A High Performance Spelling System based on EEG-EOG Signals with Visual Feedback

Min Ho Lee, John Williamson, Dong Ok Won, Siamac Fazli, Seong Whan Lee

Research output: Contribution to journalArticle

8 Citations (Scopus)

Abstract

In this paper, we propose a highly accurate and fast spelling system that employs multi-modal electroencephalography-electrooculography (EEG-EOG) signals and visual feedback technology. Over the last 20 years, various types of speller systems have been developed in brain-computer interface (BCI) and EOG/eye-tracking research; however, these conventional systems have a tradeoff between the spelling accuracy (or decoding) and typing speed. Healthy users and physically challenged participants, in particular, may become exhausted quickly; thus, there is a need for a speller system with fast typing speed while retaining a high level of spelling accuracy. In this study we propose the first hybrid speller system that combines EEG and EOG signals with visual feedback technology so that the user and the speller system can act cooperatively for optimal decision-making. The proposed spelling system consists of a classic row-column event-related potential (ERP) speller, an EOG command detector, as well as visual feedback modules. First, the online ERP speller calculates classification probabilities for all candidate characters from the EEG epochs. Second, characters are sorted by their probability, and the characters with the highest probabilities are highlighted as visual feedback within the row-column spelling layout. Finally, the user can actively select the character as the target by generating an EOG command. The proposed system shows 97.6% spelling accuracy and an information transfer rate of 39.6 (±13.2) [bits/min] across twenty participants. In our extended experiment, we redesigned the visual feedback and minimized the number of channels (4 channels) in order to enhance the speller performance and increase usability. Most importantly, a new weighted strategy resulted in 100% accuracy and a 57.8 (±23.6) [bits/min] information transfer rate (ITR) across six participants. This study demonstrates that the proposed system can provide a reliable communication channel for practical speller applications and may be used to supplement existing systems.

Original languageEnglish
JournalIEEE Transactions on Neural Systems and Rehabilitation Engineering
DOIs
Publication statusAccepted/In press - 2018 May 19

Fingerprint

Electrooculography
Sensory Feedback
Electroencephalography
Feedback
Evoked Potentials
Technology
Brain-Computer Interfaces
Brain computer interface
Disabled Persons
Hybrid systems
Decoding
Decision Making
Decision making
Communication
Detectors
Research

Keywords

  • Brain-computer interfaces (BCI)
  • electroencephalography (EEG)
  • electrooculogram (EOG)
  • P300 speller
  • visual feedback

ASJC Scopus subject areas

  • Neuroscience(all)
  • Biomedical Engineering
  • Computer Science Applications

Cite this

@article{ba5b849d865742bf983c2d437a5f02d0,
title = "A High Performance Spelling System based on EEG-EOG Signals with Visual Feedback",
abstract = "In this paper, we propose a highly accurate and fast spelling system that employs multi-modal electroencephalography-electrooculography (EEG-EOG) signals and visual feedback technology. Over the last 20 years, various types of speller systems have been developed in brain-computer interface (BCI) and EOG/eye-tracking research; however, these conventional systems have a tradeoff between the spelling accuracy (or decoding) and typing speed. Healthy users and physically challenged participants, in particular, may become exhausted quickly; thus, there is a need for a speller system with fast typing speed while retaining a high level of spelling accuracy. In this study we propose the first hybrid speller system that combines EEG and EOG signals with visual feedback technology so that the user and the speller system can act cooperatively for optimal decision-making. The proposed spelling system consists of a classic row-column event-related potential (ERP) speller, an EOG command detector, as well as visual feedback modules. First, the online ERP speller calculates classification probabilities for all candidate characters from the EEG epochs. Second, characters are sorted by their probability, and the characters with the highest probabilities are highlighted as visual feedback within the row-column spelling layout. Finally, the user can actively select the character as the target by generating an EOG command. The proposed system shows 97.6{\%} spelling accuracy and an information transfer rate of 39.6 (±13.2) [bits/min] across twenty participants. In our extended experiment, we redesigned the visual feedback and minimized the number of channels (4 channels) in order to enhance the speller performance and increase usability. Most importantly, a new weighted strategy resulted in 100{\%} accuracy and a 57.8 (±23.6) [bits/min] information transfer rate (ITR) across six participants. This study demonstrates that the proposed system can provide a reliable communication channel for practical speller applications and may be used to supplement existing systems.",
keywords = "Brain-computer interfaces (BCI), electroencephalography (EEG), electrooculogram (EOG), P300 speller, visual feedback",
author = "Lee, {Min Ho} and John Williamson and Won, {Dong Ok} and Siamac Fazli and Lee, {Seong Whan}",
year = "2018",
month = "5",
day = "19",
doi = "10.1109/TNSRE.2018.2839116",
language = "English",
journal = "IEEE Transactions on Neural Systems and Rehabilitation Engineering",
issn = "1534-4320",
publisher = "Institute of Electrical and Electronics Engineers Inc.",

}

TY - JOUR

T1 - A High Performance Spelling System based on EEG-EOG Signals with Visual Feedback

AU - Lee, Min Ho

AU - Williamson, John

AU - Won, Dong Ok

AU - Fazli, Siamac

AU - Lee, Seong Whan

PY - 2018/5/19

Y1 - 2018/5/19

N2 - In this paper, we propose a highly accurate and fast spelling system that employs multi-modal electroencephalography-electrooculography (EEG-EOG) signals and visual feedback technology. Over the last 20 years, various types of speller systems have been developed in brain-computer interface (BCI) and EOG/eye-tracking research; however, these conventional systems have a tradeoff between the spelling accuracy (or decoding) and typing speed. Healthy users and physically challenged participants, in particular, may become exhausted quickly; thus, there is a need for a speller system with fast typing speed while retaining a high level of spelling accuracy. In this study we propose the first hybrid speller system that combines EEG and EOG signals with visual feedback technology so that the user and the speller system can act cooperatively for optimal decision-making. The proposed spelling system consists of a classic row-column event-related potential (ERP) speller, an EOG command detector, as well as visual feedback modules. First, the online ERP speller calculates classification probabilities for all candidate characters from the EEG epochs. Second, characters are sorted by their probability, and the characters with the highest probabilities are highlighted as visual feedback within the row-column spelling layout. Finally, the user can actively select the character as the target by generating an EOG command. The proposed system shows 97.6% spelling accuracy and an information transfer rate of 39.6 (±13.2) [bits/min] across twenty participants. In our extended experiment, we redesigned the visual feedback and minimized the number of channels (4 channels) in order to enhance the speller performance and increase usability. Most importantly, a new weighted strategy resulted in 100% accuracy and a 57.8 (±23.6) [bits/min] information transfer rate (ITR) across six participants. This study demonstrates that the proposed system can provide a reliable communication channel for practical speller applications and may be used to supplement existing systems.

AB - In this paper, we propose a highly accurate and fast spelling system that employs multi-modal electroencephalography-electrooculography (EEG-EOG) signals and visual feedback technology. Over the last 20 years, various types of speller systems have been developed in brain-computer interface (BCI) and EOG/eye-tracking research; however, these conventional systems have a tradeoff between the spelling accuracy (or decoding) and typing speed. Healthy users and physically challenged participants, in particular, may become exhausted quickly; thus, there is a need for a speller system with fast typing speed while retaining a high level of spelling accuracy. In this study we propose the first hybrid speller system that combines EEG and EOG signals with visual feedback technology so that the user and the speller system can act cooperatively for optimal decision-making. The proposed spelling system consists of a classic row-column event-related potential (ERP) speller, an EOG command detector, as well as visual feedback modules. First, the online ERP speller calculates classification probabilities for all candidate characters from the EEG epochs. Second, characters are sorted by their probability, and the characters with the highest probabilities are highlighted as visual feedback within the row-column spelling layout. Finally, the user can actively select the character as the target by generating an EOG command. The proposed system shows 97.6% spelling accuracy and an information transfer rate of 39.6 (±13.2) [bits/min] across twenty participants. In our extended experiment, we redesigned the visual feedback and minimized the number of channels (4 channels) in order to enhance the speller performance and increase usability. Most importantly, a new weighted strategy resulted in 100% accuracy and a 57.8 (±23.6) [bits/min] information transfer rate (ITR) across six participants. This study demonstrates that the proposed system can provide a reliable communication channel for practical speller applications and may be used to supplement existing systems.

KW - Brain-computer interfaces (BCI)

KW - electroencephalography (EEG)

KW - electrooculogram (EOG)

KW - P300 speller

KW - visual feedback

UR - http://www.scopus.com/inward/record.url?scp=85047203780&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85047203780&partnerID=8YFLogxK

U2 - 10.1109/TNSRE.2018.2839116

DO - 10.1109/TNSRE.2018.2839116

M3 - Article

C2 - 29985154

AN - SCOPUS:85047203780

JO - IEEE Transactions on Neural Systems and Rehabilitation Engineering

JF - IEEE Transactions on Neural Systems and Rehabilitation Engineering

SN - 1534-4320

ER -