Matching Items (5)
Filtering by

Clear all filters

153889-Thumbnail Image.png
Description
Robust and stable decoding of neural signals is imperative for implementing a useful neuroprosthesis capable of carrying out dexterous tasks. A nonhuman primate (NHP) was trained to perform combined flexions of the thumb, index and middle fingers in addition to individual flexions and extensions of the same digits. An array

Robust and stable decoding of neural signals is imperative for implementing a useful neuroprosthesis capable of carrying out dexterous tasks. A nonhuman primate (NHP) was trained to perform combined flexions of the thumb, index and middle fingers in addition to individual flexions and extensions of the same digits. An array of microelectrodes was implanted in the hand area of the motor cortex of the NHP and used to record action potentials during finger movements. A Support Vector Machine (SVM) was used to classify which finger movement the NHP was making based upon action potential firing rates. The effect of four feature selection techniques, Wilcoxon signed-rank test, Relative Importance, Principal Component Analysis, and Mutual Information Maximization was compared based on SVM classification performance. SVM classification was used to examine the functional parameters of (i) efficacy (ii) endurance to simulated failure and (iii) longevity of classification. The effect of using isolated-neuron and multi-unit firing rates was compared as the feature vector supplied to the SVM. The best classification performance was on post-implantation day 36, when using multi-unit firing rates the worst classification accuracy resulted from features selected with Wilcoxon signed-rank test (51.12 ± 0.65%) and the best classification accuracy resulted from Mutual Information Maximization (93.74 ± 0.32%). On this day when using single-unit firing rates, the classification accuracy from the Wilcoxon signed-rank test was 88.85 ± 0.61 % and Mutual Information Maximization was 95.60 ± 0.52% (degrees of freedom =10, level of chance =10%)
ContributorsPadmanaban, Subash (Author) / Greger, Bradley (Thesis advisor) / Santello, Marco (Thesis advisor) / Helms Tillery, Stephen (Committee member) / Arizona State University (Publisher)
Created2015
136361-Thumbnail Image.png
Description
Determining the characteristics of an object during a grasping task requires a combination of mechanoreceptors in the muscles and fingertips. The width of a person's finger aperture during the grasp may affect the accuracy of how that person determines hardness, as well. These experiments aim to investigate how an individual

Determining the characteristics of an object during a grasping task requires a combination of mechanoreceptors in the muscles and fingertips. The width of a person's finger aperture during the grasp may affect the accuracy of how that person determines hardness, as well. These experiments aim to investigate how an individual perceives hardness amongst a gradient of varying hardness levels. The trend in the responses is assumed to follow a general psychometric function. This will provide information about subjects' abilities to differentiate between two largely different objects, and their tendencies towards guess-chances upon the presentation of two similar objects. After obtaining this data, it is then important to additionally test varying finger apertures in an object-grasping task. This will allow an insight into the effect of aperture on the obtained psychometric function, thus ultimately providing information about tactile and haptic feedback for further application in neuroprosthetic devices. Three separate experiments were performed in order to test the effect of finger aperture on object hardness differentiation. The first experiment tested a one-finger pressing motion among a hardness gradient of ballistic gelatin cubes. Subjects were asked to compare the hardness of one cube to another, which produced the S-curve that accurately portrayed the psychometric function. The second experiment utilized the Phantom haptic device in a similar setup, using the precision grip grasping motion, instead. This showed a more linear curve; the percentage reported harder increased as the hardness of the second presented cube increased, which was attributed to both the experimental setup limitations and the scale of the general hardness gradient. The third experiment then progressed to test the effect of three finger apertures in the same experimental setup. By providing three separate testing scenarios in the precision grip task, the experiment demonstrated that the level of finger aperture has no significant effect on an individual's ability to perceive hardness.
ContributorsMaestas, Gabrielle Elise (Author) / Helms Tillery, Stephen (Thesis director) / Tanner, Justin (Committee member) / Barrett, The Honors College (Contributor) / Harrington Bioengineering Program (Contributor)
Created2015-05
137282-Thumbnail Image.png
Description
A previous study demonstrated that learning to lift an object is context-based and that in the presence of both the memory and visual cues, the acquired sensorimotor memory to manipulate an object in one context interferes with the performance of the same task in presence of visual information about a

A previous study demonstrated that learning to lift an object is context-based and that in the presence of both the memory and visual cues, the acquired sensorimotor memory to manipulate an object in one context interferes with the performance of the same task in presence of visual information about a different context (Fu et al, 2012).
The purpose of this study is to know whether the primary motor cortex (M1) plays a role in the sensorimotor memory. It was hypothesized that temporary disruption of the M1 following the learning to minimize a tilt using a ‘L’ shaped object would negatively affect the retention of sensorimotor memory and thus reduce interference between the memory acquired in one context and the visual cues to perform the same task in a different context.
Significant findings were shown in blocks 1, 2, and 4. In block 3, subjects displayed insignificant amount of learning. However, it cannot be concluded that there is full interference in block 3. Therefore, looked into 3 effects in statistical analysis: the main effects of the blocks, the main effects of the trials, and the effects of the blocks and trials combined. From the block effects, there is a p-value of 0.001, and from the trial effects, the p-value is less than 0.001. Both of these effects indicate that there is learning occurring. However, when looking at the blocks * trials effects, we see a p-value of 0.002 < 0.05 indicating significant interaction between sensorimotor memories. Based on the results that were found, there is a presence of interference in all the blocks but not enough to justify the use of TMS in order to reduce interference because there is a partial reduction of interference from the control experiment. It is evident that the time delay might be the issue between context switches. By reducing the time delay between block 2 and 3 from 10 minutes to 5 minutes, I will hope to see significant learning to occur from the first trial to the second trial.
ContributorsHasan, Salman Bashir (Author) / Santello, Marco (Thesis director) / Kleim, Jeffrey (Committee member) / Helms Tillery, Stephen (Committee member) / Barrett, The Honors College (Contributor) / W. P. Carey School of Business (Contributor) / Harrington Bioengineering Program (Contributor)
Created2014-05
137004-Thumbnail Image.png
Description
Brain-computer interface technology establishes communication between the brain and a computer, allowing users to control devices, machines, or virtual objects using their thoughts. This study investigates optimal conditions to facilitate learning to operate this interface. It compares two biofeedback methods, which dictate the relationship between brain activity and the movement

Brain-computer interface technology establishes communication between the brain and a computer, allowing users to control devices, machines, or virtual objects using their thoughts. This study investigates optimal conditions to facilitate learning to operate this interface. It compares two biofeedback methods, which dictate the relationship between brain activity and the movement of a virtual ball in a target-hitting task. Preliminary results indicate that a method in which the position of the virtual object directly relates to the amplitude of brain signals is most conducive to success. In addition, this research explores learning in the context of neural signals during training with a BCI task. Specifically, it investigates whether subjects can adapt to parameters of the interface without guidance. This experiment prompts subjects to modulate brain signals spectrally, spatially, and temporally, as well differentially to discriminate between two different targets. However, subjects are not given knowledge regarding these desired changes, nor are they given instruction on how to move the virtual ball. Preliminary analysis of signal trends suggests that some successful participants are able to adapt brain wave activity in certain pre-specified locations and frequency bands over time in order to achieve control. Future studies will further explore these phenomena, and future BCI projects will be advised by these methods, which will give insight into the creation of more intuitive and reliable BCI technology.
ContributorsLancaster, Jenessa Mae (Co-author) / Appavu, Brian (Co-author) / Wahnoun, Remy (Co-author, Committee member) / Helms Tillery, Stephen (Thesis director) / Barrett, The Honors College (Contributor) / Harrington Bioengineering Program (Contributor) / Department of Psychology (Contributor)
Created2014-05
Description
The human hand relies on information from surrounding environment to distinguish objects based on qualities like size, texture, weight, and compliance. The size of an object can be determined from tactile feedback, proprioception, and visual feedback. This experiment aims to determine the accuracy of size discrimination in physical and virtual

The human hand relies on information from surrounding environment to distinguish objects based on qualities like size, texture, weight, and compliance. The size of an object can be determined from tactile feedback, proprioception, and visual feedback. This experiment aims to determine the accuracy of size discrimination in physical and virtual objects using proprioceptive and tactile feedback. Using both senses will help determine how much proprioceptive and tactile feedback plays a part in discriminating small size variations and whether replacing a missing sensation will increase the subject's accuracy. Ultimately, determining the specific contributions of tactile and proprioceptive feedback mechanisms during object manipulation is important in order to give prosthetic hand users the ability of stereognosis among other manipulation tasks. Two different experiments using physical and virtual objects were required to discover the roles of tactile and proprioceptive feedback. Subjects were asked to compare the size of one block to a previous object. The blocks increased in size by two millimeter increments and were randomized in order to determine whether subjects could correctly identify if a box was smaller, larger, or the same size as the previous box. In the proprioceptive experiment subjects had two sub-sets of experiments each with a different non-tactile cue. The experiment demonstrated that subjects performed better with physical objects compared to virtual objects. This suggests that size discrimination is possible in the absence of tactile feedback, but tactile input is necessary for accuracy in small size discrimination.
ContributorsFrear, Darcy Lynn (Author) / Helms Tillery, Stephen (Thesis director) / Buneo, Christopher (Committee member) / Overstreet, Cynthia (Committee member) / Barrett, The Honors College (Contributor) / Harrington Bioengineering Program (Contributor)
Created2013-05