Using Human Electroencephalography to Determine Word Interpretation via an Artificial Neural Network

Qing Wu; Wenbing Zhao; Tie Qiu

doi:10.1109/SMC.2018.00612

Using Human Electroencephalography to Determine Word Interpretation via an Artificial Neural Network

Qing Wu
, Wenbing Zhao
, Tie Qiu

Electrical and Computer Engineering

Cleveland State University
Tianjin University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

4 Scopus citations

Abstract

In this paper, we report our work on applying an artificial neural network (ANN) to interpret brain wave signals into words. Signals were acquired by a four-channel human electroencephalography (EEG) head set. EEG data were recorded through a video-guided user interface with time stamps. The objective of the experiment is to set up a two-word-based EEG library and to predict a random trail of participants' mental activities, in terms of words. We show that our algorithms can achieve a 90% prediction accuracy of the Bernoulli experiments.

Original language	English
Title of host publication	Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018
Place of Publication	usa
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	3620-3624
Number of pages	5
ISBN (Electronic)	9781538666500
DOIs	https://doi.org/10.1109/SMC.2018.00612
State	Published - Jul 2 2018
Event	2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018 - Miyazaki, Japan Duration: Oct 7 2018 → Oct 10 2018

Conference

Conference	2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018
Country/Territory	Japan
City	Miyazaki
Period	10/7/18 → 10/10/18

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

artificial neural network (ANN)
EEG decoding
electroencephalography (EEG)
human brain wave interpretation
two-word-based EEG library

Access to Document

10.1109/SMC.2018.00612

Cite this

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title = "Using Human Electroencephalography to Determine Word Interpretation via an Artificial Neural Network",

abstract = "In this paper, we report our work on applying an artificial neural network (ANN) to interpret brain wave signals into words. Signals were acquired by a four-channel human electroencephalography (EEG) head set. EEG data were recorded through a video-guided user interface with time stamps. The objective of the experiment is to set up a two-word-based EEG library and to predict a random trail of participants' mental activities, in terms of words. We show that our algorithms can achieve a 90\% prediction accuracy of the Bernoulli experiments.",

keywords = "artificial neural network (ANN), EEG decoding, electroencephalography (EEG), human brain wave interpretation, two-word-based EEG library",

author = "Qing Wu and Wenbing Zhao and Tie Qiu",

year = "2018",

month = jul,

day = "2",

doi = "10.1109/SMC.2018.00612",

language = "English",

pages = "3620--3624",

booktitle = "Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

note = "2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018 ; Conference date: 07-10-2018 Through 10-10-2018",

}

Wu, Q, Zhao, W & Qiu, T 2018, Using Human Electroencephalography to Determine Word Interpretation via an Artificial Neural Network. in Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018., 8616609, Institute of Electrical and Electronics Engineers Inc., usa, pp. 3620-3624, 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018, Miyazaki, Japan, 10/7/18. https://doi.org/10.1109/SMC.2018.00612

Using Human Electroencephalography to Determine Word Interpretation via an Artificial Neural Network. / Wu, Qing; Zhao, Wenbing; Qiu, Tie.
Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018. usa: Institute of Electrical and Electronics Engineers Inc., 2018. p. 3620-3624 8616609.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Using Human Electroencephalography to Determine Word Interpretation via an Artificial Neural Network

AU - Wu, Qing

AU - Zhao, Wenbing

AU - Qiu, Tie

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N2 - In this paper, we report our work on applying an artificial neural network (ANN) to interpret brain wave signals into words. Signals were acquired by a four-channel human electroencephalography (EEG) head set. EEG data were recorded through a video-guided user interface with time stamps. The objective of the experiment is to set up a two-word-based EEG library and to predict a random trail of participants' mental activities, in terms of words. We show that our algorithms can achieve a 90% prediction accuracy of the Bernoulli experiments.

AB - In this paper, we report our work on applying an artificial neural network (ANN) to interpret brain wave signals into words. Signals were acquired by a four-channel human electroencephalography (EEG) head set. EEG data were recorded through a video-guided user interface with time stamps. The objective of the experiment is to set up a two-word-based EEG library and to predict a random trail of participants' mental activities, in terms of words. We show that our algorithms can achieve a 90% prediction accuracy of the Bernoulli experiments.

KW - artificial neural network (ANN)

KW - EEG decoding

KW - electroencephalography (EEG)

KW - human brain wave interpretation

KW - two-word-based EEG library

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U2 - 10.1109/SMC.2018.00612

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M3 - Conference contribution

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EP - 3624

BT - Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018

PB - Institute of Electrical and Electronics Engineers Inc.

CY - usa

T2 - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018

Y2 - 7 October 2018 through 10 October 2018

ER -

Using Human Electroencephalography to Determine Word Interpretation via an Artificial Neural Network

Abstract

Conference

UN SDGs

Keywords

Access to Document

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Cite this