Abstract
Event-related potential (ERP) is bioelectrical activity that occurs in the brain in response to specific events or stimuli, reflecting the electrophysiological changes in the brain during cognitive processes. ERP is important in cognitive neuroscience and has been applied to brain-computer interfaces (BCIs). However, because ERP signals collected on the scalp are weak, mixed with spontaneous electroencephalogram (EEG) signals, and their temporal and spatial features are complex, accurate ERP detection is challenging. Compared to traditional neural networks, the capsule network (CapsNet) replaces scalar-output neurons with vector-output capsules, allowing the various input information to be well preserved in the capsules. In this study, we expect to utilize CapsNet to extract the discriminative spatial-temporal features of ERP and encode them in capsules to reduce the loss of valuable information, thereby improving the ERP detection performance for BCI. Therefore, we propose ERP-CapsNet to perform ERP detection in a BCI speller application. The experimental results on BCI Competition datasets and the Akimpech dataset show that ERP-CapsNet achieves better classification performances than do the state-of-the-art techniques. We also use a decoder to investigate the attributes of ERPs encoded in capsules. The results show that ERP-CapsNet relies on the P300 and P100 components to detect ERP. Therefore, ERP-CapsNet not only acts as an outstanding method for ERP detection, but also provides useful insights into the ERP detection mechanism.
Highlights
A BRAIN-COMPUTER interface (BCI) establishes a connection between a brain and a computer or otherManuscript received August 17, 2020; revised January 11, 2021; accepted January 30, 2021
To assess the detection performance, we introduce the following commonly used evaluation measures: true positive (TP), false negative (FN), true negative (TN), and false positive (FP)
In view of the advantages of capsule network (CapsNet), we proposed an Event-related potential (ERP)-CapsNet model for ERP detection in BCI
Summary
A BRAIN-COMPUTER interface (BCI) establishes a connection between a brain and a computer or otherManuscript received August 17, 2020; revised January 11, 2021; accepted January 30, 2021. Ronghua Ma is with the School of Automation Science and Engineering, South China University of Technology, Guangzhou 510641, China. Xiaoli Zhong is with the School of Physics and Optoelectronics, South China University of Technology, Guangzhou 510641, China. BCI systems allow patients with impaired motor function to control external devices using brain signals, thereby improving their quality of life. Event-related potential (ERP) is the type of electroencephalogram (EEG) signal most commonly used in BCI. P300 evoked potential is the most widely used type of ERP signal. It was first described by Sutton in 1965 [2], and named because its positive peak occurred approximately 300 ms after target stimulus onset. One of the most classical oddball paradigms for BCI is the row-column paradigm of the P300 speller proposed by Farwell and Donchin in 1988 [3]
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