A. James Clark School of Engineering
Permanent URI for this communityhttp://hdl.handle.net/1903/1654
The collections in this community comprise faculty research works, as well as graduate theses and dissertations.
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Item Decoding Repetitive Finger Movements with Brain Signals Acquired Via Noninvasive Electroencephalography(2011) Paek, Andrew Young; Contreras-Vidal, Jose L; Bioengineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)We investigated how well finger movements can be decoded from electroencephalography (EEG) signals. 18 hand joint angles were measured simultaneously with 64-channel EEG while subjects performed a repetitive finger tapping task. A linear decoder with memory was used to predict continuous index finger angular velocities from EEG signals. A genetic algorithm was used to select EEG channels across temporal lags between the EEG and kinematics recordings, which optimized decoding accuracies. To evaluate the accuracy of the decoder, the Pearson's correlation coefficient (r) between the observed and predicted trajectories was calculated in a 10-fold cross-validation scheme. Our results (median r = .403, maximum r = .704), compare favorably with previous studies that used electrocorticography (ECoG) to decode finger movements. The decoder used in this study can be used for future brain machine interfaces, where individuals can control peripheral devices through EEG signals.Item Human Gait Based Relative Foot Sensing for Personal Navigation(2010) Spiridonov, Timofey N.; Pines, Darryll J; Aerospace Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Human gait dynamics were studied to aid the design of a robust personal navigation and tracking system for First Responders traversing a variety of GPS-denied environments. IMU packages comprised of accelerometers, gyroscopes, and magnetometer are positioned on each ankle. Difficulties in eliminating drift over time make inertial systems inaccurate. A novel concept for measuring relative foot distance via a network of RF Phase Modulation sensors is introduced to augment the accuracy of inertial systems. The relative foot sensor should be capable of accurately measuring distances between each node, allowing for the geometric derivation of a drift-free heading and distance. A simulation to design and verify the algorithms was developed for five subjects in different gait modes using gait data from a VICON motion capture system as input. These algorithms were used to predict the distance traveled up to 75 feet, with resulting errors on the order of one percent.Item An Optimal Control Model for Human Postural Regulation(2010) Li, Yao; Levine, William S; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Human upright stance is inherently unstable without a balance control scheme. Many biological behaviors are likely to be optimal with respect to some performance measure that involves energy. It is reasonable to believe that the human is (unconsciously) optimizing some performance measure as he regulates his balance posture. In experimental studies, a notable feature of postural control is a small constant sway. Specifically, there is greater sway than would occur with a linear feedback control without delay. A second notable feature of the human postural control is that the response to perturbations varies with their amplitude. Small disturbances produce motion only at the ankles with the hip and knee angles unchanging. Large perturbation evoke ankle and hip angular movement only. Still larger perturbation result in movement of all three joint angles. Inspired by these features, a biomechanical model resembling human balance control is proposed. The proposed model consists of three main components which are the body dynamics, a sensory estimator for delay and disturbance, and an optimal nonlinear control scheme providing minimum required corrective response. The human body is modeled as a multiple segment inverted pendulum in the sagittal plane and controlled by ankle and hip joint torques. A series of nonlinear optimal control problems are devised as mathematical models of human postural control during quiet standing. Several performance criteria that are high even orders in the body state or functions of these states (such as joint angle, Center of Pressure COP or Center of Mass COM) and quadratic in the joint control are utilized. This objective function provides a trade-off between the allowed deviations of the position from its nominal value and the neuromuscular energy required to correct for these deviations. Note that this performance measure reduces the actuator energy used by penalizing small postural errors very lightly. By using the Model Predictive Control (MPC) technique, the discrete-time approximation to each of these problems can be converted into a nonlinear programming problem and then solved by optimization methods. The solution gives a control scheme that agrees with the main features of the joint kinematics and its coordination process. The derived model is simulated for different scenarios to validate and test the performance of the proposed postural control architecture.Item Noninvasive neural decoding of overt and covert hand movement(2010) Bradberry, Trent Jason; Contreras-Vidal, José L.; Bioengineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)It is generally assumed that the signal-to-noise ratio and information content of neural data acquired noninvasively via magnetoencephalography (MEG) or scalp electroencephalography (EEG) are insufficient to extract detailed information about natural, multi-joint movements of the upper limb. If valid, this assumption could severely limit the practical usage of noninvasive signals in brain-computer interface (BCI) systems aimed at continuous complex control of arm-like prostheses for movement impaired persons. Fortunately this dissertation research casts doubt on the veracity of this assumption by extracting continuous hand kinematics from MEG signals collected during a 2D center-out drawing task (Bradberry et al. 2009, NeuroImage, 47:1691-700) and from EEG signals collected during a 3D center-out reaching task (Bradberry et al. 2010, Journal of Neuroscience, 30:3432-7). In both studies, multiple regression was performed to find a matrix that mapped past and current neural data from multiple sensors to current hand kinematic data (velocity). A novel method was subsequently devised that incorporated the weights of the mapping matrix and the standardized low resolution electromagnetic tomography (sLORETA) software to reveal that the brain sources that encoded hand kinematics in the MEG and EEG studies were corroborated by more traditional studies that required averaging across trials and/or subjects. Encouraged by the favorable results of these off-line decoding studies, a BCI system was developed for on-line decoding of covert movement intentions that provided users with real-time visual feedback of the decoder output. Users were asked to use only their thoughts to move a cursor to acquire one of four targets on a computer screen. With only one training session, subjects were able to accomplish this task. The promising results of this dissertation research significantly advance the state-of-the-art in noninvasive BCI systems.