Novel Emerging Molecular Targets in Non-Small Cell Lung Cancer.

In the scenario of systemic treatment for advanced non-small cell lung cancer (NSCLC) patients, one of the most relevant breakthroughs is represented by targeted therapies. Throughout the last years, inhibitors of the epidermal growth factor receptor (EGFR), anaplastic lymphoma kinase (ALK), c-Ros oncogene 1 (ROS1), and V-raf murine sarcoma viral oncogene homolog B (BRAF) have been approved and are currently used in clinical practice. However, other promising molecular drivers are rapidly emerging as therapeutic targets. This review aims to cover the molecular alterations with a potential clinical impact in NSCLC, including amplifications or mutations of the mesenchymal-epithelial transition factor (MET), fusions of rearranged during transfection (RET), rearrangements of the neurotrophic tyrosine kinase (NTRK) genes, mutations of the Kirsten rat sarcoma viral oncogene (KRAS) and phosphatidylinositol-4,5-bisphosphate 3-kinase, catalytic subunit alpha (PIK3CA), as well as amplifications or mutations of human epidermal growth factor receptor 2 (HER2). Additionally, we summarized the current status of targeted agents under investigation for such alterations. This revision of the current literature on emerging molecular targets is needed as the evolving knowledge on novel actionable oncogenic drivers and targeted agents is expected to increase the proportion of patients who will benefit from tailored therapeutic approaches.

Co-Administration of Fendiline Hydrochloride Enhances Chemotherapeutic Efficacy of Cisplatin in Neuroblastoma Treatment.

Despite significant improvement of neuroblastoma (NB) patients' survival due to recent treatment advancements in recent years, NB is still associated with high mortality rate. In search of novel strategies to increase NB's susceptibility to pharmacological treatments, we investigated the in vitro and in vivo effects of fendiline hydrochloride as an enhancer of cisplatin antitumor activity. To assess the modulation of fendiline treatment on cisplatin responses, we used in vitro (evaluating NB cell proliferation by XCELLigence technology and colony formation, and gene expression by RT-PCR) and in vivo (NB cell grafts in NOD-SCID mice) models of NB. NB cell treatment with fendiline induced the expression of the ncRNA NDM29, leading to cell differentiation and to the reduction of the expression of MDRs/ABC transporters linked to multidrug resistance. These events were correlated to higher NB cell susceptibility to cisplatin and, consequently, increased its cytotoxic potency. In vivo, this drug interaction causes an enhanced ability of cisplatin to induce apoptosis in NB masses, resulting in tumor growth reduction and prolonged animal survival rate. Thus, the administration of fendiline might be a possible novel therapeutic approach to increase cisplatin efficacy in aggressive and poorly responsive NB cases.

Somatic mutations in specific and connected subpathways are associated with short neuroblastoma patients' survival and indicate proteins targetable at onset of disease.

Neuroblastoma (NB) is an embryonic malignancy of the sympathetic nervous system with heterogeneous biological, morphological, genetic and clinical characteristics. Although genomic studies revealed the specific biological features of NB pathogenesis useful for new therapeutic approaches, the improvement of high-risk (HR)-NB patients overall survival remains unsatisfactory. To further clarify the biological basis of disease aggressiveness, we used whole-exome sequencing to examine the genomic landscape of HR-NB patients at stage M with short survival (SS) and long survival (LS). Only a few genes, including SMARCA4, SMO, ZNF44 and CHD2, were recurrently and specifically mutated in the SS group, confirming the low recurrence of common mutations in this tumor. A systems biology approach revealed that in the two patient groups, mutations occurred in different pathways. Mutated genes (ARHGEF11, CACNA1G, FGF4, PTPRA, PTK2, ANK3, SMO, NTNG2, VCL and NID2) regulate the MAPK pathway associated with the organization of the extracellular matrix, cell motility through PTK2 signaling and matrix metalloproteinase activity. Moreover, we detected mutations in LAMA2, PTK2, LAMA4, and MMP14 genes, impairing MET signaling, in SFI1 and CHD2 involved in centrosome maturation and chromosome remodeling, in AK7 and SPTLC2, which regulate the metabolism of nucleotides and lipoproteins, and in NALCN, SLC12A1, SLC9A9, which are involved in the transport of small molecules. Notably, connected networks of somatically mutated genes specific for SS patients were identified. The detection of mutated genes present at the onset of disease may help to address an early treatment of HR-NB patients using FDA-approved compounds targeting the deregulated pathways.

Autophagy inhibition improves the cytotoxic effects of receptor tyrosine kinase inhibitors.

BACKGROUND: A growing field of evidence suggests the involvement of oncogenic receptor tyrosine kinases (RTKs) in cell transformation. Deregulated activity of RTKs in tumors can determine disease progression and therapeutic responses in several types of cancer, including neuroblastoma (NB). Therefore, RTKs targeting is a worthwhile challenge for the oncologists. Nevertheless, acquired resistance to RTK inhibitors (RTKi) remains a serious problem. Autophagy activation is among the possible obstacles for good efficacy of the therapy with RTKi. METHODS: Under different treatment conditions we measured autophagic flux using immunoblot and immunofluorescence assays. Death induction was validated by trypan blue exclusion assay and FACS analysis (calcein-AM/propidium iodide). The NB cell lines SH-SY5Y and Kelly were used for the in vitro study. RESULTS: In order to define whether autophagy might be a limiting factor for the efficacy of RTKi in NB cells, we firstly checked its activation following the treatment with several RTKi. Next, we investigated the possibility to increase their therapeutic efficiency by combining RTKi with autophagy blocking agents in vitro. We exploited the effectiveness of three RTKi either alone or in combination with autophagy inhibitors (Chloroquine-CQ and Spautin-1). We demonstrated that autophagy induction was drug-dependent, and that its inhibition increased the anti-tumor activity of a single RTKi unevenly. We observed that the combined use of blocking agents which impair late autophagy events, such as CQ, and RTKi can be more effective with respect to the use of RTKi alone. CONCLUSIONS: In the present report, we assessed the conditions under which autophagy is activated during the use of different RTKi currently in the pre-clinical evaluation for NB. We summarized the achievements of combined RTK/autophagy inhibitors treatment as a promising approach to enhance the efficacy of RTKi in impairing tumor cells viability.

An 18 gene expression-based score classifier predicts the clinical outcome in stage 4 neuroblastoma.

BACKGROUND: The prognosis of children with metastatic stage 4 neuroblastoma (NB) has remained poor in the past decade. PATIENTS AND METHODS: Using microarray analyses of 342 primary tumors, we here developed and validated an easy to use gene expression-based risk score including 18 genes, which can robustly predict the outcome of stage 4 patients. RESULTS: This classifier was a significant predictor of overall survival in two independent validation cohorts [cohort 1 (n = 214): P = 6.3 × 10(-5); cohort 2 (n = 27): P = 3.1 × 10(-2)]. The prognostic value of the risk score was validated by multivariate analysis including the established markers age and MYCN status (P = 0.027). In the pooled validation cohorts (n = 241), integration of the risk score with the age and/or MYCN status identified subgroups with significantly differing overall survival (ranging from 35 to 100 %). CONCLUSION: Together, the 18-gene risk score classifier can identify patients with stage 4 NB with favorable outcome and may therefore improve risk assessment and treatment stratification of NB patients with disseminated disease.

Genetic abnormalities in adolescents and young adults with neuroblastoma: A report from the Italian Neuroblastoma group.

BACKGROUND: Less than 5% of neuroblastomas (NB) occur in adolescents and young adults (AYA), in whom the disease has an indolent and fatal course. PROCEDURE: We studied the genomic profile and histological characteristics of 34 NBs from AYA patients enrolled in the Italian Neuroblastoma Registry (INBR) between 1979 and 2009. RESULTS: Disease was disseminated in 20 patients and localized in 14; 30/34 tumors were classified as NB and 4/34 as nodular ganglioneuroblastoma (nGNB). Segmental Chromosome Aberrations (SCAs) were observed in 29 tumors (85%) namely 1p imbalance (58%), 17q gain (52%), 9p loss (32%), 11q loss (30%), 1q gain (17%), 7q gain (17%), 2p gain (14%), 3p loss (14%), and 4p loss (7%). MYCN amplification and MYCN gain were detected in 3 (10%) and 2 cases (7%) respectively. An anaplastic lymphoma receptor tyrosine kinase (ALK) gene mutation study on the available cases from this cohort revealed 4/25 (16%) mutated cases. In parallel, alpha thalassaemia/mental retardation syndrome X linked (ATRX) gene mutations were also sought, a novel mutation being detected in 1/21 (4,7%) cases. CONCLUSION: This study confirmed the low incidence of MYCN amplification in AYA and recorded a high frequency of 17q gain and 9p and 11q loss independently from the stage of the disease. The presence of 1q gain, which identifies patients with particularly aggressive disease, relapse and poor survival, was also detected. Furthermore, the frequency of ALK mutations suggests that a target-based therapy with ALK inhibitors might be effective in this subset of patients.

Identification of ALK as a major familial neuroblastoma predisposition gene.

Neuroblastoma is a childhood cancer that can be inherited, but the genetic aetiology is largely unknown. Here we show that germline mutations in the anaplastic lymphoma kinase (ALK) gene explain most hereditary neuroblastomas, and that activating mutations can also be somatically acquired. We first identified a significant linkage signal at chromosome bands 2p23-24 using a whole-genome scan in neuroblastoma pedigrees. Resequencing of regional candidate genes identified three separate germline missense mutations in the tyrosine kinase domain of ALK that segregated with the disease in eight separate families. Resequencing in 194 high-risk neuroblastoma samples showed somatically acquired mutations in the tyrosine kinase domain in 12.4% of samples. Nine of the ten mutations map to critical regions of the kinase domain and were predicted, with high probability, to be oncogenic drivers. Mutations resulted in constitutive phosphorylation, and targeted knockdown of ALK messenger RNA resulted in profound inhibition of growth in all cell lines harbouring mutant or amplified ALK, as well as in two out of six wild-type cell lines for ALK. Our results demonstrate that heritable mutations of ALK are the main cause of familial neuroblastoma, and that germline or acquired activation of this cell-surface kinase is a tractable therapeutic target for this lethal paediatric malignancy.

A Comparative Study on Feature Extraction Techniques for the Discrimination of Frontotemporal Dementia and Alzheimer's Disease with Electroencephalography in Resting-State Adults.

Early-stage Alzheimer's disease (AD) and frontotemporal dementia (FTD) share similar symptoms, complicating their diagnosis and the development of specific treatment strategies. Our study evaluated multiple feature extraction techniques for identifying AD and FTD biomarkers from electroencephalographic (EEG) signals. We developed an optimised machine learning architecture that integrates sliding windowing, feature extraction, and supervised learning to distinguish between AD and FTD patients, as well as from healthy controls (HCs). Our model, with a 90% overlap for sliding windowing, SVD entropy for feature extraction, and K-Nearest Neighbors (KNN) for supervised learning, achieved a mean F1-score and accuracy of 93% and 91%, 92.5% and 93%, and 91.5% and 91% for discriminating AD and HC, FTD and HC, and AD and FTD, respectively. The feature importance array, an explainable AI feature, highlighted the brain lobes that contributed to identifying and distinguishing AD and FTD biomarkers. This research introduces a novel framework for detecting and discriminating AD and FTD using EEG signals, addressing the need for accurate early-stage diagnostics. Furthermore, a comparative evaluation of sliding windowing, multiple feature extraction, and machine learning methods on AD/FTD detection and discrimination is documented.

Extracellular vesicles miR-574-5p and miR-181a-5p as prognostic markers in NSCLC patients treated with nivolumab.

Immune checkpoint inhibitors (ICIs) have revolutionized the management of advanced non-small cell lung cancer (NSCLC), although patient survival is still unsatisfactory. Accurate predictive markers capable of personalizing the treatment of patients with NSCLC are still lacking. Circulating extracellular vesicles involved in cell-to-cell communications through miRNAs (EV-miRs) transfer are promising markers. Plasma from 245 patients with advanced NSCLC who received nivolumab as second-line therapy was collected and analyzed. EV-miRnome was profiled on 174/245 patients by microarray platform, and selected EV-miRs were validated by qPCR. A prognostic model combining EV-miR and clinical variables was built using stepwise Cox regression analysis and tested on an independent patient cohort (71/245). EV-PD-L1 gene copy number was assessed by digital PCR. For 54 patients with disease control, EV-miR changes at best response versus baseline were investigated by microarray and validated by qPCR. EV-miRNome profiling at baseline identified two EV-miRs (miR-181a-5p and miR-574-5p) that, combined with performance status, are capable of discriminating patients unlikely from those that are likely to benefit from immunotherapy (median overall survival of 4 months or higher than 9 months, respectively). EV-PD-L1 digital evaluation reported higher baseline copy number in patients at increased risk of mortality, without improving the prognostic score. Best response EV-miRNome profiling selected six deregulated EV-miRs (miR19a-3p, miR-20a-5p, miR-142-3p, miR-1260a, miR-1260b, and miR-5100) in responding patients. Their longitudinal monitoring highlighted a significant downmodulation already in the first treatment cycles, which lasted more than 6 months. Our results demonstrate that EV-miRs are promising prognostic markers for NSCLC patients treated with nivolumab.

Replication of GWAS-identified neuroblastoma risk loci strengthens the role of BARD1 and affirms the cumulative effect of genetic variations on disease susceptibility.

Several neuroblastoma (NB) susceptibility loci have been identified within LINC00340, BARD1, LMO1, DUSP12, HSD17B12, DDX4, IL31RA, HACE1 and LIN28B by genome-wide association (GWA) studies including European American individuals. To validate and comprehensively evaluate the impact of the identified NB variants on disease risk and phenotype, we analyzed 16 single nucleotide polymorphisms (SNPs) in an Italian population (370 cases and 809 controls). We assessed their regulatory activity on gene expression in lymphoblastoid (LCLs) and NB cell lines. We evaluated the cumulative effect of the independent loci on NB risk and high-risk phenotype development in Italian and European American (1627 cases and 2575 controls) populations. All NB susceptibility genes replicated in the Italian dataset except for DDX4 and IL31RA, and the most significant SNP was rs6435862 in BARD1 (P = 8.4 × 10(-15)). BARD1 showed an additional and independent SNP association (rs7585356). This variant influenced BARD1 mRNA expression in LCLs and NB cell lines. No evidence of epistasis among the NB-associated variants was detected, whereas a cumulative effect of risk variants on NB risk (European Americans: P (trend) = 6.9 × 10(-30), Italians: P (trend) = 8.55 × 10(13)) and development of high-risk phenotype (European Americans: P (trend) = 6.9 × 10(-13), Italians: P (trend) = 2.2 × 10(-1)) was observed in a dose-dependent manner. These results provide further evidence that the risk loci identified in GWA studies contribute to NB susceptibility in distinct populations and strengthen the role of BARD1 as major genetic contributor to NB risk. This study shows that even in the absence of interaction the combination of several low-penetrance alleles has potential to distinguish subgroups of patients at different risks of developing NB.

Schizo-Net: A novel Schizophrenia Diagnosis framework using late fusion multimodal deep learning on Electroencephalogram-based Brain connectivity indices.

Schizophrenia (SCZ) is a serious mental condition that causes hallucinations, delusions, and disordered thinking. Traditionally, SCZ diagnosis involves the subject's interview by a skilled psychiatrist. The process needs time and is bound to human errors and bias. Recently, brain connectivity indices have been used in a few pattern recognition methods to discriminate neuro-psychiatric patients from healthy subjects. The study presents Schizo-Net, a novel, highly accurate, and reliable SCZ diagnosis model based on a late multimodal fusion of estimated brain connectivity indices from EEG activity. First, the raw EEG activity is pre-processed exhaustively to remove unwanted artifacts. Next, six brain connectivity indices are estimated from the windowed EEG activity, and six different deep learning architectures (with varying neurons and hidden layers) are trained. The present study is the first which considers a large number of brain connectivity indices, especially for SCZ. A detailed study was also performed that identifies SCZ-related changes occurring in brain connectivity, and the vital significance of BCI is drawn in this regard to identify the biomarkers of the disease. Schizo-Net surpasses current models and achieves 99.84% accuracy. An optimum deep learning architecture selection is also performed for improved classification. The study also establishes that Late fusion technique outperforms single architecture-based prediction in diagnosing SCZ.

How Visual Stimuli Evoked P300 is Transforming the Brain-Computer Interface Landscape: A PRISMA Compliant Systematic Review.

Non-invasive Visual Stimuli evoked-EEG-based P300 BCIs have gained immense attention in recent years due to their ability to help patients with disability using BCI-controlled assistive devices and applications. In addition to the medical field, P300 BCI has applications in entertainment, robotics, and education. The current article systematically reviews 147 articles that were published between 2006-2021*. Articles that pass the pre-defined criteria are included in the study. Further, classification based on their primary focus, including article orientation, participants' age groups, tasks given, databases, the EEG devices used in the studies, classification models, and application domain, is performed. The application-based classification considers a vast horizon, including medical assessment, assistance, diagnosis, applications, robotics, entertainment, etc. The analysis highlights an increasing potential for P300 detection using visual stimuli as a prominent and legitimate research area and demonstrates a significant growth in the research interest in the field of BCI spellers utilizing P300. This expansion was largely driven by the spread of wireless EEG devices, advances in computational intelligence methods, machine learning, neural networks and deep learning.

SB202190 Predicts BRAF-Activating Mutations in Primary Colorectal Cancer Organoids via Erk1-2 Modulation.

The p38 inhibitor SB202190 is a necessary component of the medium used for normal colorectal mucosa cultures. Sato et al. suggested that the primary activity of SB202190 may be EGFR signaling stabilization, causing an increased phosphorylation of Erk1-2 sustaining organoid proliferation. However, the growth of some colorectal cancer (CRC)-derived organoid cultures is inhibited by this molecule via an unknown mechanism. We biochemically investigated SB202190 activity on a collection of 25 primary human CRC organoids, evaluating EGFR, Akt and Erk1-2 activation using Western blot. We found that Erk1-2 phosphorylation was induced by SB202190 in 20 organoid cultures and inhibited in 5 organoid cultures. A next-generation sequencing (NGS) analysis revealed that the inhibition of p-Erk1-2 signaling corresponded to the cultures with BRAF mutations (with four different hits, one being undescribed), while p-Erk1-2 induction was apparently unrelated to other mutations involving the EGFR pathway (Her2, KRAS and NRAS). We found that SB202190 mirrored the biochemical activity of the BRAF inhibitor Dabrafenib, known to induce the paradoxical activation of p-Erk1-2 signaling in BRAF wild-type cells. SB202190 was a more effective inhibitor of BRAF-mutated organoid growth in the long term than the specific BRAF inhibitors Dabrafenib and PLX8394. Overall, SB202190 can predict BRAF-activating mutations in patient-derived organoids, as well as allowing for the identification of new BRAF variants, preceding and enforcing NGS data.

Identification of ALK germline mutation (3605delG) in pediatric anaplastic medulloblastoma.

The anaplastic lymphoma kinase (ALK) gene has been found either rearranged or mutated in several neoplasms such as anaplastic large-cell lymphoma, non-small-cell lung cancer, neuroblastoma and anaplastic thyroid cancer. Medulloblastoma (MB) is an embryonic pediatric cancer arising from nervous system, a tissue in which ALK is expressed during embryonic development. We performed an ALK mutation screening in 52 MBs and we found a novel heterozygous germline deletion of a single base in exon 23 (3605delG) in a case with marked anaplasia. This G deletion results in a frameshift mutation producing a premature stop codon in exon 25 of ALK tyrosine kinase domain. We also screened three human MB cell lines without finding any mutation of ALK gene. Quantitative expression analysis of 16 out of 52 samples showed overexpression of ALK mRNA in three MBs. In the present study, we report the first mutation of ALK found in MB. Moreover, a deletion of ALK gene producing a stop codon has not been detected in human tumors up to now. Further investigations are now required to elucidate whether the truncated form of ALK may have a role in signal transduction.

Genome-wide linkage analysis to identify genetic modifiers of ALK mutation penetrance in familial neuroblastoma.

BACKGROUND: Neuroblastoma (NB) is an important childhood cancer with a strong genetic component related to disease susceptibility. Approximately 1% of NB cases have a positive family history. Following a genome-wide linkage analysis and sequencing of candidate genes in the critical region, we identified ALK as the major familial NB gene. Dominant mutations in ALK are found in more than 50% of familial NB cases. However, in the families used for the linkage study, only about 50% of carriers of ALK mutations are affected by NB. METHODS: To test whether genetic variation may explain the reduced penetrance of the disease phenotype, we analyzed genome-wide genotype data in ALK mutation-positive families using a model-based linkage approach with different liability classes for carriers and non-carriers of ALK mutations. RESULTS: The region with the highest LOD score was located at chromosome 2p23-p24 and included the ALK locus under models of dominant and recessive inheritance. CONCLUSIONS: This finding suggests that variants in the non-mutated ALK gene or another gene linked to it may affect penetrance of the ALK mutations and risk of developing NB in familial cases.

Modeling Cognitive Load as a Self-Supervised Brain Rate with Electroencephalography and Deep Learning.

The principal reason for measuring mental workload is to quantify the cognitive cost of performing tasks to predict human performance. Unfortunately, a method for assessing mental workload that has general applicability does not exist yet. This is due to the abundance of intuitions and several operational definitions from various fields that disagree about the sources or workload, its attributes, the mechanisms to aggregate these into a general model and their impact on human performance. This research built upon these issues and presents a novel method for mental workload modelling from EEG data employing deep learning. This method is self-supervised, employing a continuous brain rate, an index of cognitive activation, and does not require human declarative knowledge. The aim is to induce models automatically from data, supporting replicability, generalisability and applicability across fields and contexts. This specific method is a convolutional recurrent neural network trainable with spatially preserving spectral topographic head-maps from EEG data, aimed at fitting a novel brain rate variable. Findings demonstrate the capacity of the convolutional layers to learn meaningful high-level representations from EEG data since within-subject models had, on average, a test Mean Absolute Percentage Error of around 11%. The addition of a Long-Short Term Memory layer for handling sequences of high-level representations was not significant, although it did improve their accuracy. These findings point to the existence of quasi-stable blocks of automatically learnt high-level representations of cognitive activation because they can be induced through convolution and seem not to be dependent on each other over time, intuitively matching the non-stationary nature of brain responses. Additionally, across-subject models, induced with data from an increasing number of participants, thus trained with data containing more variability, obtained a similar accuracy to the within-subject models. This highlights the potential generalisability of the induced high-level representations across people, suggesting the existence of subject-independent cognitive activation patterns. This research contributes to the body of knowledge by providing scholars with a novel computational method for mental workload modelling that aims to be generally applicable and does not rely on ad hoc human crafted models.

An Evaluation of the EEG Alpha-to-Theta and Theta-to-Alpha Band Ratios as Indexes of Mental Workload.

Many research works indicate that EEG bands, specifically the alpha and theta bands, have been potentially helpful cognitive load indicators. However, minimal research exists to validate this claim. This study aims to assess and analyze the impact of the alpha-to-theta and the theta-to-alpha band ratios on supporting the creation of models capable of discriminating self-reported perceptions of mental workload. A dataset of raw EEG data was utilized in which 48 subjects performed a resting activity and an induced task demanding exercise in the form of a multitasking SIMKAP test. Band ratios were devised from frontal and parietal electrode clusters. Building and model testing was done with high-level independent features from the frequency and temporal domains extracted from the computed ratios over time. Target features for model training were extracted from the subjective ratings collected after resting and task demand activities. Models were built by employing Logistic Regression, Support Vector Machines and Decision Trees and were evaluated with performance measures including accuracy, recall, precision and f1-score. The results indicate high classification accuracy of those models trained with the high-level features extracted from the alpha-to-theta ratios and theta-to-alpha ratios. Preliminary results also show that models trained with logistic regression and support vector machines can accurately classify self-reported perceptions of mental workload. This research contributes to the body of knowledge by demonstrating the richness of the information in the temporal, spectral and statistical domains extracted from the alpha-to-theta and theta-to-alpha EEG band ratios for the discrimination of self-reported perceptions of mental workload.

On the Minimal Amount of EEG Data Required for Learning Distinctive Human Features for Task-Dependent Biometric Applications.

Biometrics is the process of measuring and analyzing human characteristics to verify a given person's identity. Most real-world applications rely on unique human traits such as fingerprints or iris. However, among these unique human characteristics for biometrics, the use of Electroencephalogram (EEG) stands out given its high inter-subject variability. Recent advances in Deep Learning and a deeper understanding of EEG processing methods have led to the development of models that accurately discriminate unique individuals. However, it is still uncertain how much EEG data is required to train such models. This work aims at determining the minimal amount of training data required to develop a robust EEG-based biometric model (+95% and +99% testing accuracies) from a subject for a task-dependent task. This goal is achieved by performing and analyzing 11,780 combinations of training sizes, by employing various neural network-based learning techniques of increasing complexity, and feature extraction methods on the affective EEG-based DEAP dataset. Findings suggest that if Power Spectral Density or Wavelet Energy features are extracted from the artifact-free EEG signal, 1 and 3 s of data per subject is enough to achieve +95% and +99% accuracy, respectively. These findings contributes to the body of knowledge by paving a way for the application of EEG to real-world ecological biometric applications and by demonstrating methods to learn the minimal amount of data required for such applications.

Corrigendum: Human mental workload: A survey and a novel inclusive definition.

[This corrects the article DOI: 10.3389/fpsyg.2022.883321.].

Human Mental Workload: A Survey and a Novel Inclusive Definition.

Human mental workload is arguably the most invoked multidimensional construct in Human Factors and Ergonomics, getting momentum also in Neuroscience and Neuroergonomics. Uncertainties exist in its characterization, motivating the design and development of computational models, thus recently and actively receiving support from the discipline of Computer Science. However, its role in human performance prediction is assured. This work is aimed at providing a synthesis of the current state of the art in human mental workload assessment through considerations, definitions, measurement techniques as well as applications, Findings suggest that, despite an increasing number of associated research works, a single, reliable and generally applicable framework for mental workload research does not yet appear fully established. One reason for this gap is the existence of a wide swath of operational definitions, built upon different theoretical assumptions which are rarely examined collectively. A second reason is that the three main classes of measures, which are self-report, task performance, and physiological indices, have been used in isolation or in pairs, but more rarely in conjunction all together. Multiple definitions complement each another and we propose a novel inclusive definition of mental workload to support the next generation of empirical-based research. Similarly, by comprehensively employing physiological, task-performance, and self-report measures, more robust assessments of mental workload can be achieved.