NDLI: Event-related modulation of steady-state visual evoked potentials for eyes-closed brain computer interface

Content Provider	IEEE Xplore Digital Library
Author	Nishifuji, S. Sugita, Y. Hirano, H.
Copyright Year	2015
Description	Author affiliation: Strategic Planning & Oper. Dept. for Res. Initiatives, Yamaguchi Univ., Yamaguchi, Japan (Hirano, H.) \|\| Dept. of Electr. & Electron. Eng., Yamaguchi Univ., Ube, Japan (Nishifuji, S.; Sugita, Y.)
Abstract	Brain computer interfaces (BCIs), also be referred to be as brain machine interfaces, transform modulations of electroencephalogram (EEG) into user's intents to communicate with others without voice and physical movement. BCIs have been studied and developed as one of the important means for communication-aid between disabled with severe motor disabilities such as amyotrophic lateral sclerosis and muscular dystrophy patients and their caregivers. State-of-art BCIs have achieved the outstanding performance in information transfer rate and classification accuracy. However, most of conventional BCIs are still unavailable for patients with impaired oculomotor control due to requirement of visual modality. The present study aimed at developing a novel 2-class BCI which was independent of oculomotor control including eye-opening using event-related modulation of steady state visual evoked potential (SSVEP) associated with mental tasks under eyes-closed condition. Eleven healthy subjects aged 21-24 years old were recruited and directed to perform each of two mental tasks under an eyes-closed condition; mental focus on flicker stimuli and image recall of their favorite animals, respectively. The magnitudes of SSVEP in the posterior regions of almost all the subjects were seen to be modulated by performing the mental tasks under the conditions of the flickering frequency of 10 Hz and stimulus intensity of 3-5 lx, which was used to express a user's binary intent, namely, performing one of the mental tasks or not (rest). The classification performance on the mental focus, 80 %, was larger than that on the image recall, 75 %, in average across all the subjects. Shortening of the data length used for classification would improve the information transfer rate of the proposed BCI.
Starting Page	1918
Ending Page	1921
File Size	681044
Page Count	4
File Format	PDF
ISSN	1557170X
e-ISBN	9781424492718
DOI	10.1109/EMBC.2015.7318758
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-08-25
Publisher Place	Italy
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Modulation Electroencephalography Brain-computer interfaces Electrodes Visualization Steady-state Accuracy
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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