Electric motor controller using electroencephalograms: A brain-computer interface application

Date of Publication

8-2016

Document Type

Bachelor's Thesis

Degree Name

Bachelor of Science in Electronics and Communications Engineering

Subject Categories

Electrical and Computer Engineering

College

Gokongwei College of Engineering

Department/Unit

Electronics and Communications Engineering

Thesis Adviser

Roy Francis R. Navea

Defense Panel Chair

Alexander C. Abad

Defense Panel Member

Noriel C. Mallari
Mark Lorenze R. Torregoza

Abstract/Summary

Brain-Computer Interface (BCI) is a computer-based system that utilizes brain signals, analyzes them, and interprets them into commands that are transmitted to an output device to perform a preferred action. In this study, a BCI system was developed for electric motor control using artificial neural networks (ANN). Supervised and unsupervised learning methods were explored to train the network. Guided thoughts and unguided thoughts, in the form of an EEG signals, were obtained from the respondents. These thoughts corresponds to a certain switching task to control the electric motor. Four sets of features and switching controls were considered using five EEG bands. High accuracies were obtained using the Gamma band using a supervised learning method. Guided thoughts with 5 switching controls, and 4 features gave better results. Control accuracies varies between off-line and real-time implementations.

Abstract Format

html

Note

Michelle Alipaspas and Joshua Guillermo were under Bachelor of Science in Computer Engineering program

Language

English

Format

Print

Accession Number

TU17030; CDTU017030

Shelf Location

Archives, The Learning Commons, 12F, Henry Sy Sr. Hall

Physical Description

xiv, 164, 25 unnumbered leaves : colored illustrations ; 28 cm. + 1 computer optical disc

Keywords

Electric motors; Electric controllers; Brain-computer interfaces; Neural networks (Computer science)

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4-14-2026

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