Research on the Emotions Based on Brain-Computer Technology: A Bibliometric Analysis and Research Agenda

Scarcity of research on psychological or psychiatric states using validated questionnaires in low- and middle-income countries: A ChatGPT-assisted bibliometric analysis and national case study on some psychometric properties

Background

It is vital to assess whether research on psychological or psychiatric states using validated questionnaires is still lagging in low- and middle-income countries and to what degree, and to continue to assess the psychometric properties of the most informative questionnaires.

Methods

We performed a bibliometric analysis of Web of Science Core Collection for all years to determine the number of studies performed in each country that used an inventory or a questionnaire on aggression, anxiety, depression, borderline personality, narcissism, self-harm, shame, or childhood trauma. We conducted a simple observational analysis of distributions by countries to derive the main overall conclusions, assisted by ChatGPT to test its ability to summarise and interpret this type of information. We also carried out a study in Croatia to examine some psychometric properties of five commonly used questionnaires, using Cronbach's α coefficient and zero-order correlations.

Results

We observed a concentration of research activity in a few high-income countries, primarily the United States and several European nations, suggesting a robust research infrastructure and a strong emphasis on studying psychological and psychiatric states within their population. In contrast, low- and middle-income countries were notably under-represented in research on psychological and psychiatric states, although the gap seems to be closing in some countries. Turkey, Iran, Brazil, South Africa, Mexico, India, Malaysia and Pakistan have been consistently contributing an increasing number of studies and catching up with the most research-intensive high-income countries. The national case study in Croatia confirmed adequate psychometric properties of the most frequently used questionnaires.

Conclusions

Addressing research gaps in low- and middle-income countries is crucial, because relying solely on research from high-income countries may not fully capture the nuances of psychological and psychiatric states within diverse populations. To bridge this gap, it is essential to prioritise mental health research in low-resource settings, provide training and resources to local researchers, and establish international collaborations. Such efforts can lead to the development of culturally valid questionnaires, an improved understanding of psychological and psychiatric states in diverse contexts, and the creation of effective interventions to promote mental well-being on a global scale.

What do you have in mind? ERP markers of visual and auditory imagery

This study aimed to investigate the psychophysiological markers of imagery processes through EEG/ERP recordings. Visual and auditory stimuli representing 10 different semantic categories were shown to 30 healthy participants. After a given interval and prompted by a light signal, participants were asked to activate a mental image corresponding to the semantic category for recording synchronized electrical potentials. Unprecedented electrophysiological markers of imagination were recorded in the absence of sensory stimulation. The following peaks were identified at specific scalp sites and latencies, during imagination of infants (centroparietal positivity, CPP, and late CPP), human faces (anterior negativity, AN), animals (anterior positivity, AP), music (P300-like), speech (N400-like), affective vocalizations (P2-like) and sensory (visual vs auditory) modality (PN300). Overall, perception and imagery conditions shared some common electro/cortical markers, but during imagery the category-dependent modulation of ERPs was long latency and more anterior, with respect to the perceptual condition. These ERP markers might be precious tools for BCI systems (pattern recognition, classification, or A.I. algorithms) applied to patients affected by consciousness disorders (e.g., in a vegetative or comatose state) or locked-in-patients (e.g., spinal or SLA patients).

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.

Event-related brain potential markers of visual and auditory perception: A useful tool for brain computer interface systems

Objective

A majority of BCI systems, enabling communication with patients with locked-in syndrome, are based on electroencephalogram (EEG) frequency analysis (e.g., linked to motor imagery) or P300 detection. Only recently, the use of event-related brain potentials (ERPs) has received much attention, especially for face or music recognition, but neuro-engineering research into this new approach has not been carried out yet. The aim of this study was to provide a variety of reliable ERP markers of visual and auditory perception for the development of new and more complex mind-reading systems for reconstructing the mental content from brain activity.

Methods

A total of 30 participants were shown 280 color pictures (adult, infant, and animal faces; human bodies; written words; checkerboards; and objects) and 120 auditory files (speech, music, and affective vocalizations). This paradigm did not involve target selection to avoid artifactual waves linked to decision-making and response preparation (e.g., P300 and motor potentials), masking the neural signature of semantic representation. Overall, 12,000 ERP waveforms × 126 electrode channels (1 million 512,000 ERP waveforms) were processed and artifact-rejected.

Results

Clear and distinct category-dependent markers of perceptual and cognitive processing were identified through statistical analyses, some of which were novel to the literature. Results are discussed from the view of current knowledge of ERP functional properties and with respect to machine learning classification methods previously applied to similar data.

Conclusion

The data showed a high level of accuracy (p ≤ 0.01) in the discriminating the perceptual categories eliciting the various electrical potentials by statistical analyses. Therefore, the ERP markers identified in this study could be significant tools for optimizing BCI systems [pattern recognition or artificial intelligence (AI) algorithms] applied to EEG/ERP signals.

Targeting Frontal Gamma Activity with Neurofeedback to Improve Working Memory in Schizophrenia

Optimal working memory (WM), the mental ability to internally maintain and manipulate task-relevant information, requires coordinated activity of dorsal-lateral prefrontal cortical (DLPFC) neurons. More specifically, during delay periods of tasks with WM features, DLPFC microcircuits generate persistent, stimulus-specific higher-frequency (e.g., gamma) activity. This activity largely depends on recurrent connections between parvalbumin positive inhibitory interneurons and pyramidal neurons in more superficial DLPFC layers. Due to the size and organization of pyramidal neurons (especially apical dendrites), local field potentials generated by DLPFC microcircuits are strong enough to pass outside the skull and can be detected using electroencephalography (EEG). Since patients with schizophrenia (SCZ) exhibit both DLPFC and WM abnormalities, EEG markers of DLPFC microcircuit activity during WM may serve as effective biomarkers or treatment targets. In this review, we summarize converging evidence from primate and human studies for a critical role of DLPFC microcircuit activity during WM and in the pathophysiology of SCZ. We also present a meta-analysis of studies available in PubMed specifically comparing frontal gamma activity between participants with SCZ and healthy controls, to determine whether frontal gamma activity may be a valid biomarker or treatment target for patients with SCZ. We summarize the complex cognitive and neurophysiologic processes contributing to neural oscillations during tasks with WM features, and how such complexity has stalled the development of neurophysiologic biomarkers and treatment targets. Finally, we summarize promising results from early reports using neuromodulation to target DLPFC neural activity and improve cognitive function in participants with SCZ, including a study from our team demonstrating that gamma-EEG neurofeedback increases frontal gamma power and WM performance in participants with SCZ. From the evidence discussed in this review, we believe the emerging field of neuromodulation, which includes extrinsic (electrical or magnetic stimulation) and intrinsic (EEG neurofeedback) modalities, will, in the coming decade, provide promising treatment options targeting specific neurophysiologic properties of specific brain areas to improve cognitive and behavioral health for patients with SCZ.

Big Data in Entrepreneurship

Entrepreneurship research is paying increasing attention to big data. However, there is only a fragmented understanding on how big data influences entrepreneurial activities. To review previous research systematically and quantitatively, the authors use bibliometrics method to analyze 164 research articles on big data in entrepreneurship. They visualize the landscape of these studies, such as publication year, country, and research area. They then use VOSviewer to conduct theme clustering analysis, finding four themes, namely the COVID-19 pandemic and small medium enterprise (SME) digitization, application of big data analytics to decision making, application of big data in platform, and the effects of big data on enterprises. In addition, they construct an integrated framework that integrates the antecedents of big data adoption and influence mechanism of big data on entrepreneurial activities.