Reading activity recognition using an off-the-shelf eeg - Detecting reading activities and distinguishing genres of documents

Kai Steven Kunze, Yuki Shiga, Shoya Ishimaru, Koichi Kise

Research output: Contribution to journalArticle

13 Citations (Scopus)

Abstract

The document analysis community spends substantial resources towards computer recognition of any type of text (e.g. characters, handwriting, document structure etc.). In this paper, we introduce a new paradigm focusing on recognizing the activities and habits of users while they are reading. We describe the differences to the traditional approaches of document analysis. We present initial work towards recognizing reading activities. We report our initial findings using a commercial, dry electrode Electroencephalography (EEG) system. We show the feasibility to distinguish reading tasks for 3 different document genres with one user and near perfect accuracy. Distinguishing reading tasks for 3 different document types we achieve 97 % with user specific training. We present evidence that reading and non-reading related activities can be separated over 3 users using 6 classes, perfectly separating reading from non-reading. A simple EEG system seems promising for distinguishing the reading of different document genres.

Original languageEnglish
Article number6628592
Pages (from-to)96-100
Number of pages5
JournalUnknown Journal
DOIs
Publication statusPublished - 2013
Externally publishedYes

Fingerprint

Activity Recognition
Electroencephalography
Reading
genre
Document Analysis
Electrodes
Electrode
document analysis
Handwriting
Paradigm
handwriting
Resources
Habits
habits
paradigm
resources
community
evidence

Keywords

  • activity recognition
  • cognitive
  • document analysis
  • EEG
  • pervasive
  • reading

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition

Cite this

Reading activity recognition using an off-the-shelf eeg - Detecting reading activities and distinguishing genres of documents. / Kunze, Kai Steven; Shiga, Yuki; Ishimaru, Shoya; Kise, Koichi.

In: Unknown Journal, 2013, p. 96-100.

Research output: Contribution to journalArticle

@article{fd6d0e6e688d48afaefa00269c5ae1c0,
title = "Reading activity recognition using an off-the-shelf eeg - Detecting reading activities and distinguishing genres of documents",
abstract = "The document analysis community spends substantial resources towards computer recognition of any type of text (e.g. characters, handwriting, document structure etc.). In this paper, we introduce a new paradigm focusing on recognizing the activities and habits of users while they are reading. We describe the differences to the traditional approaches of document analysis. We present initial work towards recognizing reading activities. We report our initial findings using a commercial, dry electrode Electroencephalography (EEG) system. We show the feasibility to distinguish reading tasks for 3 different document genres with one user and near perfect accuracy. Distinguishing reading tasks for 3 different document types we achieve 97 {\%} with user specific training. We present evidence that reading and non-reading related activities can be separated over 3 users using 6 classes, perfectly separating reading from non-reading. A simple EEG system seems promising for distinguishing the reading of different document genres.",
keywords = "activity recognition, cognitive, document analysis, EEG, pervasive, reading",
author = "Kunze, {Kai Steven} and Yuki Shiga and Shoya Ishimaru and Koichi Kise",
year = "2013",
doi = "10.1109/ICDAR.2013.27",
language = "English",
pages = "96--100",
journal = "Mathematical Social Sciences",
issn = "0165-4896",
publisher = "Elsevier",

}

TY - JOUR

T1 - Reading activity recognition using an off-the-shelf eeg - Detecting reading activities and distinguishing genres of documents

AU - Kunze, Kai Steven

AU - Shiga, Yuki

AU - Ishimaru, Shoya

AU - Kise, Koichi

PY - 2013

Y1 - 2013

N2 - The document analysis community spends substantial resources towards computer recognition of any type of text (e.g. characters, handwriting, document structure etc.). In this paper, we introduce a new paradigm focusing on recognizing the activities and habits of users while they are reading. We describe the differences to the traditional approaches of document analysis. We present initial work towards recognizing reading activities. We report our initial findings using a commercial, dry electrode Electroencephalography (EEG) system. We show the feasibility to distinguish reading tasks for 3 different document genres with one user and near perfect accuracy. Distinguishing reading tasks for 3 different document types we achieve 97 % with user specific training. We present evidence that reading and non-reading related activities can be separated over 3 users using 6 classes, perfectly separating reading from non-reading. A simple EEG system seems promising for distinguishing the reading of different document genres.

AB - The document analysis community spends substantial resources towards computer recognition of any type of text (e.g. characters, handwriting, document structure etc.). In this paper, we introduce a new paradigm focusing on recognizing the activities and habits of users while they are reading. We describe the differences to the traditional approaches of document analysis. We present initial work towards recognizing reading activities. We report our initial findings using a commercial, dry electrode Electroencephalography (EEG) system. We show the feasibility to distinguish reading tasks for 3 different document genres with one user and near perfect accuracy. Distinguishing reading tasks for 3 different document types we achieve 97 % with user specific training. We present evidence that reading and non-reading related activities can be separated over 3 users using 6 classes, perfectly separating reading from non-reading. A simple EEG system seems promising for distinguishing the reading of different document genres.

KW - activity recognition

KW - cognitive

KW - document analysis

KW - EEG

KW - pervasive

KW - reading

UR - http://www.scopus.com/inward/record.url?scp=84889563552&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84889563552&partnerID=8YFLogxK

U2 - 10.1109/ICDAR.2013.27

DO - 10.1109/ICDAR.2013.27

M3 - Article

SP - 96

EP - 100

JO - Mathematical Social Sciences

JF - Mathematical Social Sciences

SN - 0165-4896

M1 - 6628592

ER -