Engineering – Page 14 – UROP Spring Symposium 2021

Engineering

C++ Programming for Brain-Computer Interface Calibration Innovations

The UM Direct Brain Interface Laboratory utilizes the classifier program included in the C++ distribution of the BCI2000 v3 to calibrate a P300 BCI to the brain activity of an individual. “BCI” refers to an electroencephalogram (EEG)-based brain-computer interface which allows participants with physical impairments to directly interact with a computer interface using their brains with minimal motor demands. In order to interact with this interface, participants use the P300 component of the event-related brain potential (ERP) (Farwell and Donchin 1988). Though the technology is promising, there are barriers to clinical implementation that the UM-DBI Laboratory aims to address. It is to aid the efficiency and effectiveness of UM-DBI studies that relevant C++ tool and usability additions are proposed. The P300 BCI Classifier calibrates via machine learning and this classifier program has been the point of focus for this project. Through the addition of various practical additions and refinements, the source code for the P300 BCI classifier may be better modified to provide more meaningful output, and allow for a more accessible and functional user interface when assessing output and input. These additions are made through careful coding and testing practice. The general methodology implemented in this research project includes assessing desired changes/additions to be made, understanding the context in which this modification should be implemented, and carefully testing input and output in order to assess adequate functionality (without any unintended consequences). This work is ongoing, and it is intended that all additions will provide added usability and functionality to UM-DBI laboratory researchers.

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Consentful Messaging

In the offline world, people’s communication with others revolves around networks. People tend to communicate more with people they have a strong tie with, which is easy to accomplish in fluid and nuanced ways. However, current social media systems lack such mechanisms for controlling interaction and communication based on network strength, which often leads to massive online harassment and abuse. A major example is Twitter, a social platform well-known for its openness. In this work, we present a system called NetRule, a Chrome extension that augments Twitter and gives users the ability to author network rules to control incoming messages and notifications. Through NetRule, users can easily combine and apply network-based rules on Twitter accounts that initiate interactions, such as whether the number of mutuals is high enough, whether the account has been blocked by one’s following, etc. Users also have the freedom to decide what happens to the accounts flagged by the rules, such as muting, blocking, or visibly coloring the accounts on the interface as a warning. Our evaluation of the system, a field deployment study on Twitter, showed that XXX.

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Sentiment Analysis of International Trade Agreements

The aim was to examine the changes in sentiment that occurred within trade agreements over time using rule-based sentiment analysis. In this case, the sentiment of a text was measured by the degree to which it expresses or implies an opinion. The focus was on a collection of English hundred trade agreements written within the last few decades. Before analysis, a dictionary of trade terms was created using seminal texts. Terms were categorized based on if they expressed a cooperative, punitive, or bureaucratic sentiment. The analysis entailed assigning sentiment scores to trade agreements based on the number of categorized terms within. There is no clear expected result, but there will likely be some observable change in overall sentiment over time, whether it is more or less sentiment.

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Does air-breathing constrain skull function and diversity in fishes?

The anabantoids are a diverse clade of tropical and subtropical freshwater fishes distributed throughout Africa, Asia, and the Indian subcontinent notable for possessing air-breathing organs (ABOs). The ABO allows the anabantoids to breathe outside of water, making it a key innovation: a trait critical to diversification within a particular lineage. While it is unknown if these organs developed independently or from a single ancestral phenotype, fishes with ABOs are dependent on atmospheric oxygen, even drowning without it. The size and arrangement of the ABO influences the skeleton around it (phenotypic integration), and we predict that the ABO will covary in size and shape with skull structure. We visualized skull anatomy with micro-computed tomography (CT) scanning and then used linear morphometrics to capture skull shape variation. By plotting ABO shape changes against skull shape, this study investigated whether fishes with ABOs have more strongly integrated skulls. Using these data, this study will reveal the evolvability of the ABO itself, and the skulls of fishes with or without these organs. We expect: (1) that skull shape will co-vary accordingly with ABO shape and (2) that fishes with ABOs will have less diverse head shapes. Our dataset can also demonstrate how many times ABOs have evolved in anabantoids and whether these complex organs are capable of being lost, perhaps if no longer needed in certain habitats. We found that the ABOs have evolved two to three times independently across anabantarians.

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Reimagining Current Messaging Systems of Social Networks

While social networks have enabled improved communication globally and the widespread Human-Computer Interaction (HCI), it has also exasperated unsolicited communication and harassing messages which consistently target vulnerable, marginalized groups. Consentful Messaging offers Twitter users the ability to filter potential message senders before the risk of receiving unsolicited messages through a system built on Twitter’s API, Python, and JavaScript. The field deployment of Consentful Messaging will reveal how social media users choose to receive messages and to what extent they wish to customize potential message senders. Consentful Messaging is made of four possible customizable functions: the user can set a threshold of number of followers required for a potential message sender, the Consentful Messaging user can verify whether they follow the potential message sender, the Consentful Messaging user can determine whether the potential message sender is followed by at least one account that the Consentful Messaging user follows, and verify whether the Consentful Messaging user has ever replied to a message from the potential message sender. The result is that Consentful Messaging Twitter extension will be deployed in March of 2021. This field study will show how and to what extent Twitter users choose to protect themselves from unsolicited messages with the help of technology. Consentful Messaging will offer not only a customizable layer of protection to marginalized social media users by providing a board for healthy interactions among users, but also information as to how Twitter users interact with and modify personalized computer settings.

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Reimagining current messaging systems on the social internet

While social networks have enabled improved communication globally, it has also exacerbated unsolicited communication and harassing messages which consistently target vulnerable, marginalized groups. Consentful Messaging offers Twitter users the ability to filter potential message senders before the risk of receiving unsolicited messages through a system built on Twitter’s API, Django, MySQL, Python, and JavaScript. The field deployment of Consentful Messaging will reveal how social media users choose to exercise agency over receiving messages and to what extent they wish to customize potential message senders. Consentful Messaging provides the following customizable functions that a Twitter user can use to carve out the network that can initiate interactions: the user can set a threshold of number of followers required for a potential message sender, the system only allows notifications or messages from accounts that the user follows, the user can determine whether the potential message sender is followed by at least one account that the user follows, and verify whether the user has ever replied to a message/tweet from the potential message sender. In short, Consentful Messaging aims to offer a customizable layer of protection to social media users, especially marginalized groups, by providing a board for healthy interactions among users. We plan to deploy the system and conduct a field study sometime in the spring. This field study will help us understand the effectiveness of the approach, and show how and to what extent Twitter users choose to protect themselves from unsolicited messages with the help of technology.

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Consentful Messaging: Giving People Agency over Online Interactions using Network Data

Our project works on developing a Chrome extension that allows Twitter users to have more control over their inbox messages and analyzes other accounts to generate potential warnings. This extension tackles the growing issue of online harassment and unsolicited messages on social media by letting Twitter users apply network-based rules on the messages and notifications. We use coding languages such as HTML, Javascript, CSS, Python, and web framework Django to create the frontend of our extension/website as well as the backend where the server runs. Team members can choose to work on the frontend, backend, or both. We will evaluate the extension by conducting a field deployment study on Twitter. We will recruit active Twitter users and ask them to use the extension for at least two weeks. Once the participants are finished using the system, they will be invited to complete a post-study survey and participate in interviews. There aren’t exactly any results or conclusions at this stage of developing the extension. We are currently still working on different parts of the project and individually working on appropriate functions/code. We plan to provide a fully functional and helpful extension that is able to reduce the issue of online harassment (on the user’s side).

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Image mosaic stitching/blending for large scale neuronal tracing from mouse brain images

Existing knowledge of how brains work is limited. Specifically, there are major gaps in our understanding of the fundamental building blocks and the procedures by which they work together to achieve higher-level brain functions. Modern microscopy techniques permit imaging large brain volume by taking overlapping image tiles with high resolution. This study aims to implement image processing algorithms to stitch such overlapping tiles and blend them into a seamless continuous image. The blended images can then be used to map and functionally reverse engineer the lower-level functions of mice brains. Currently, progress is still being made in the model construction phase. When completed, these models will hopefully allow advanced machine learning algorithms to identify patterns in the neural maps to reverse engineer the brains which has can lead to subsequent breakthroughs in areas like medicine and thought-controlled devices.

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Large scale neuron image storage solution using HDF5 file format

In Neuroscience and Neuro-biomedical research, it is crucial for researchers to understand the connections between different neurons to fully realize the mechanism for the brain’s functionality. We have proposed a light imaging-based approach to resolve neural circuits, that is first gathering Brainbow images from the microscope by scanning brain sample sections, and then neuronal structures, including synaptic connections can be reconstructed from these images. It is crucial to let researchers specifying requests that retrieving the spatial range and spectral channels of a subvolume of the images as the Brainbow image files generated from modern high-throughput microscopes are rather large. This causes bottlenecks not only for the software to deliver its functionality due to lack of memory but also for file transferring via the internet, which all brought the necessity of designing a new data storage system to efficiently store and process such files. Our project introduces a customized back-end infrastructure by storing compressed Brainbow images using the HDF5 file format that enables easy access and modification of subvolume images. We explored the influence of file segmentation size on the read and write performance of the Brainbow images, and the data compression and storing pipeline. Finally, we discuss the possibility of designing a distributed system for large scientific data hosting.

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Developing fast and unbiased computer vision algorithms

Images and videos are a common way to easily store data for research. However, turning these into usable and analyze data is much more time consuming and costly. In order to help research teams save time and money, DEVIATE is developing a computer vision algorithm to label image and video data for them. An essential part of the development process is to consider and mitigate any possible bias. In order to reduce bias, DEVIATE is using human coders to label the training and testing data. In addition, the pool of data represents a diversity of skin tones as well as different light levels. It is especially important to reduce bias since this product will be used by other research groups. If the algorithm was biased, it would affect countless other research studies as well, so it is essential that DEVIATE is able to minimize any harm coming from its product. As this is still an ongoing project, it is expected that the final product will be an unbiased comprehensive computer vision algorithm that can help research teams label data stored in images and video.

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