagery (MI) based brain-computer interface (BCI), success depends on reliable processing of the noisy, non-linear, and non-stationary brain activity signals for extraction of features and effective classification of MI activity as well as translation to the corresponding intended actions. In this study, signal processing and classification techniques are presented for electroencephalogram (EEG) signals for motor imagery based brain-computer interface. EEG signals have been acquired placing the electrodes following the international 10-20 system. The acquired signals have been pre-processed removing artifacts using empirical mode decomposition (EMD) and two extended versions of EMD, ensemble empirical mode decomposition (EEMD), and multivariate empirical mode decomposition (MEMD) leading to better signal to noise ratio (SNR) and reduced mean square error (MSE) compared to independent component analysis (ICA). EEG signals have been decomposed into independent mode function (IMFs) that are further processed to extract features like sample entropy (SampEn) and band power (BP). The extracted features have been used in support vector machines to characterize and identify MI activities. EMD and its variants, EEMD, MEMD have been compared with common spatial pattern (CSP) for different MI activities. SNR values from EMD, EEMD and MEMD (4.3, 7.64, 10.62) are much better than ICA (2.1) but accuracy of MI activity identification is slightly better for ICA than EMD using BP and SampEn. Further work is outlined to include more features with larger database for better classification accuracy

Damping and Ejection of Sloshing Liquid Drops from Elastic Substrates

Liquid slosh is a potential source of disturbance of the motion of a moving structure. In this thesis, we probe the damping technique of sessile drop by absorbing a portion of the kinetic energy. The sloshing dynamics are typically represented by a mechanical model of a spring mass damper. However, damping by drop sloshing is dependent on viscosity, surface tension, drop size and drop location. We explore highly-coupled fluid-solid mechanics using singular liquid drop with varying viscosity and surface tension resting on a millimetric cantilever. Cantilevers are displaced 0.6 mm and their free end is allowed to vibrate freely. Cantilever vibration causes drops to deform, or slosh, which dissipates kinetic energy via viscous dissipation within the drop. A solid weight with the same mass as experimental drops is used to compare the damping imposed by liquids, thereby accounting for other damping sources. Neither the most viscous nor least viscous drops studied imposed the greatest damping on cantilever motion. Instead, drops of intermediate viscosity strike the most effective balance of sloshing and internal dissipative capacity. The removal of pinned drops from small, delicate surfaces such as sensors and flight surfaces on micro-flyers can be challenging due to remote location and small scale and they require large deflection. Robustness is enhanced when such surfaces, of comparable scale to deposited drops, can remove deposition without external influence. Drop ejection for drops larger than the capillary length, can be a complicated, multi-stage event in which fluid removal occurs through multiple mechanisms in sequence. In this combined experimental and theoretical work, we propose drop release mechanism from elastic materials and characterization of drop sloshing damping. In our primary work, we observe three principal modes of drop release that can be singly witnessed under the appropriate set of cantilevers and drop conditions. We categorize these three release modes as sliding, normal-to-cantilever ejection, and pinch-off. We found that, the selection of system variables such as cantilever length L (a proxy for stiffness), drop location, drop size and wettability allows for the solicitation of a particular ejection mode

Electroencephalographic signal processing and classification techniques for noninvasive motor imagery based brain computer interface

In motor imagery (MI) based brain-computer interface (BCI), success depends on reliable processing of the noisy, non-linear, and non-stationary brain activity signals for extraction of features and effective classification of MI activity as well as translation to the corresponding intended actions. In this study, signal processing and classification techniques are presented for electroencephalogram (EEG) signals for motor imagery based brain-computer interface. EEG signals have been acquired placing the electrodes following the international 10-20 system. The acquired signals have been pre-processed removing artifacts using empirical mode decomposition (EMD) and two extended versions of EMD, ensemble empirical mode decomposition (EEMD), and multivariate empirical mode decomposition (MEMD) leading to better signal to noise ratio (SNR) and reduced mean square error (MSE) compared to independent component analysis (ICA). EEG signals have been decomposed into independent mode function (IMFs) that are further processed to extract features like sample entropy (SampEn) and band power (BP). The extracted features have been used in support vector machines to characterize and identify MI activities. EMD and its variants, EEMD, MEMD have been compared with common spatial pattern (CSP) for different MI activities. SNR values from EMD, EEMD and MEMD (4.3, 7.64, 10.62) are much better than ICA (2.1) but accuracy of MI activity identification is slightly better for ICA than EMD using BP and SampEn. Further work is outlined to include more features with larger database for better classification accuracy

Liquid jet stability through elastic planar nozzles

An extensive number of processes require liquid jets such as cleaning, waterjet cutting, hydroentanglement, and atomization in combustion. The coherence and stability of the jet highly depend on the characteristics of the nozzle. Jet breakup lengths have been extensively studied for a multitude of nozzle characteristics and external stimuli, yet jets issuing from deformable, elastic nozzles have not been considered. In this study, we take the enduring topic of jet breakup into a new realm by introducing nozzles that passively deform when exposed to liquid flow by making an approximately 500 \upmu \hbox {m} orifice in thin sheets. We perform the experiments with nozzles of varying hardness and thickness, starting with a rigid BeCu nozzle, and continuing with shore hardness 70A, 65A, 35A and 20A. We observe nozzle dilation scales well with Reynolds number and that softer nozzles experiences greater dilation, as expected. We introduce a modification to linear stability theory to describe the break-up length of deformable nozzles to account for the dilation, a scaling which works best for our stiffer nozzles. The three softest materials provide the most stable jets through the range of flow rates in which they can operate before failure. For all nozzles, breakup is highly variable with time and jet velocity