High-Speed Visual Target Identification for Low-Cost Wearable Brain-Computer Interfaces

IEEE Access

Yunseo Ku¹  Dokyun Kim²  Ji-Hoon Kim³  Wooseok Byun⁴ 

[1] Department of Biomedical Engineering, College of Medicine, Chungnam National University, Daejeon, South Korea;Department of Electrical Information Engineering, Seoul National University of Science and Technology, Seoul, South Korea;Department of Electronic and Electrical Engineering, Ewha Womans University, Seoul, South Korea;Department of Electronics Engineering, Chungnam National University, Daejeon, South Korea;

关键词: Brain-computer interface (BCI);    canonical correlation analysis (CCA);    electroencephalogram (EEG);    steady-state visual evoked potential (SSVEP);    target identification;   

DOI  :  10.1109/ACCESS.2019.2912997

来源: DOAJ

【 摘 要 】

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%.

【 授权许可】

Unknown