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Transactions ID Number: 19-129
Full Name: Inês Oliveira
Position: Ph.D. Candidate
Age: ON
Sex: Female
Address: LaSiGE/FCUL, University of Lisbon, Campo Grande
Country: PORTUGAL
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E-mail address: ines.oliveira@ulusofona.pt
Other E-mails: ogrigore@di.fc.ul.pt, guimaraesn@acm.org
Title of the Paper: EEG Signal Analysis for Silent Visual Reading Classification
Authors as they appear in the Paper: Inês Oliveira, Ovidio Grigori, Nuno Guimarães
Email addresses of all the authors: ines.oliveira@ulusofona.pt, ogrigore@di.fc.ul.pt , guimaraesn@acm.org
Number of paper pages: 8
Abstract: This paper describes a study regarding the detection of silent visual reading mental activity through electroencephalogram (EEG) analysis and processing. Our work is in the context of human computer interaction research field, and we pretend to use EEG signals in applications to assist and analyze reading tasks. The need of users to be constantly and tightly coupled with the applications is being highly stimulated by the design of universally-accessible interactive systems. In this context, the use of biomedical signals has become an emerging area. Visual reading has a great interest to us, since it is a frequent activity while users interact with applications. Users will stop reading whether they feel disturbed or lost, or lose their interest, or even if application visual characteristics (such as font size and color) make it difficult. The analysis of visual reading flow will allow a better understanding of users mind while interacting with applications and hel!
p to objectify some still subjective usability tests. The work focuses on building reliable capture and preprocessing procedures, extracting relevant features and testing simple learning algorithms. The detection process uses left hemisphere EEG signals, which are referred to as being the relevant brain area for this type of tasks. The signals were processed to extract the power spectrum density of delta, theta, and alpha rhythms, known frequencies of this type of signals. We also present two real time demonstration applications of assisted reading.
Keywords: Reading Detection, Electroencephalogram Signal Preprocessing, Feature Extraction, Pattern Recognition, Human Computer Interaction
EXTENSION of the file: .pdf
Special (Invited) Session: Reading Detection Based on Electroencephalogram Processing
Organizer of the Session: 620_644
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