A brain-computer interface (BCI), which provides an advanced direct human-machine interaction, has gained substantial research interest in the last decade for its great potential in various applications including rehabilitation and communication. Among them, the P300-based BCI speller is a typical application that is capable of identifying the expected stimulated characters. However, the applicability of the P300 speller is hampered for the low recognition rate partially attributed to the complex spatio-temporal characteristics of the EEG signals. Here, we developed a deep-learning analysis framework named ST-CapsNet to overcome the challenges regarding better P300 detection using a capsule network with both spatial and temporal attention modules. Specifically, we first employed spatial and temporal attention modules to obtain Manuscript
Reliability investigation of measures is important in studies of brain science and neuroengineering. Measures' reliability hasn't been investigated across brain states, leaving unknown how reliable the measures are in the context of the change from alert state to fatigue state during driving. To compensate for the lack, we performed a comprehensive investigation.A two-session experiment with an interval of approximately one week was designed to evaluate the reliability of the measures at both sensor and source levels. The results showed that the average intraclass correlation coefficients (ICCs) of the measures at the sensor level were generally higher than those at the source level, except for the directed between-region measures.Single-region measures generally exhibited higher average ICCs relative to between-region measures. The exploration of brain network topology showed that nodal metrics displayed highly varying ICCs across regions and global metrics varied associated with nodal metrics. Single-region measures displayed higher ICCs in the frontal and occipital regions while the between-region measures exhibited higher ICCs in the area involving frontal, central and occipital regions. This study provides an appraisal for the measures' reliability over a long interval, which is informative for measure selection in practical mental monitoring.
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