Emotions play an important role in human interaction and decision-making processes. Frontal asymmetry in brain activity is a promising neurophysiological indicator of emotion. Emotions are psychologically explained by the valence-arousal model, but as yet, frontal asymmetry has not been fully explained by this model. In this study, we explored frontal asymmetry of emotions based on the valence-arousal model using the same auditory stimulus. Changes in emotional states using self-report questionnaires were investigated before and after the auditory stimulus. Spectral power and weighted phase lag index were calculated in the delta, theta, alpha, beta, and gamma bands. Phase-amplitude coupling was also measured to explore communication among different frequency bands associated with emotions. After the auditory stimulus, alpha power decreased in both left and right frontal regions and the deltaweighted phase lag index in the left-right regions was increased. However, no frontal asymmetry was identified after the auditory stimulus. Additionally, we explored the brain changes according to the valencearousal model based on emotional states. After the auditory stimulus, frontal asymmetry of alpha power was clearly observed only for negative valence. This finding was possible because subjective emotions were considered despite listening to the same stimulus. Finally, phase-amplitude coupling identified lefthemisphere dominance after the auditory stimulus, regardless of subjective emotions. These results may help us understand frontal asymmetry associated with emotional mechanisms. In addition, these findings can be used directly in the brain-computer interface to improve emotion recognition performance for realworld practical applications. INDEX TERMS Electroencephalogram (EEG), Emotion, Frontal asymmetry, Power spectral density (PSD), Weighted phase lag index (WPLI), Phase-amplitude coupling (PAC).

We present a mobile dataset obtained from electroencephalography (EEG) of the scalp and around the ear as well as from locomotion sensors by 24 participants moving at four different speeds while performing two brain-computer interface (BCI) tasks. The data were collected from 32-channel scalp-EEG, 14-channel ear-EEG, 4-channel electrooculography, and 9-channel inertial measurement units placed at the forehead, left ankle, and right ankle. The recording conditions were as follows: standing, slow walking, fast walking, and slight running at speeds of 0, 0.8, 1.6, and 2.0 m/s, respectively. For each speed, two different BCI paradigms, event-related potential and steady-state visual evoked potential, were recorded. To evaluate the signal quality, scalp- and ear-EEG data were qualitatively and quantitatively validated during each speed. We believe that the dataset will facilitate BCIs in diverse mobile environments to analyze brain activities and evaluate the performance quantitatively for expanding the use of practical BCIs.

The brain-computer interface (BCI) has been investigated as a form of communication tool between the brain and external devices. BCIs have been extended beyond communication and control over the years. The 2020 international BCI competition aimed to provide high-quality neuroscientific data for open access that could be used to evaluate the current degree of technical advances in BCI. Although there are a variety of remaining challenges for future BCI advances, we discuss some of more recent application directions: (i) few-shot EEG learning, (ii) micro-sleep detection (iii) imagined speech decoding, (iv) cross-session classification, and (v) EEG(+ear-EEG) detection in an ambulatory environment. Not only did scientists from the BCI field compete, but scholars with a broad variety of backgrounds and nationalities participated in the competition to address these challenges. Each dataset was prepared and separated into three data that were released to the competitors in the form of training and validation sets followed by a test set. Remarkable BCI advances were identified through the 2020 competition and indicated some trends of interest to BCI researchers.

The assessment of consciousness and unconsciousness is a challenging issue in modern neuroscience. Consciousness is closely related to memory consolidation in that memory is a critical component of conscious experience. So far, many studies have been reported on memory consolidation during consciousness, but there is little research on memory consolidation during unconsciousness. Therefore, we aim to assess the unconsciousness in terms of memory consolidation using electroencephalogram signals. In particular, we used unconscious state during a nap; because sleep is the only state in which consciousness disappears under normal physiological conditions. Seven participants performed two memory tasks (word-pairs and visuo-spatial) before and after the nap to assess the memory consolidation during unconsciousness. As a result, spindle power in central, parietal, occipital regions during unconsciousness was positively correlated with the performance of location memory. With the memory performance, there was also a negative correlation between delta connectivity and word-pairs memory, alpha connectivity and location memory, and spindle connectivity and word-pairs memory. We additionally observed the significant relationship between unconsciousness and brain changes during memory recall before and after the nap. These findings could help present new insights into the assessment of unconsciousness by exploring the relationship with memory consolidation.

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