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A new Brain-Computer Interface (BCI) technique, which is called a hybrid BCI, has recently been proposed to address the limitations of conventional single BCI system. Although some hybrid BCI studies have shown promising results, the field of hybrid BCI is still in its infancy and there is much to be done. Especially, since the hybrid BCI systems are so complicated and complex, it is difficult to understand the constituent and role of a hybrid BCI system at a glance. Also, the complicated and complex systems make it difficult to evaluate the usability of the systems. We systematically reviewed and analyzed the current state-of-the-art hybrid BCI studies, and proposed a systematic taxonomy for classifying the types of hybrid BCIs with multiple taxonomic criteria. After reviewing 74 journal articles, hybrid BCIs could be categorized with respect to 1) the source of brain signals, 2) the characteristics of the brain signal, and 3) the characteristics of operation in each system. In addition, we exhaustively reviewed recent literature on usability of BCIs. To identify the key evaluation dimensions of usability, we focused on task and measurement characteristics of BCI usability. We classified and summarized 31 BCI usability journal articles according to task characteristics (type and description of task) and measurement characteristics (subjective and objective measures). Afterwards, we proposed usability dimensions for BCI and hybrid BCI systems according to three core-constructs: Satisfaction, effectiveness, and efficiency with recommendations for further research. This paper can help BCI researchers, even those who are new to the field, can easily understand the complex structure of the hybrid systems at a glance. Recommendations for future research can also be helpful in establishing research directions and gaining insight in how to solve ergonomics and HCI design issues surrounding BCI and hybrid BCI systems by usability evaluation.

The demand for public datasets has increased as data-driven methodologies have been introduced in the field of brain-computer interfaces (BCIs). Indeed, many BCI datasets are available in various platforms or repositories on the web, and the studies that have employed these datasets appear to be increasing. Motor imagery is one of the significant control paradigms in the BCI field, and many datasets related to motor tasks are open to the public already. However, to the best of our knowledge, these studies have yet to investigate and evaluate the datasets, although data quality is essential for reliable results and the design of subject− or system-independent BCIs. In this study, we conducted a thorough investigation of motor imagery/execution EEG datasets recorded from healthy participants published over the past 13 years. The 25 datasets were collected from six repositories and subjected to a meta-analysis. In particular, we reviewed the specifications of the recording settings and experimental design, and evaluated the data quality measured by classification accuracy from standard algorithms such as Common Spatial Pattern (CSP) and Linear Discriminant Analysis (LDA) for comparison and compatibility across the datasets. As a result, we found that various stimulation types, such as text, figure, or arrow, were used to instruct subjects what to imagine and the length of each trial also differed, ranging from 2.5 to 29 s with a mean of 9.8 s. Typically, each trial consisted of multiple sections: pre-rest (2.38 s), imagination ready (1.64 s), imagination (4.26 s, ranging from 1 to 10 s), the post-rest (3.38 s). In a meta-analysis of the total of 861 sessions from all datasets, the mean classification accuracy of the two-class (left-hand vs. right-hand motor imagery) problem was 66.53%, and the population of the BCI poor performers, those who are unable to reach proficiency in using a BCI system, was 36.27% according to the estimated accuracy distribution. Further, we analyzed the CSP features and found that each dataset forms a cluster, and some datasets overlap in the feature space, indicating a greater similarity among them. Finally, we checked the minimal essential information (continuous signals, event type/latency, and channel information) that should be included in the datasets for convenient use, and found that only 71% of the datasets met those criteria. Our attempts to evaluate and compare the public datasets are timely, and these results will contribute to understanding the dataset’s quality and recording settings as well as the use of using public datasets for future work on BCIs.

The aim of this study was to conduct a comprehensive review on hyperscanning research (measuring brain activity simultaneously from more than two people interacting) using an explicit systematic method, the preferred reporting items for systematic reviews and meta-analyses (PRISMA). Data were searched from IEEE Xplore, PubMed, Engineering Village, Web of Science and Scopus databases. Inclusion criteria were journal articles written in English from 2000 to 19 June 2019. A total of 126 empirical studies were screened out to address three specific questions regarding the neuroimaging method, the application domain, and the experiment paradigm. Results showed that the most used neuroimaging method with hyperscanning was magnetoencephalography/electroencephalography (MEG/EEG; 47%), and the least used neuroimaging method was hyper-transcranial Alternating Current Stimulation (tACS) (1%). Applications in cognition accounted for almost half the studies (48%), while educational applications accounted for less than 5% of the studies. Applications in decision-making tasks were the second most common (26%), shortly followed by applications in motor synchronization (23%). The findings from this systematic review that were based on documented, transparent and reproducible searches should help build cumulative knowledge and guide future research regarding inter-brain neural synchrony during social interactions, that is, hyperscanning research.

There is debate about whether , the tendency to label emotions in a nuanced and specific manner, is merely a product of labeling abilities, or a systematic difference in the experience of emotion during emotionally evocative events. According to the Conceptual Act Theory of Emotion (CAT) (Barrett, 2006), emotional granularity is due to the latter and is a product of on-going temporal differences in how individuals categorize and thus make meaning of their affective states. To address this question, the present study investigated the effects of individual differences in emotional granularity on electroencephalography-based brain activity during the experience of emotion in response to affective images. Event-related potentials (ERP) and event-related desynchronization and synchronization (ERD/ERS) analysis techniques were used. We found that ERP responses during the very early (60-90 ms), middle (270-300 ms), and later (540-570 ms) moments of stimulus presentation were associated with individuals' level of granularity. We also observed that highly granular individuals, compared to lowly granular individuals, exhibited relatively stable desynchronization of alpha power (8-12 Hz) and synchronization of gamma power (30-50 Hz) during the 3 s of stimulus presentation. Overall, our results suggest that emotional granularity is related to differences in neural processing throughout emotional experiences and that high granularity could be associated with access to executive control resources and a more habitual processing of affective stimuli, or a kind of \"emotional complexity.\" Implications for models of emotion are also discussed.

Alzheimer's disease (AD) affects the whole brain from the cellular level to the entire brain network structure. This study used Dynamic Causal Modeling (DCM) method to assess effective connectivity (EC) and investigate the changes that accompany AD progression. We included the resting-state fMRI data of 34 AD patients, 31 late mild cognitive impairment (LMCI) patients, 34 early MCI (EMCI) patients, and 31 cognitive normal (CN) subjects selected from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Parametric Empirical Bayes (PEB) method was used to infer the effective connectivities and the corresponding probabilities. A linear regression analysis was carried out to test if the connection strengths could predict subjects’ cognitive scores. The results showed that the connections reduced from full connection in the CN group to no connection in the AD group. Statistical analysis showed the connectivity strengths were lower for later stage patients. Linear regression analysis showed that the connection strengths were partially predictive of the cognitive scores. Our results demonstrated the dwindling connectivity accompanying AD progression on causal relationships among brain regions and indicated the potential of EC as a loyal biomarker in AD progression.

This study investigated the interaction between two executive function processes, inhibition and updating, through analyses of behavioral, neurophysiological, and effective connectivity metrics. Although, many studies have focused on behavioral effects of executive function processes individually, few studies have examined the dynamic causal interactions between these two functions. A total of twenty participants from a local university performed a dual task combing flanker and n-back experimental paradigms, and completed the Operation Span Task designed to measure working memory capacity. We found that both behavioral (accuracy and reaction time) and neurophysiological (P300 amplitude and alpha band power) metrics on the inhibition task (i.e., flanker task) were influenced by the updating load (n-back level) and modulated by working memory capacity. Using independent component analysis, source localization (DIPFIT), and Granger Causality analysis of the EEG time-series data, the present study demonstrated that manipulation of cognitive demand in a dual executive function task influenced the causal neural network. We compared connectivity across three updating loads (n-back levels) and found that experimental manipulation of working memory load enhanced causal connectivity of a large-scale neurocognitive network. This network contains the prefrontal and parietal cortices, which are associated with inhibition and updating executive function processes. This study has potential applications in human performance modeling and assessment of mental workload, such as the design of training materials and interfaces for those performing complex multitasking under stress.

Background: Although low-intensity transcranial ultrasound stimulation (LI-TUS) has received more recognition for its neuromodulation potential, there remains a crucial knowledge gap regarding the neuromodulatory effects of LI-TUS and its potential for translation as a therapeutic tool in humans. Objective: In this review, we summarized the findings reported by recently published studies regarding the effect of LI-TUS on neuromodulation in both animals and humans. We also aim to identify challenges and opportunities for the translation process. Methods: A literature search of PubMed, Medline, EMBASE, and Web of Science was performed from January 2019 to June 2020 with the following keywords and Boolean operators: [transcranial ultrasound OR transcranial focused ultrasound OR ultrasound stimulation] AND [neuromodulation]. The methodological quality of the animal studies was assessed by the SYRCLE's risk of bias tool, and the quality of human studies was evaluated by the PEDro score and the NIH quality assessment tool. Results: After applying the inclusion and exclusion criteria, a total of 26 manuscripts (24 animal studies and two human studies) out of 508 reports were included in this systematic review. Although both inhibitory (10 studies) and excitatory (16 studies) effects of LI-TUS were observed in animal studies, only inhibitory effects have been reported in primates (five studies) and human subjects (two studies). The ultrasonic parameters used in animal and human studies are different. The SYRCLE quality score ranged from 25 to 43%, with a majority of the low scores related to performance and detection bias. The two human studies received high PEDro scores (9/10). Conclusion: LI-TUS appears to be capable of targeting both superficial and deep cerebral structures to modulate cognitive or motor behavior in both animals and humans. Further human studies are needed to more precisely define the effective modulation parameters and thereby translate this brain modulatory tool into the clinic.

Brain-computer interface (BCI) has helped people by allowing them to control a computer or machine through brain activity without actual body movement. Despite this advantage, BCI cannot be used widely because some people cannot achieve controllable performance. To solve this problem, researchers have proposed stimulation methods to modulate relevant brain activity to improve BCI performance. However, multiple studies have reported mixed results following stimulation, and the comparative study of different stimulation modalities has been overlooked. Accordingly, this study was designed to compare vibrotactile stimulation and transcranial direct current stimulation’s (tDCS) effects on brain activity modulation and motor imagery BCI performance among inefficient BCI users. We recruited 44 subjects and divided them into sham, vibrotactile stimulation, and tDCS groups, and low performers were selected from each stimulation group. We found that the latter’s BCI performance in the vibrotactile stimulation group increased significantly by 9.13% ( p  < 0.01), and while the tDCS group subjects’ performance increased by 5.13%, it was not significant. In contrast, sham group subjects showed no increased performance. In addition to BCI performance, pre-stimulus alpha band power and the phase locking values (PLVs) averaged over sensory motor areas showed significant increases in low performers following stimulation in the vibrotactile stimulation and tDCS groups, while sham stimulation group subjects and high performers showed no significant stimulation effects across all groups. Our findings suggest that stimulation effects may differ depending upon BCI efficiency, and inefficient BCI users have greater plasticity than efficient BCI users.

Adults with stroke need to perform cognitive-motor dual tasks during their day-to-day activities. However, they face several challenges owing to their impaired motor and cognitive functions. This case-controlled pilot study investigates the speed and accuracy tradeoffs in adults with stroke while performing cognitive-upper limb motor dual tasks. Ten adults with stroke and seven similar-aged controls participated in this study. The participants used a robotic arm for the single motor task and participated in either the serial sevens (S7) or the controlled oral word association test (COWAT) for single-cognitive task. For the dual task, the participants performed the motor and cognitive components simultaneously. Their speed and accuracy were measured for the motor and cognitive tasks, respectively. Two-sample t-statistics indicated that the participants with stroke exhibited a lower motor accuracy in the cross task than in the circle task. The cognitive speed and motor accuracy registered by the subjects with stroke in the dual task significantly decreased. There was a negative linear correlation between motor speed and accuracy in the subjects with stroke when the COWAT task was performed in conjunction with the cross task (ρ = -0.6922, = 0.0388). This study proves the existence of cognitive-upper limb motor interference in adults with stroke while performing dual tasks, based on the observation that their performance during one or both dual tasks deteriorated compared to that during the single task. Both speed and accuracy were complementary parameters that may indicate clinical effectiveness in motor and cognitive outcomes in individuals with stroke.

This study investigated the effect of task demand transitions at multiple levels of analysis including behavioral performance, subjective rating, and brain effective connectivity, while comparing human data to Adaptive Control of Thought-Rational (ACT-R) simulated data. Three stages of task demand were designed and performed sequentially (Low-High-Low) during AF-MATB tasks, and the differences in neural connectivity during workload transition were identified. The NASA Task Load Index (NASA-TLX) and the Instantaneous Self-Assessment (ISA) were used to measure the subjective mental workload that accompanies the hysteresis effect in the task demand transitions. The results found significant hysteresis effects on performance and various brain network measures such as outflow of the prefrontal cortex and connectivity magnitude. These findings would assist in clarifying the direction and strength of the Granger Causality under demand transitions. As a result, these findings involving the neural mechanisms of hysteresis effects in multitasking environments may be utilized in applications of neuroergonomics research. The ability to compare data derived from human participants to data gathered by the ACT-R model allows researchers to better account for hysteresis effects in neuro-cognitive models in the future.