EEG BASED BRAIN COMPUTER INTERFACE APPLICATION OF DETECTING HUMAN GAIT ACTIVITY
Okubo, Ryu
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https://hdl.handle.net/2142/114926
Description
Title
EEG BASED BRAIN COMPUTER INTERFACE APPLICATION OF DETECTING HUMAN GAIT ACTIVITY
Author(s)
Okubo, Ryu
Issue Date
2022-05
Keyword(s)
EEG, gait activity, signal processing, machine learning, brain computer interface
Abstract
Traditionally, Brain-Computer Interface (BCI) study aims to establish or improve the
communication between patients with permanent paralysis and their family or formal caretakers.
Electroencephalogram (EEG) is one of the most common and well-known methods to develop
BCI technology. This is because EEGs are usually cheap, easy to wear, and have a high temporal
resolution. This study investigated the correlation between human gait activity and EEG data.
From past studies, it was known that EEG could be used to detect human locomotor activities,
but whether real time BCI applications can detect precise information (such as if a subject is in
gait activity or not/ which gait stage they are in) is yet to be known. This study aims to establish
a pipeline to detect human gait activity from EEG data with the help of signal processing,
statistical analysis, and machine learning algorithms. The findings from this study may help
establish or improve the current gait rehabilitation model and provide valuable information for
human cortex involvement in gait activity.
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