EEG BASED BRAIN COMPUTER INTERFACE APPLICATION OF DETECTING HUMAN GAIT ACTIVITY

Okubo, Ryu

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https://hdl.handle.net/2142/114926 Copy

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.

Type of Resource

text

Language

en

Handle URL

https://hdl.handle.net/2142/114926

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