Matching Items (7)

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The Use of Brain Signals to Control a Robotic Car: A First Step

Description

In this study, the engineers from biomedical engineering and electrical engineering researched and analyzed the components, uses, and processes for the brain and the Brain-Computer Interfaces (BCIs). They investigated the

In this study, the engineers from biomedical engineering and electrical engineering researched and analyzed the components, uses, and processes for the brain and the Brain-Computer Interfaces (BCIs). They investigated the basics on the brain, the signals, and the overall uses of the devices. There have been many uses for electroencephalogram (EEG) signals, including prosthetics for patients after nerve injuries, cursor movements on a computer, moving vehicles, and many more projects. There are studies currently in progress and that will be in progress in the future that extend the uses of BCIs. The researchers in this thesis focused more on the processes the scientists used to approach the given problem. Some worked with patients to better his or her life, while others worked with volunteers to gain more knowledge of the brain and/or the BCIs. This thesis includes many different approaches for many unique projects. The analysis includes the location of the signal, the processing of the signal, the filtering of the signal, the transmission of the signal, and the movement of the device based on the signal. The current BCIs are not ready to be in patient’s daily lives, but the researchers are trying to create and perfect them in order to help as many patients as possible. As a biomedical engineer, the researchers in this thesis can apply the knowledge from the articles to solving potential problems in the future and further specific studies.

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Date Created
  • 2019-05

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Neural correlates of learning in brain machine interface controlled tasks

Description

Brain-machine interfaces (BMIs) were first imagined as a technology that would allow subjects to have direct communication with prosthetics and external devices (e.g. control over a computer cursor or robotic

Brain-machine interfaces (BMIs) were first imagined as a technology that would allow subjects to have direct communication with prosthetics and external devices (e.g. control over a computer cursor or robotic arm movement). Operation of these devices was not automatic, and subjects needed calibration and training in order to master this control. In short, learning became a key component in controlling these systems. As a result, BMIs have become ideal tools to probe and explore brain activity, since they allow the isolation of neural inputs and systematic altering of the relationships between the neural signals and output. I have used BMIs to explore the process of brain adaptability in a motor-like task. To this end, I trained non-human primates to control a 3D cursor and adapt to two different perturbations: a visuomotor rotation, uniform across the neural ensemble, and a decorrelation task, which non-uniformly altered the relationship between the activity of particular neurons in an ensemble and movement output. I measured individual and population level changes in the neural ensemble as subjects honed their skills over the span of several days. I found some similarities in the adaptation process elicited by these two tasks. On one hand, individual neurons displayed tuning changes across the entire ensemble after task adaptation: most neurons displayed transient changes in their preferred directions, and most neuron pairs showed changes in their cross-correlations during the learning process. On the other hand, I also measured population level adaptation in the neural ensemble: the underlying neural manifolds that control these neural signals also had dynamic changes during adaptation. I have found that the neural circuits seem to apply an exploratory strategy when adapting to new tasks. Our results suggest that information and trajectories in the neural space increase after initially introducing the perturbations, and before the subject settles into workable solutions. These results provide new insights into both the underlying population level processes in motor learning, and the changes in neural coding which are necessary for subjects to learn to control neuroprosthetics. Understanding of these mechanisms can help us create better control algorithms, and design training paradigms that will take advantage of these processes.

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Created

Date Created
  • 2015

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Brain computer interfaces for the control of robotic swarms

Description

A robotic swarm can be defined as a large group of inexpensive, interchangeable

robots with limited sensing and/or actuating capabilities that cooperate (explicitly

or implicitly) based on local communications and sensing in

A robotic swarm can be defined as a large group of inexpensive, interchangeable

robots with limited sensing and/or actuating capabilities that cooperate (explicitly

or implicitly) based on local communications and sensing in order to complete a

mission. Its inherent redundancy provides flexibility and robustness to failures and

environmental disturbances which guarantee the proper completion of the required

task. At the same time, human intuition and cognition can prove very useful in

extreme situations where a fast and reliable solution is needed. This idea led to the

creation of the field of Human-Swarm Interfaces (HSI) which attempts to incorporate

the human element into the control of robotic swarms for increased robustness and

reliability. The aim of the present work is to extend the current state-of-the-art in HSI

by applying ideas and principles from the field of Brain-Computer Interfaces (BCI),

which has proven to be very useful for people with motor disabilities. At first, a

preliminary investigation about the connection of brain activity and the observation

of swarm collective behaviors is conducted. After showing that such a connection

may exist, a hybrid BCI system is presented for the control of a swarm of quadrotors.

The system is based on the combination of motor imagery and the input from a game

controller, while its feasibility is proven through an extensive experimental process.

Finally, speech imagery is proposed as an alternative mental task for BCI applications.

This is done through a series of rigorous experiments and appropriate data analysis.

This work suggests that the integration of BCI principles in HSI applications can be

successful and it can potentially lead to systems that are more intuitive for the users

than the current state-of-the-art. At the same time, it motivates further research in

the area and sets the stepping stones for the potential development of the field of

Brain-Swarm Interfaces (BSI).

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Created

Date Created
  • 2017

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Use of Bayesian filtering and adaptive learning methods to improve the detection and estimation of pathological and neurological disorders

Description

Biological and biomedical measurements, when adequately analyzed and processed, can be used to impart quantitative diagnosis during primary health care consultation to improve patient adherence to recommended treatments. For example,

Biological and biomedical measurements, when adequately analyzed and processed, can be used to impart quantitative diagnosis during primary health care consultation to improve patient adherence to recommended treatments. For example, analyzing neural recordings from neurostimulators implanted in patients with neurological disorders can be used by a physician to adjust detrimental stimulation parameters to improve treatment. As another example, biosequences, such as sequences from peptide microarrays obtained from a biological sample, can potentially provide pre-symptomatic diagnosis for infectious diseases when processed to associate antibodies to specific pathogens or infectious agents. This work proposes advanced statistical signal processing and machine learning methodologies to assess neurostimulation from neural recordings and to extract diagnostic information from biosequences.

For locating specific cognitive and behavioral information in different regions of the brain, neural recordings are processed using sequential Bayesian filtering methods to detect and estimate both the number of neural sources and their corresponding parameters. Time-frequency based feature selection algorithms are combined with adaptive machine learning approaches to suppress physiological and non-physiological artifacts present in neural recordings. Adaptive processing and unsupervised clustering methods applied to neural recordings are also used to suppress neurostimulation artifacts and classify between various behavior tasks to assess the level of neurostimulation in patients.

For pathogen detection and identification, random peptide sequences and their properties are first uniquely mapped to highly-localized signals and their corresponding parameters in the time-frequency plane. Time-frequency signal processing methods are then applied to estimate antigenic determinants or epitope candidates for detecting and identifying potential pathogens.

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Created

Date Created
  • 2016

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EEG-Based Estimation of Human Reaction Time Corresponding to Change of Visual Event.

Description

The human brain controls a person's actions and reactions. In this study, the main objective is to quantify reaction time towards a change of visual event and figuring out the

The human brain controls a person's actions and reactions. In this study, the main objective is to quantify reaction time towards a change of visual event and figuring out the inherent relationship between response time and corresponding brain activities. Furthermore, which parts of the human brain are responsible for the reaction time is also of interest. As electroencephalogram (EEG) signals are proportional to the change of brain functionalities with time, EEG signals from different locations of the brain are used as indicators of brain activities. As the different channels are from different parts of our brain, identifying most relevant channels can provide the idea of responsible brain locations. In this study, response time is estimated using EEG signal features from time, frequency and time-frequency domain. Regression-based estimation using the full data-set results in RMSE (Root Mean Square Error) of 99.5 milliseconds and a correlation value of 0.57. However, the addition of non-EEG features with the existing features gives RMSE of 101.7 ms and a correlation value of 0.58. Using the same analysis with a custom data-set provides RMSE of 135.7 milliseconds and a correlation value of 0.69. Classification-based estimation provides 79% & 72% of accuracy for binary and 3-class classication respectively. Classification of extremes (high-low) results in 95% of accuracy. Combining recursive feature elimination, tree-based feature importance, and mutual feature information method, important channels, and features are isolated based on the best result. As human response time is not solely dependent on brain activities, it requires additional information about the subject to improve the reaction time estimation.

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Date Created
  • 2019

Novel organic light emitting diodes for optogenetic experiments

Description

Optical Fibers coupled to laser light sources, and Light Emitting Diodes are the two classes of technologies used for optogenetic experiments. Arizona State University's Flexible Display Center fabricates novel flexible

Optical Fibers coupled to laser light sources, and Light Emitting Diodes are the two classes of technologies used for optogenetic experiments. Arizona State University's Flexible Display Center fabricates novel flexible Organic Light Emitting Diodes(OLEDs). These OLEDs have the capability of being monolithically fabricated over flexible, transparent plastic substrates and having power efficient ways of addressing high density arrays of LEDs. This thesis critically evaluates the technology by identifying the key advantages, current limitations and experimentally assessing the technology in in-vivo and in-vitro animal models. For in-vivo testing, the emitted light from a flat OLED panel was directly used to stimulate the neo-cortex in the M1 region of transgenic mice expressing ChR2 (B6.Cg-Tg (Thy1-ChR2/EYFP) 9Gfng/J). An alternative stimulation paradigm using a collimating optical system coupled with an optical fiber was used for stimulating neurons in layer 5 of the motor cortex in the same transgenic mice. EMG activity was recorded from the contralateral vastus lateralis muscles. In vitro testing of the OLEDs was done in primary cortical neurons in culture transfected with blue light sensitive ChR2. The neurons were cultured on a microelectrode array for taking neuronal recordings.

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Date Created
  • 2015

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Transfer of motor learning from a virtual to real task using EEG signals resulting from embodied and abstract thoughts

Description

This research is focused on two separate but related topics. The first uses an electroencephalographic (EEG) brain-computer interface (BCI) to explore the phenomenon of motor learning transfer. The second takes

This research is focused on two separate but related topics. The first uses an electroencephalographic (EEG) brain-computer interface (BCI) to explore the phenomenon of motor learning transfer. The second takes a closer look at the EEG-BCI itself and tests an alternate way of mapping EEG signals into machine commands. We test whether motor learning transfer is more related to use of shared neural structures between imagery and motor execution or to more generalized cognitive factors. Using an EEG-BCI, we train one group of participants to control the movements of a cursor using embodied motor imagery. A second group is trained to control the cursor using abstract motor imagery. A third control group practices moving the cursor using an arm and finger on a touch screen. We hypothesized that if motor learning transfer is related to the use of shared neural structures then the embodied motor imagery group would show more learning transfer than the abstract imaging group. If, on the other hand, motor learning transfer results from more general cognitive processes, then the abstract motor imagery group should also demonstrate motor learning transfer to the manual performance of the same task. Our findings support that motor learning transfer is due to the use of shared neural structures between imaging and motor execution of a task. The abstract group showed no motor learning transfer despite being better at EEG-BCI control than the embodied group. The fact that more participants were able to learn EEG-BCI control using abstract imagery suggests that abstract imagery may be more suitable for EEG-BCIs for some disabilities, while embodied imagery may be more suitable for others. In Part 2, EEG data collected in the above experiment was used to train an artificial neural network (ANN) to map EEG signals to machine commands. We found that our open-source ANN using spectrograms generated from SFFTs is fundamentally different and in some ways superior to Emotiv's proprietary method. Our use of novel combinations of existing technologies along with abstract and embodied imagery facilitates adaptive customization of EEG-BCI control to meet needs of individual users.

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Created

Date Created
  • 2013