NDLI: Enhancing detection of steady-state visual evoked potentials using individual training data

Content Provider	IEEE Xplore Digital Library
Author	Yijun Wang Nakanishi, M. Yu-Te Wang Tzyy-Ping Jung
Copyright Year	2014
Description	Author affiliation: Swartz Center for Comput. Neurosci., Univ. of California, San Diego, La Jolla, CA, USA (Yijun Wang; Yu-Te Wang; Tzyy-Ping Jung) \|\| Grad. Sch. of Sci. & Technol., Keio Univ., Yokohama, Japan (Nakanishi, M.)
Abstract	Although the performance of steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) has improved gradually in the past decades, it still does not meet the requirement of a high communication speed in many applications. A major challenge is the interference of spontaneous background EEG activities in discriminating SSVEPs. An SSVEP BCI using frequency coding typically does not have a calibration procedure since the frequency of SSVEPs can be recognized by power spectrum density analysis (PSDA). However, the detection rate can be deteriorated by the spontaneous EEG activities within the same frequency range because phase information of SSVEPs is ignored in frequency detection. To address this problem, this study proposed to incorporate individual SSVEP training data into canonical correlation analysis (CCA) to improve the frequency detection of SSVEPs. An eight-class SSVEP dataset recorded from 10 subjects in a simulated online BCI experiment was used for performance evaluation. Compared to the standard CCA method, the proposed method obtained significantly improved detection accuracy (95.2% vs. 88.4%, p<;0.05) and information transfer rates (ITR) (104.6 bits/min vs. 89.1 bits/min, p<;0.05). The results suggest that the employment of individual SSVEP training data can significantly improve the detection rate and thereby facilitate the implementation of a high-speed BCI.
Sponsorship	IEEE Eng. Med. Biol. Soc.
Starting Page	3037
Ending Page	3040
File Size	1636987
Page Count	4
File Format	PDF
ISBN	9781424479290
ISSN	1557170X
DOI	10.1109/EMBC.2014.6944263
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-08-26
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Correlation Visualization Electroencephalography Training data Standards Training Brain-computer interfaces
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
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
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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

A dynamic stopping method for improving performance of steady-state visual evoked potential based brain-computer interfaces

Enhancing unsupervised canonical correlation analysis-based frequency detection of SSVEPs by incorporating background EEG

sBCI: Fast detection of steady-state visual evoked potentials

Toward binary brain computer interface using steady-state visually evoked potential under eyes closed condition

Semi-supervised adaptation in ssvep-based brain-computer interface using tri-training

Comparison of steady-state visual and somatosensory evoked potentials for brain-computer interface control

Visual evoked potentials for attentional gating in a brain-computer interface

Common spatial patterns for steady-state somatosensory evoked potentials

Enhancing detection of steady-state visual evoked potentials using individual training data

Similar Documents

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

A dynamic stopping method for improving performance of steady-state visual evoked potential based brain-computer interfaces

Enhancing unsupervised canonical correlation analysis-based frequency detection of SSVEPs by incorporating background EEG

sBCI: Fast detection of steady-state visual evoked potentials

Toward binary brain computer interface using steady-state visually evoked potential under eyes closed condition

Semi-supervised adaptation in ssvep-based brain-computer interface using tri-training

Comparison of steady-state visual and somatosensory evoked potentials for brain-computer interface control

Visual evoked potentials for attentional gating in a brain-computer interface

Common spatial patterns for steady-state somatosensory evoked potentials

Enhancing detection of steady-state visual evoked potentials using individual training data