Emotions are a critical aspect of human behavior. One widely used technique for research in emotion measurement is based on the use of EEG signals. In general terms, the first step of signal processing is the elimination of noise, which can be done in manual or automatic terms. The next step is determining the feature vector using, for example, entropy calculation and its variations to generate a classification model. It is possible to use this approach to classify theoretical models such as the Circumplex model. This model proposes that emotions are distributed in a two-dimensional circular space. However, methods to determine the feature vector are highly susceptible to noise that may exist in the signal. In this article, a new method to adjust the classifier is proposed using metaheuristics based on the black hole algorithm. The method is aimed at obtaining results similar to those obtained with manual noise elimination methods. In order to evaluate the proposed method, the MAHNOB HCI Tagging Database was used. Results show that using the black hole algorithm to optimize the feature vector of the Support Vector Machine we obtained an accuracy of 92.56% over 30 executions.

<b><i>Background:</i></b> The Montreal Cognitive Assessment (MoCA) is a sensitive screening instrument for mild neurocognitive disorder (mild NCD). However, cut-off scores and accuracy indices should be established using representative samples of the population. In this context, the aim of this study was to update the normative values, and diagnostic efficiency statistics of the MoCA to detect mild NCD in the Chilean population. <b><i>Methods:</i></b> This study included 226 participants from the north, center, and south of the country, classified into 3 groups: healthy elderly (HE; <i>n</i> = 113), mild NCD (<i>n</i> = 65), and major neurocognitive disorder (major NCD; <i>n</i> = 48). <b><i>Results:</i></b> The optimal cut-off score to discriminate mild NCD from HE participants was 20 points with a sensitivity of 82.8% and a specificity of 84.1%. The observed balance between sensitivity and specificity shows a good test performance either to confirm or discard a diagnosis. The cut-off between mild NCD and major NCD from HE participants was 19 points with 85.6% of sensitivity and 90.3% of specificity. <b><i>Conclusion:</i></b> Overall diagnostic accuracy can be considered as outstanding (AUC ≥0.904) when discriminating HE from both mild NCD and major NCD. These results showed that the MoCA is a suitable tool to identify mild NCD and major NCD.

Provision of mental health care is almost entirely built on a singular medium -naturally occurring spoken-language conversations. However, datasets of spoken language from patients experiencing mental health issues are surprisingly difficult to obtain. In this commentary, we discuss some of the reasons behind this, and highlight successful approaches adopted in other areas of clinical linguistics and pose some ways forward, especially for the study of psychosis. Barriers to sharing speech dataAcross disciplines, researchers are rapidly adopting Open Science principles for datasharing. This movement encourages researchers, clinicians, and institutions to provide fully open access to research data, programs, and publications. For example the National Institutes of Health's Strategic Plan for Data Science requires that newlyfunded research projects share data in accord with the FAIR principles [1] for open access and that they include in their budget requests for the resources necessary to complete open access. Although many disciplines, funding agencies, researchers, journals, libraries, and institutions have adopted this new model, the movement has also encountered significant resistance, particularly for open sharing of spoken language data, including spoken language data from clinical populations (SLDCP). We can identify at least six barriers to open sharing of SLDCP [2]. Some of these barriers come from interpretation of regulations by various institutions, while others pertain to the prevailing public perception regarding SLDCP. Here we consider each of these barriers and ways in which systems such as TalkBank [3] or Databrary[4] manage to overcome them. With emerging collaborative efforts to study language in psychosis (e.g., https://discourseinpsychosis.org/), we anticipate the commentary here to eventually inform 'speech bank' infrastructures for psychiatric disorders.1. Informed consent. A frequent objection to the sharing of SLDCP is that it violates participants' rights of privacy and confidentiality. Such usage would be a violation if there had been no informed consent from the participants for sharing of their data -this is unfortunately the case for many existing speech samples from clinical populations, precluding retrospective sharing. In these cases, recontacting participants to obtain consent for data-sharing is an option, if a consent for such re-contact is in place. In the absence of consent to re-contact, IRBs may be able to grant a 'waiver', i.e. modifying the initial consent parameters (see https://conp.ca/ethics-toolkit/). Some national laws also provide alternatives for re-consenting for scientific purposes [5] . Explicitly stating in informed consent forms that the data will be made available to qualified researchers (holding an identifiable position in an academic or research enterprise wherein research activities are governed by a code of conduct on academic integrity), and that it can be removed from a sharing portal if the participant requests removal, will address this barrier. Qualif...