TY - JOUR
T1 - High-speed visual target identification for low-cost wearable brain-computer interfaces
AU - Kim, Dokyun
AU - Byun, Wooseok
AU - Ku, Yunseo
AU - Kim, Ji Hoon
N1 - Funding Information:
This work was supported in part by the National Research Foundation (NRF) of Korea through the Basic Science Research Program, funded by the Ministry of Science, ICT, and Future Planning, under Grant 2015R1D1A1A01060247, and in part by the X-Project through NRF, funded by the Ministry of Science, ICT, and Future Planning, under Grant 2017R1E1A2A02023127.
Publisher Copyright:
© 2013 IEEE.
PY - 2019
Y1 - 2019
N2 - Non-invasive brain-computer interfaces (BCI) have received a great deal of attention due to recent advances in signal processing. Two types of electroencephalograms (EEG), P300 and steady-state visual evoked potential (SSVEP), have been widely used to enable paralyzed patients to communicate with others. Although there have been many signal processing algorithms focusing on target identification accuracies such as power spectral density analysis (PSDA) and canonical correlation analysis (CCA), their high computational complexity drives up the cost of such systems. In the proposed lightweight target identification algorithm, we have focused on developing an improved information transfer rate (ITR) for high-quality communication and reducing overall implementation cost. The proposed algorithm, CCA-Lite, includes two algorithmic optimizations-signal binarization and on-the-fly covariance matrix calculation-which have enabled the development of a low-cost, single-channel, and wearable BCI system using SSVEP. The prototypical BCI system makes use of an ARM Cortex-M3-based low-cost microcontroller unit (MCU), which has been built for 1.5s SSVEP recordings. Compared to the state-of-the-art CCA-based target identification algorithm, CCA-Lite exhibits 25% better ITR and has reduced memory requirements by 92% and single-target identification cycle time by 26%.
AB - Non-invasive brain-computer interfaces (BCI) have received a great deal of attention due to recent advances in signal processing. Two types of electroencephalograms (EEG), P300 and steady-state visual evoked potential (SSVEP), have been widely used to enable paralyzed patients to communicate with others. Although there have been many signal processing algorithms focusing on target identification accuracies such as power spectral density analysis (PSDA) and canonical correlation analysis (CCA), their high computational complexity drives up the cost of such systems. In the proposed lightweight target identification algorithm, we have focused on developing an improved information transfer rate (ITR) for high-quality communication and reducing overall implementation cost. The proposed algorithm, CCA-Lite, includes two algorithmic optimizations-signal binarization and on-the-fly covariance matrix calculation-which have enabled the development of a low-cost, single-channel, and wearable BCI system using SSVEP. The prototypical BCI system makes use of an ARM Cortex-M3-based low-cost microcontroller unit (MCU), which has been built for 1.5s SSVEP recordings. Compared to the state-of-the-art CCA-based target identification algorithm, CCA-Lite exhibits 25% better ITR and has reduced memory requirements by 92% and single-target identification cycle time by 26%.
KW - Brain-computer interface (BCI)
KW - canonical correlation analysis (CCA)
KW - electroencephalogram (EEG)
KW - steady-state visual evoked potential (SSVEP)
KW - target identification
UR - http://www.scopus.com/inward/record.url?scp=85066991256&partnerID=8YFLogxK
U2 - 10.1109/ACCESS.2019.2912997
DO - 10.1109/ACCESS.2019.2912997
M3 - Article
AN - SCOPUS:85066991256
SN - 2169-3536
VL - 7
SP - 55169
EP - 55179
JO - IEEE Access
JF - IEEE Access
M1 - 8698249
ER -