A CNN-based multi-target fast classification method for AR-SSVEP. (February 2022)

• Record Type: Journal Article
• Authors: Zhao, Xincan; Du, Yulin; Zhang, Rui
• Is Part Of: Computers in biology and medicine. Volume 141(2022)
• Publication Date: 2022-02
• Subjects: Convolutional neural network -- Augmented reality -- Brain–computer interfaces -- Steady-state visual evoked potentials; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2021.105042
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: Because an augmented-reality-based brain-computer interface (AR-BCI) is easily disturbed by external factors, the traditional electroencephalograph (EEG) classification algorithms fail to meet the real-time processing requirements with a large number of stimulus targets or in a real environment. We propose a multi-target fast classification method for augmented-reality-based steady-state visual evoked potential (AR-SSVEP), using a convolutional neural network (CNN). To explore the availability and accuracy of high-efficiency multi-target classification methods in AR-SSVEP with a short stimulation duration, a similar stimulus layout was used for a computer screen (PC) and an optical see-through head-mounted display (OST-HMD) device (HoloLens). The experiment included nine flicker stimuli of different frequencies, and a multi-target fast classification method based on a CNN was constructed to complete nine classification tasks, for which the average accuracy of AR-BCI in our CNN model at 0.5- and 1-s stimulus duration was 67.93 % and 80.83 %, respectively. These results verified the efficacy of the proposed model for processing multi-target classification in AR-BCI. Highlights: Realizing the experimental paradigm containing 9 stimuli in PC and AR. Applying CNN to the multi-target classification task of SSVEP. Improve the classification accuracy of PC-SSVEP and AR-SSVEP under a shorter stimulation duration. Improve the classification accuracy of stimulus targets distributed at the edge in AR-SSVEP.