Academic department under which the project should be listed

CCSE - Computer Science

Faculty Sponsor Name

Md Abdullah Al Hafiz Khan

Abstract (300 words maximum)

Title: Brain-to-text communication through an invasive BCI

Authors:

Cesar Lucena Trujillo (clucenat@students.kennesaw.edu)

Donovan McGregor (dmcgreg3@students.kennesaw.edu)

Md Abdullah Al Hafiz Khan (mkhan74@kennesaw.edu)

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.

Project Type

Poster

How will this be presented?

Yes, in person

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Brain-to-text communication through an invasive BC

Title: Brain-to-text communication through an invasive BCI

Authors:

Cesar Lucena Trujillo (clucenat@students.kennesaw.edu)

Donovan McGregor (dmcgreg3@students.kennesaw.edu)

Md Abdullah Al Hafiz Khan (mkhan74@kennesaw.edu)

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.

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