Brain-to-text communication through an invasive BC

Title: Brain-to-text communication through an invasive BCI Authors: Cesar Lucena Trujillo ([email protected]) Donovan McGregor ([email protected]) Md Abdullah Al Hafiz Khan ([email protected]) Abstract: The purpose of the current study is to investigate the functionality of BCI’s to decode the attempted handwriting thoughts from neural activity in the motor cortex and translates it to text in real time. While there have been past studies regarding the efficacy of BCI’s the practical realization has been proven difficult due to limitations in accuracy and speed. Previous studies have approached this problem by using neural signals to choose from a limited set of possible words, this study seeks to have a more general model that can type any word in the vast English vocabulary. In this study, we create an end-to-end BCI that translates neural signals associated with visualization of the handwriting motion into text output. With this BCI, our study attempts to assist any individual that suffered any brain or physical damage that impedes the function of writing or that it affects the parietal lobes impeding the person of communicating

Brain-to-text communication through a non-invasive BCI

The purpose of the current study is to investigate the functionality of BCI’s to decode the attempted handwriting thoughts from neural activity in the motor cortex and translates it to text in real time. While there have been past studies regarding the efficacy of BCI’s the practical realization has been proven difficult due to limitations in accuracy and speed. Previous studies have approached this problem by using neural signals to choose from a limited set of possible words, this study seeks to have a more general model that can type any word in the vast English vocabulary. In this study, we create an end-to-end BCI that translates neural signals associated with visualization of the handwriting motion into text output. To assess the neural representation of attempted handwriting, participants attempted to handwrite each character one at a time, following the instructions given on a computer screen. The collected data from the EEG signals is challenging to process due to the noise and the similarities between different trials. To target the strategy for visualizing similarity relationships in high-dimensional data is to start by using an autoencoder to compress the data into a low-dimensional space, then use t-SNE for mapping the compressed data to a 2D plane. The TSNE technique that is applied serves to visualize the low-dimensional data. With this BCI, our study attempts to assist any individual that suffered any brain or physical damage that impedes the function of writing or that it affects the parietal lobes impeding the person of communicating

Revolutionizing Healthcare Data Analysis: Semi-Supervised EHR Classification with Transfer Learning

Electronic Health Records are a vital tool in combating the increasing suicide rate among young adults in the United States through providing an insight into the patient’s current mental state. However, given the limited resources available in the mental health industry, there is a need for robust algorithms which can detect and predict suicidal behaviors. Therefore, our research plans are to develop an NLP algorithm which can traverse the dataset, detect instances of suicide attempts or ideation, and provide information regarding the type of suicide

Machine Learning-Enabled Dyslexia Detection from Dytective Gaming Participants Datasets

For our project, we specifically focused on Dyslexia, and how we can organize data of Dyslexic patients and non-Dyslexic patients based on data from previous research papers. We developed a Logistic Regression model, which is a model that processes the data points and their sets as numerical values, and the model will use one of the sets as a dependent variable to compare the rest of the data sets to find a correlation between them. This model organized the data from a study that tested the use of games to help diagnose patients with Dyslexia, and the model was tested on how well it was able to identify Dyslexia from the current data provided from the paper due to the paper labeling each point as Dyslexia or not. The model used 80% of the data from the paper as training to understand the correlation between the independent variables to the dependent variable, while 20% of the data was used to test the accuracy of the model. The model was able to output an accuracy of 78.88% of the data from the testing set, and the missing percent could be attributed to certain points with the data due to some of the patients of the study being marked as possibly having Dyslexia. Thus, the use of Machine Learning techniques has provided great results for diagnosing disorders based on the data that is provided to the models which helps reduce the risk of misdiagnosis