Abstract
Implementing a complex spelling program using a steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) remains a challenge due to difficulties in stimulus presentation and target identification. This study aims to explore the feasibility of mixed frequency and phase coding in building a high-speed SSVEP speller with a computer monitor. A frequency and phase approximation approach was developed to eliminate the limitation of the number of targets caused by the monitor refresh rate, resulting in a speller comprising 32 flickers specified by eight frequencies (8-15 Hz with a 1 Hz interval) and four phases (0°, 90°, 180°, and 270°). A multi-channel approach incorporating Canonical Correlation Analysis (CCA) and SSVEP training data was proposed for target identification. In a simulated online experiment, at a spelling rate of 40 characters per minute, the system obtained an averaged information transfer rate (ITR) of 166.91 bits/min across 13 subjects with a maximum individual ITR of 192.26 bits/min, the highest ITR ever reported in electroencephalogram (EEG)-based BCIs. The results of this study demonstrate great potential of a high-speed SSVEP-based BCI in real-life applications.
| Original language | English |
|---|---|
| Article number | 1450019 |
| Journal | International Journal of Neural Systems |
| Volume | 24 |
| Issue number | 6 |
| DOIs | |
| Publication status | Published - 2014 |
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Keywords
- brain-computer interface
- mixed frequency and phase coding
- speller
- Steady-state visual evoked potential
ASJC Scopus subject areas
- Computer Networks and Communications
- Medicine(all)
Cite this
A high-speed brain speller using steady-state visual evoked potentials. / Nakanishi, Masaki; Wang, Yijun; Wang, Yu Te; Mitsukura, Yasue; Jung, Tzyy Ping.
In: International Journal of Neural Systems, Vol. 24, No. 6, 1450019, 2014.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - A high-speed brain speller using steady-state visual evoked potentials
AU - Nakanishi, Masaki
AU - Wang, Yijun
AU - Wang, Yu Te
AU - Mitsukura, Yasue
AU - Jung, Tzyy Ping
PY - 2014
Y1 - 2014
N2 - Implementing a complex spelling program using a steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) remains a challenge due to difficulties in stimulus presentation and target identification. This study aims to explore the feasibility of mixed frequency and phase coding in building a high-speed SSVEP speller with a computer monitor. A frequency and phase approximation approach was developed to eliminate the limitation of the number of targets caused by the monitor refresh rate, resulting in a speller comprising 32 flickers specified by eight frequencies (8-15 Hz with a 1 Hz interval) and four phases (0°, 90°, 180°, and 270°). A multi-channel approach incorporating Canonical Correlation Analysis (CCA) and SSVEP training data was proposed for target identification. In a simulated online experiment, at a spelling rate of 40 characters per minute, the system obtained an averaged information transfer rate (ITR) of 166.91 bits/min across 13 subjects with a maximum individual ITR of 192.26 bits/min, the highest ITR ever reported in electroencephalogram (EEG)-based BCIs. The results of this study demonstrate great potential of a high-speed SSVEP-based BCI in real-life applications.
AB - Implementing a complex spelling program using a steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) remains a challenge due to difficulties in stimulus presentation and target identification. This study aims to explore the feasibility of mixed frequency and phase coding in building a high-speed SSVEP speller with a computer monitor. A frequency and phase approximation approach was developed to eliminate the limitation of the number of targets caused by the monitor refresh rate, resulting in a speller comprising 32 flickers specified by eight frequencies (8-15 Hz with a 1 Hz interval) and four phases (0°, 90°, 180°, and 270°). A multi-channel approach incorporating Canonical Correlation Analysis (CCA) and SSVEP training data was proposed for target identification. In a simulated online experiment, at a spelling rate of 40 characters per minute, the system obtained an averaged information transfer rate (ITR) of 166.91 bits/min across 13 subjects with a maximum individual ITR of 192.26 bits/min, the highest ITR ever reported in electroencephalogram (EEG)-based BCIs. The results of this study demonstrate great potential of a high-speed SSVEP-based BCI in real-life applications.
KW - brain-computer interface
KW - mixed frequency and phase coding
KW - speller
KW - Steady-state visual evoked potential
UR - http://www.scopus.com/inward/record.url?scp=84905563746&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84905563746&partnerID=8YFLogxK
U2 - 10.1142/S0129065714500191
DO - 10.1142/S0129065714500191
M3 - Article
C2 - 25081427
AN - SCOPUS:84905563746
VL - 24
JO - International Journal of Neural Systems
JF - International Journal of Neural Systems
SN - 0129-0657
IS - 6
M1 - 1450019
ER -