Several inaccurate or erroneous conceptions and misleading propaganda about brain-computer interfaces.

Brain-computer interface (BCI) is a revolutionizing human-computer interaction, which has potential applications for specific individuals or groups in specific scenarios. Extensive research has been conducted on the principles and implementation methods of BCI, and efforts are currently being made to bridge the gap from research to real-world applications. However, there are inaccurate or erroneous conceptions about BCI among some members of the public, and certain media outlets, as well as some BCI researchers, developers, manufacturers, and regulators, propagate misleading or overhyped claims about BCI technology. Therefore, this article summarizes the several misconceptions and misleading propaganda about BCI, including BCI being capable of "mind-controlled," "controlling brain," "mind reading," and the ability to "download" or "upload" information from or to the brain using BCI, among others. Finally, the limitations (shortcomings) and limits (boundaries) of BCI, as well as the necessity of conducting research aimed at countering BCI systems are discussed, and several suggestions are offered to reduce misconceptions and misleading claims about BCI.

Alpha neurofeedback training improves visual working memory in healthy individuals.

Neurofeedback (NF) training is a closed-loop brain training in which participants learn to regulate their neural activation. NF training of alpha (8-12 Hz) activity has been reported to enhance working memory capacity, but whether it affects the precision in working memory has not yet been explored. Moreover, whether NF training distinctively influences performance in different types of working memory tasks remains unclear. Therefore, the present study conducted a randomized, single-blind, sham-controlled experiment to investigate how alpha NF training affected the capacity and precision of working memory, as well as the related neural change. Forty participants were randomly and equally assigned to the NF group and the sham control group. Both groups received NF training (about 30 min daily) for five consecutive days. The NF group received alpha (8-12 Hz) training, while the sham control group received sham NF training. We found a significant alpha increase within sessions but no significant difference across sessions. However, the behavioral performance and neural activity in the modified Sternberg task did not show significant change after alpha NF training. On the contrary, the alpha NF training group significantly increased visual working memory capacity measured by the Corsi-block tapping task and improved visual working memory precision in the interference condition in a color-recall task. These results suggest that alpha NF training influences performance in working memory tasks involved in the visuospatial sketchpad. Notably, we demonstrated that alpha NF training improves the quantity and quality of visual working memory.

Emotion Classification Based on Transformer and CNN for EEG Spatial-Temporal Feature Learning.

OBJECTIVES: The temporal and spatial information of electroencephalogram (EEG) signals is crucial for recognizing features in emotion classification models, but it excessively relies on manual feature extraction. The transformer model has the capability of performing automatic feature extraction; however, its potential has not been fully explored in the classification of emotion-related EEG signals. To address these challenges, the present study proposes a novel model based on transformer and convolutional neural networks (TCNN) for EEG spatial-temporal (EEG ST) feature learning to automatic emotion classification. METHODS: The proposed EEG ST-TCNN model utilizes position encoding (PE) and multi-head attention to perceive channel positions and timing information in EEG signals. Two parallel transformer encoders in the model are used to extract spatial and temporal features from emotion-related EEG signals, and a CNN is used to aggregate the EEG's spatial and temporal features, which are subsequently classified using Softmax. RESULTS: The proposed EEG ST-TCNN model achieved an accuracy of 96.67% on the SEED dataset and accuracies of 95.73%, 96.95%, and 96.34% for the arousal-valence, arousal, and valence dimensions, respectively, for the DEAP dataset. CONCLUSIONS: The results demonstrate the effectiveness of the proposed ST-TCNN model, with superior performance in emotion classification compared to recent relevant studies. SIGNIFICANCE: The proposed EEG ST-TCNN model has the potential to be used for EEG-based automatic emotion recognition.

Successful learning of alpha up-regulation through neurofeedback training modulates sustained attention.

As a fundamental attention function, sustained attention plays a critical role in general cognitive abilities and is closely linked to EEG alpha oscillations. Neurofeedback training (NFT) of alpha activity on different aspects of attention has been studied previously. However, it remains unclear how NFT with up- or down-regulation directions modulates sustained attention. Here we employed a counterbalanced single-blind sham-controlled crossover design, in which healthy young adults underwent one NFT session of alpha up-regulation, one NFT session of alpha down-regulation, and one sham-control NFT session over the posterior area. The session order was counterbalanced with a 7-day interval between each session. After each NFT session, the participants completed a visual continuous temporal expectancy task (vCTET) to assess their sustained attention performance. The results showed that compared to sham-control NFT, successful learning of alpha up-regulation resulted in increased reaction time at the beginning of the attention task but a slower increase over vCTET blocks. On the other hand, successful learning of alpha down-regulation had no impact on attention performance compared to sham-control NFT. These findings suggest that successful learning of alpha up-regulation through NFT could impair initial attention performance but slow down visual attention deterioration over time, i.e., alpha enhancement by NFT stabilizing visual attention.

Down-regulation of theta amplitude through neurofeedback improves executive control network efficiency in healthy children.

Despite extensive clinical research on neurofeedback (NF) in attention-deficit/hyperactivity disorder (ADHD), few studies targeted the optimization of attention performance in healthy children. As a crucial component of attention networks, the executive control network, involved in resolving response conflicts and allocating cognitive resources, is closely linked to theta activity. Here, we aimed to answer whether theta down-regulating NF can enhance healthy children's attention performance, especially the executive control network. Sixty children aged 6-12 years were randomly assigned to the NF and waitlist control groups. The NF group received theta down-regulation NF training for five days (a total of 100 mins), and the attention performance of both groups was measured by the attention network test (ANT) in the pre, post-NF, and 7-day follow-up. The electroencephalographic (EEG) results demonstrated a significant decrease in resting-state theta amplitude within sessions. For the behavioral results, the NF group exhibited significant improvements in overall attention performance and the efficiency of the executive control network relative to the control group in the post-NF and follow-up assessment, whereas the alerting and orienting networks remained unchanged. These findings proved the feasibility of theta down-regulating NF and its positive effect on attention in the healthy children population. In particular, the facilitation of the efficiency of the executive control network and the unaltered performance of the other two attention networks in the NF group may support the causality between theta rhythm and the executive control network.

Cross-Domain Identification of Multisite Major Depressive Disorder Using End-to-End Brain Dynamic Attention Network.

Establishing objective and quantitative imaging markers at individual level can assist in accurate diagnosis of Major Depressive Disorder (MDD). However, the clinical heterogeneity of MDD and the shift to multisite data decreased identification accuracy. To address these issues, the Brain Dynamic Attention Network (BDANet) is innovatively proposed, and analyzed bimodal scans from 2055 participants of the Rest-meta-MDD consortium. The end-to-end BDANet contains two crucial components. The Dynamic BrainGraph Generator dynamically focuses and represents topological relationships between Regions of Interest, overcoming limitations of static methods. The Ensemble Classifier is constructed to obfuscate domain sources to achieve inter-domain alignment. Finally, BDANet dynamically generates sample-specific brain graphs by downstream recognition tasks. The proposed BDANet achieved an accuracy of 81.6%. The regions with high attribution for classification were mainly located in the insula, cingulate cortex and auditory cortex. The level of brain connectivity in p24 region was negatively correlated ( [Formula: see text]) with the severity of MDD. Additionally, sex differences in connectivity strength were observed in specific brain regions and functional subnetworks ( [Formula: see text] or [Formula: see text]). These findings based on a large multisite dataset support the conclusion that BDANet can better solve the problem of the clinical heterogeneity of MDD and the shift of multisite data. It also illustrates the potential utility of BDANet for personalized accurate identification, treatment and intervention of MDD.

Successful alpha neurofeedback training enhances working memory updating and event-related potential activity.

Neurofeedback (NF) is a promising method to self-regulate human brain activity for cognition enhancement. Due to the unclear results of alpha NF training on working memory updating as well as the impact of feedback modality on NF learning, this study aimed to understand further the underlying neural mechanism of alpha NF training effects on working memory updating, where the NF learning was also compared between visual and auditory feedback modalities. A total of 30 participants were assigned to Visual NF, Auditory NF, and Control groups. Working memory updating was evaluated by n-back (n =2,3) tasks before and after five alpha upregulation NF sessions. The result showed no significant difference in NF learning performance between the Visual and Auditory groups, indicating that the difference in feedback modality did not affect NF learning. In addition, compared to the control group, the participants who achieved successful NF learning showed a significant increase in n-back behavioral performance and P3a amplitude in 2-back and a significant decrease in P3a latency in 3-back. Our results in n-back further suggested that successful alpha NF training might improve updating performance in terms of the behavioral and related event-related potential (ERP) measures. These findings contribute to the understanding of the effect of alpha training on memory updating and the design of NF experimental protocol in terms of feedback modality selection.

Modulating Individual Alpha Frequency through Short-Term Neurofeedback for Cognitive Enhancement in Healthy Young Adults.

Human alpha oscillation (7-13 Hz) has been extensively studied over the years for its connection with cognition. The individual alpha frequency (IAF), defined as the frequency that provides the highest power in the alpha band, shows a positive correlation with cognitive processes. The modulation of alpha activities has been accomplished through various approaches aimed at improving cognitive performance. However, very few studies focused on the direct modulation of IAF by shifting the peak frequency, and the understanding of IAF modulation remains highly limited. In this study, IAFs of healthy young adults were up-regulated through short-term neurofeedback training using haptic feedback. The results suggest that IAFs have good trainability and are up-regulated, also that IAFs are correlated with the enhanced cognitive performance in mental rotation and n-back tests compared to sham-neurofeedback control. This study demonstrates the feasibility of self-regulating IAF for cognition enhancement and provides potential therapeutic benefits for cognitive-impaired patients.

[Ethics considerations on brain-computer interface technology].

The development and potential application of brain-computer interface (BCI) technology is closely related to the human brain, so that the ethical regulation of BCI has become an important issue attracting the consideration of society. Existing literatures have discussed the ethical norms of BCI technology from the perspectives of non-BCI developers and scientific ethics, while few discussions have been launched from the perspective of BCI developers. Therefore, there is a great need to study and discuss the ethical norms of BCI technology from the perspective of BCI developers. In this paper, we present the user-centered and non-harmful BCI technology ethics, and then discuss and look forward on them. This paper argues that human beings can cope with the ethical issues arising from BCI technology, and as BCI technology develops, its ethical norms will be improved continuously. It is expected that this paper can provide thoughts and references for the formulation of ethical norms related to BCI technology.

[Neurofeedback technology based on functional near infrared spectroscopy imaging and its applications].

Neurofeedback (NF) technology based on electroencephalogram (EEG) data or functional magnetic resonance imaging (fMRI) has been widely studied and applied. In contrast, functional near infrared spectroscopy (fNIRS) has become a new technique in NF research in recent years. fNIRS is a neuroimaging technology based on hemodynamics, which has the advantages of low cost, good portability and high spatial resolution, and is more suitable for use in natural environments. At present, there is a lack of comprehensive review on fNIRS-NF technology (fNIRS-NF) in China. In order to provide a reference for the research of fNIRS-NF technology, this paper first describes the principle, key technologies and applications of fNIRS-NF, and focuses on the application of fNIRS-NF. Finally, the future development trend of fNIRS-NF is prospected and summarized. In conclusion, this paper summarizes fNIRS-NF technology and its application, and concludes that fNIRS-NF technology has potential practicability in neurological diseases and related fields. fNIRS can be used as a good method for NF training. This paper is expected to provide reference information for the development of fNIRS-NF technology.

Research on Top Archer's EEG Microstates and Source Analysis in Different States.

The electroencephalograph (EEG) microstate is a method used to describe the characteristics of the EEG signal through the brain scalp electrode potential's spatial distribution; as such, it reflects the changes in the brain's functional state. The EEGs of 13 elite archers from China's national archery team and 13 expert archers from China's provincial archery team were recorded under the alpha rhythm during the resting state (with closed eyes) and during archery aiming. By analyzing the differences between the EEG microstate parameters and the correlation between these parameters with archery performance, as well as by combining our findings through standardized low-resolution brain electromagnetic tomography source analysis (sLORETA), we explored the changes in the neural activity of professional archers of different levels, under different states. The results of the resting state study demonstrated that the duration, occurrence, and coverage in microstate D of elite archers were significantly higher than those of expert archers and that their other microstates had the greatest probability of transferring to microstate D. During the archery aiming state, the average transition probability of the other microstates transferring to microstate in the left temporal region was the highest observed in the two groups of archers. Moreover, there was a significant negative correlation between the duration and coverage of microstates in the frontal region of elite archers and their archery performance. Our findings indicate that elite archers are more active in the dorsal attention system and demonstrate a higher neural efficiency during the resting state. When aiming, professional archers experience an activation of brain regions associated with archery by suppressing brain regions unrelated to archery tasks. These findings provide a novel theoretical basis for the study of EEG microstate dynamics in archery and related cognitive motor tasks, particularly from the perspective of the subject's mental state.

Alpha/Theta Ratio Neurofeedback Training for Attention Enhancement in Normal Developing Children: A Brief Report.

Attention plays an important role in children's development and learning, and neurofeedback training (NFT) has been proposed as a promising method to improve attention, mainly in population with attention problems such as attention deficit hyperactivity disorder. However, whether this approach has a positive effect on attention in normal developing children has been rarely investigated. This pilot study conducted ten sessions of alpha/theta ratio (ATR) NFT on eight primary students in school environment, with two to three sessions per week. The results showed inter-individual difference in NFT learning efficacy that was assessed by the slope of ATR over training sessions. In addition, the attention performance was significantly improved after NFT. Importantly, the improvement of attention performance was positively correlated with the NFT learning efficacy. It thus highlighted the need for optimizing ATR NFT protocol for the benefits on attention at the individual level. Future work can employ a double-blind placebo-controlled design with larger sample size to validate the benefits of ATR NFT for attention in normal developing children.

Personalized Brain-Computer Interface and Its Applications.

Brain-computer interfaces (BCIs) are a new technology that subverts traditional human-computer interaction, where the control signal source comes directly from the user's brain. When a general BCI is used for practical applications, it is difficult for it to meet the needs of different individuals because of the differences among individual users in physiological and mental states, sensations, perceptions, imageries, cognitive thinking activities, and brain structures and functions. For this reason, it is necessary to customize personalized BCIs for specific users. So far, few studies have elaborated on the key scientific and technical issues involved in personalized BCIs. In this study, we will focus on personalized BCIs, give the definition of personalized BCIs, and detail their design, development, evaluation methods and applications. Finally, the challenges and future directions of personalized BCIs are discussed. It is expected that this study will provide some useful ideas for innovative studies and practical applications of personalized BCIs.

The effects of working memory training in children revealed by behavioral responses and ERP.

BACKGROUND: Recent studies have examined the effect of computerized cognitive training on working memory (WM), but the behavioral and neural effects were uncertain. Also, few studies have explored WM training effects on children using event-related potentials. The purpose of our study was to investigate the effects of WM training in children, including the effects on behavioral performance and neurophysiological outcomes. METHODS: Forty-four healthy children (mean age = 7.76 years, SD = 0.57 years, 18 females) were assigned to the training and control groups. Over 20 training sessions, the training group participated in the computation-span and spatial N-back tasks, whereas the control group joined in normal class activities. They all completed the pre- and post-test evaluation of WM tasks (digit span backwards task and N-back task). RESULTS: The results showed that WM training led to improved performance in the digit span backwards task and 2-back task of post-test evaluation, shortened P3a and P3b latencies in nontarget trials during the spatial 1-back task, shortened P3a latency in target and nontarget trials, as well as increased P3b amplitude and shortened P3b latency in target trials during the spatial 2-back task. CONCLUSIONS: These results suggested that WM training might enhance children's behavioral performance on WM tasks and brought about neurophysiological changes. This study gives insights into the potential of WM training effects on children's behavioral performance and neurophysiological outcomes.

A Review of Neurofeedback Training for Improving Sport Performance From the Perspective of User Experience.

Neurofeedback training (NFT) is a non-invasive, safe, and effective method of regulating the nerve state of the brain. Presently, NFT is widely used to prevent and rehabilitate brain diseases and improve an individual's external performance. Among the various NFT methods, NFT to improve sport performance (SP-NFT) has become an important research and application focus worldwide. Several studies have shown that the method is effective in improving brain function and motor control performance. However, appropriate reviews and prospective directions for this technology are lacking. This paper proposes an SP-NFT classification method based on user experience, classifies and discusses various SP-NFT research schemes reported in the existing literature, and reviews the technical principles, application scenarios, and usage characteristics of different SP-NFT schemes. Several key issues in SP-NFT development, including the factors involved in neural mechanisms, scheme selection, learning basis, and experimental implementation, are discussed. Finally, directions for the future development of SP-NFT, including SP-NFT based on other electroencephalograph characteristics, SP-NFT integrated with other technologies, and SP-NFT commercialization, are suggested. These discussions are expected to provide some valuable ideas to researchers in related fields.

An Exploratory Study of Training Intensity in EEG Neurofeedback.

Neurofeedback training has shown benefits in clinical treatment and behavioral performance enhancement. Despite the wide range of applications, no consensus has been reached about the optimal training schedule. In this work, an EEG neurofeedback practical experiment was conducted aimed at investigating the effects of training intensity on the enhancement of the amplitude in the individual upper alpha band. We designed INTENSIVE and SPARSE training modalities, which differed regarding three essential aspects of training intensity: the number of sessions, the duration of a session, and the interval between sessions. Nine participants in the INTENSIVE group completed 4 sessions with 37.5 minutes each during consecutive days, while nine participants in the SPARSE group performed 6 sessions of 25 minutes spread over approximately 3 weeks. As a result, regarding the short-term effects, the upper alpha band amplitude change within sessions did not significantly differ between the two groups. Nonetheless, only the INTENSIVE group showed a significant increase in the upper alpha band amplitude. However, for the sustained effects across sessions, none of the groups showed significant changes in the upper alpha band amplitude across the whole course of training. The findings suggest that the progression within session is favored by the intensive design. Therefore, based on these findings, it is proposed that training intensity influences EEG self-regulation within sessions. Further investigations are needed to isolate different aspects of training intensity and effectively confirm if one modality globally outperforms the other.

The relationship of resting-state EEG oscillations to executive functions in middle childhood.

Executive functions (EFs) play important roles in children's development, but their neural mechanisms are rarely investigated, especially for the different components of EFs in middle childhood. Therefore, this study aimed to explore the links between resting-state EEG in the frontal scalp region and EFs in children aged 7-9 years. Fifty-nine typically developing children from the second and third grades performed two core EF tasks, i.e., inhibition and working memory, and a high-level EF task, i.e., planning, followed by the recording of EEG signals during eyes-open and eyes-closed resting states. The results showed that distinct EEG activities in the frontal scalp region predicted different EF components. More specifically, after controlling for age and verbal ability, alpha to theta power ratio (ATR) and beta to theta power ratio (BTR) during the eyes-open resting state positively predicted inhibition, and beta to theta power ratio (BTR) during the eyes-open resting state positively predicted planning. However, we did not find any EEG features related to working memory. Our results contributed to the understanding of inter-individual differences in EFs and provided insights into the regulation of corresponding EEG activities through EEG neurofeedback for enhancing children's EFs.

Session Frequency Matters in Neurofeedback Training of Athletes.

Neurofeedback training has been an increasingly used technique and is taking its first steps in sport. Being at an embryonic stage, it is difficult to find consensus regarding the applied methodology to achieve the best results. This study focused on understanding one of the major methodological issues-the training session frequency. The aim of the investigation was to understand if there are differences between performing two sessions or three sessions per week in enhancement of alpha activity and improvement of cognition; and in case there are differences, infer the best protocol. Forty-five athletes were randomly assigned to the three-session-training-per-week group, the two-session-training-per-week group and a control group. The results showed that neurofeedback training with three sessions per week was more effective in increase of alpha amplitude during neurofeedback training than two sessions per week. Furthermore, only the three-session-per-week group showed significant enhancement in N-back and oddball performance after training. The findings suggested more condensed training protocol lead to better outcomes, providing guidance on neurofeedback protocol design in order to optimize training efficacy.

Switching Tinnitus-On: Maps and source localization of spontaneous EEG.

OBJECTIVE: To identify the spectrotemporal changes and sources in patients that could "turn on" tinnitus with multichannel electroencephalography (EEG) system. METHODS: Multichannel EEG was recorded from six patients during the Tinnitus-On and Tinnitus-Off states. The EEG power spectrum and eLORETA-based sources were measured. RESULTS: There was a global increase in delta and theta during Tinnitus-On plus large changes in alpha 1 and alpha 2. During the Tinnitus-On state, many new sources in delta, theta, alpha 1 and gamma bands emerged in the opposite hemisphere in the inferior temporal gyrus (Brodmann area, BA 20), middle temporal gyrus (BA 21), lateral perirhinal cortex (BA 36), ventral entorhinal cortex (BA 28) and anterior pole of the temporal gyrus (BA 38). CONCLUSIONS: The emergence of new delta, theta and gamma band sources in the inferior temporal gyrus (BA 20), middle temporal gyrus (BA 21) and lateral perirhinal cortex (BA 36) plus the appearance of new delta and theta sources in the ventral entorhinal cortex (BA28) and anterior pole of the temporal lobe (BA 38) may comprise a network capable of evoking the phantom sound of tinnitus by simultaneously engaging brain regions involved in memory, sound recognition, and distress which together contribute to tinnitus severity. SIGNIFICANCE: The sudden appearance of new sources of activity in the opposite hemisphere within the inferior temporal gyrus, middle temporal gyrus and perirhinal cortex may initiate the perception of tinnitus perception.

Individual variation in alpha neurofeedback training efficacy predicts pain modulation.

Studies have shown an association between sensorimotor α-oscillation and pain perception. It suggests the potential use of neurofeedback (NFB) training for pain modulation through modifying sensorimotor α-oscillation. Here, a single-session NFB training protocol targeted on increasing sensorimotor α-oscillations was applied to forty-five healthy participants. Pain thresholds to nociceptive laser stimulations and pain ratings (intensity and unpleasantness) to identical laser painful stimulations were assessed immediately before and after NFB training. Participants had larger pain thresholds, but rated the identical painful laser stimulation as more unpleasant after NFB training. These pain measurements were further compared between participants with high or low NFB training efficacy that was quantified as the regression slope of α-oscillation throughout the ten training blocks. A significant increase in pain thresholds was observed among participants with high-efficacy; whereas a significant increase in pain ratings was observed among participants with low-efficacy. These results suggested that NFB training decreased the sensory-discriminative aspect of pain, but increased the affective-motivational aspect of pain, whereas both pain modulations were dependent upon the NFB training efficacy. Importantly, correlation analysis across all participants revealed that a greater NFB training efficacy predicted a greater increase in pain thresholds particularly at hand contralateral to NFB target site, but no significant correlation was observed between NFB training efficacy and modulation on pain ratings. It thus provided causal evidence for a link between sensorimotor α-oscillation and the sensory-discriminative aspect of pain, and highlighted the need for personalized neurofeedback for the benefits on pain modulation at the individual level. Future studies can adopt a double-blind sham-controlled protocol to validate NFB training induced pain modulation.