Assessment of high-frequency steady-state visual evoked potentials from below-the-hairline areas for a brain-computer interface based on Depth-of-Field

Background and Objective: Recently, a promising Brain-Computer Interface based on Steady-State Visual Evoked Potential (SSVEP-BCI) was proposed, which composed of two stimuli presented together in the center of the subject's field of view, but at different depth planes (Depth-of-Field setup). Thus, users were easily able to select one of them by shifting their eye focus. However, in that work, EEG signals were collected through electrodes placed on occipital and parietal regions (hair-covered areas), which demanded a long preparation time. Also, that work used low-frequency stimuli, which can produce visual fatigue and increase the risk of photosensitive epileptic seizures. In order to improve the practicality and visual comfort, this work proposes a BCI based on Depth-of-Field using the high-frequency SSVEP response measured from below-the-hairline areas (behind-the-ears). Methods: Two high-frequency stimuli (31 Hz and 32 Hz) were used in a Depth-of-Field setup to study the SSVEP response from behind-the-ears (TP9 and TP10). Multivariate Spectral F-test (MSFT) method was used to verify the elicited response. Afterwards, a BCI was proposed to command a mobile robot in a virtual reality environment. The commands were recognized through Temporally Local Multivariate Synchronization Index (TMSI) method. Results: The data analysis reveal that the focused stimuli elicit distinguishable SSVEP response when measured from hairless areas, in spite of the fact that the non-focused stimulus is also present in the field of view. Also, our BCI shows a satisfactory result, reaching average accuracy of 91.6% and Information Transfer Rate (ITR) of 5.3 bits/min. Conclusion: These findings contribute to the development of more safe and practical BCI.Fil: Floriano, Alan. Universidade Federal do Espírito Santo; BrasilFil: Delisle Rodriguez, Denis. Universidade Federal do Espírito Santo; BrasilFil: Diez, Pablo Federico. Universidad Nacional de San Juan. Facultad de Ingeniería. Departamento de Electrónica y Automática. Gabinete de Tecnología Médica; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan; ArgentinaFil: Bastos Filho, Teodiano Freire. Universidade Federal do Espírito Santo; Brasi

Shallow convolutional network excel for classifying motor imagery EEG in BCI applications

Many studies applying Brain-Computer Interfaces (BCIs) based on Motor Imagery (MI) tasks for rehabilitation have demonstrated the important role of detecting the Event-Related Desynchronization (ERD) to recognize the user’s motor intention. Nowadays, the development of MI-based BCI approaches without or with very few calibration stages session-by-session for different days or weeks is still an open and emergent scope. In this work, a new scheme is proposed by applying Convolutional Neural Networks (CNN) for MI classification, using an end-to-end Shallow architecture that contains two convolutional layers for temporal and spatial feature extraction. We hypothesize that a BCI designed for capturing event-related desynchronization/synchronization (ERD/ERS) at the CNN input, with an adequate network design, may enhance the MI classification with fewer calibration stages. The proposed system using the same architecture was tested on three public datasets through multiple experiments, including both subject-specific and non-subject-specific training. Comparable and also superior results with respect to the state-of-the-art were obtained. On subjects whose EEG data were never used in the training process, our scheme also achieved promising results with respect to existing non-subject-specific BCIs, which shows greater progress in facilitating clinical applications

Monte Carlo dropout for uncertainty estimation and motor imagery classification

Motor Imagery (MI)-based Brain–Computer Interfaces (BCIs) have been widely used as an alternative communication channel to patients with severe motor disabilities, achieving high classification accuracy through machine learning techniques. Recently, deep learning techniques have spotlighted the state-of-the-art of MI-based BCIs. These techniques still lack strategies to quantify predictive uncertainty and may produce overconfident predictions. In this work, methods to enhance the performance of existing MI-based BCIs are proposed in order to obtain a more reliable system for real application scenarios. First, the Monte Carlo dropout (MCD) method is proposed on MI deep neural models to improve classification and provide uncertainty estimation. This approach was implemented using Shallow Convolutional Neural Network (SCNN-MCD) and with an ensemble model (E-SCNN-MCD). As another contribution, to discriminate MI task predictions of high uncertainty, a threshold approach is introduced and tested for both SCNN-MCD and E-SCNN-MCD approaches. The BCI Competition IV Databases 2a and 2b were used to evaluate the proposed methods for both subject-specific and non-subject-specific strategies, obtaining encouraging results for MI recognition

The HoxD cluster is a dynamic and resilient TAD boundary controlling the segregation of antagonistic regulatory landscapes

The mammalian HoxD cluster lies between two topologically associating domains (TADs) matching distinct enhancer-rich regulatory landscapes. During limb development, the telomeric TAD controls the early transcription of Hoxd genes in forearm cells, whereas the centromeric TAD subsequently regulates more posterior Hoxd genes in digit cells. Therefore, the TAD boundary prevents the terminal Hoxd13 gene from responding to forearm enhancers, thereby allowing proper limb patterning. To assess the nature and function of this CTCF-rich DNA region in embryos, we compared chromatin interaction profiles between proximal and distal limb bud cells isolated from mutant stocks where various parts of this boundary region were removed. The resulting progressive release in boundary effect triggered inter-TAD contacts, favored by the activity of the newly accessed enhancers. However, the boundary was highly resilient, and only a 400-kb deletion, including the whole-gene cluster, was eventually able to merge the neighboring TADs into a single structure. In this unified TAD, both proximal and distal limb enhancers nevertheless continued to work independently over a targeted transgenic reporter construct. We propose that the whole HoxD cluster is a dynamic TAD border and that the exact boundary position varies depending on both the transcriptional status and the developmental context. Press

Emotion analysis in children through facial emissivity of infrared thermal imaging.

Physiological signals may be used as objective markers to identify emotions, which play relevant roles in social and daily life. To measure these signals, the use of contact-free techniques, such as Infrared Thermal Imaging (IRTI), is indispensable to individuals who have sensory sensitivity. The goal of this study is to propose an experimental design to analyze five emotions (disgust, fear, happiness, sadness and surprise) from facial thermal images of typically developing (TD) children aged 7-11 years using emissivity variation, as recorded by IRTI. For the emotion analysis, a dataset considered emotional dimensions (valence and arousal), facial bilateral sides and emotion classification accuracy. The results evidence the efficiency of the experimental design with interesting findings, such as the correlation between the valence and the thermal decrement in nose; disgust and happiness as potent triggers of facial emissivity variations; and significant emissivity variations in nose, cheeks and periorbital regions associated with different emotions. Moreover, facial thermal asymmetry was revealed with a distinct thermal tendency in the cheeks, and classification accuracy reached a mean value greater than 85%. From the results, the emissivity variations were an efficient marker to analyze emotions in facial thermal images, and IRTI was confirmed to be an outstanding technique to study emotions. This study contributes a robust dataset to analyze the emotions of 7-11-year-old TD children, an age range for which there is a gap in the literature

Impact of genome architecture on the functional activation and repression of Hox regulatory landscapes

Background The spatial organization of the mammalian genome relies upon the formation of chromatin domains of various scales. At the level of gene regulation in cis, collections of enhancer sequences define large regulatory landscapes that usually match with the presence of topologically associating domains (TADs). These domains often contain ranges of enhancers displaying similar or related tissue specificity, suggesting that in some cases, such domains may act as coherent regulatory units, with a global on or off state. By using the HoxD gene cluster, which specifies the topology of the developing limbs via highly orchestrated regulation of gene expression, as a paradigm, we investigated how the arrangement of regulatory domains determines their activity and function.ResultsProximal and distal cells in the developing limb express different levels of Hoxd genes, regulated by flanking 3 and 5 ' TADs, respectively. We characterized the effect of large genomic rearrangements affecting these two TADs, including their fusion into a single chromatin domain. We show that, within a single hybrid TAD, the activation of both proximal and distal limb enhancers globally occurred as when both TADs are intact. However, the activity of the 3 ' TAD in distal cells is generally increased in the fused TAD, when compared to wild type where it is silenced. Also, target gene activity in distal cells depends on whether or not these genes had previously responded to proximal enhancers, which determines the presence or absence of H3K27me3 marks. We also show that the polycomb repressive complex 2 is mainly recruited at the Hox gene cluster and can extend its coverage to far-cis regulatory sequences as long as confined to the neighboring TAD structure.Conclusions We conclude that antagonistic limb proximal and distal enhancers can exert their specific effects when positioned into the same TAD and in the absence of their genuine target genes. We also conclude that removing these target genes reduced the coverage of a regulatory landscape by chromatin marks associated with silencing, which correlates with its prolonged activity in time

Knee Impedance Modulation to Control an Active Orthosis Using Insole Sensors

Robotic devices for rehabilitation and gait assistance have greatly advanced with the objective of improving both the mobility and quality of life of people with motion impairments. To encourage active participation of the user, the use of admittance control strategy is one of the most appropriate approaches, which requires methods for online adjustment of impedance components. Such approach is cited by the literature as a challenge to guaranteeing a suitable dynamic performance. This work proposes a method for online knee impedance modulation, which generates variable gains through the gait cycle according to the users’ anthropometric data and gait sub-phases recognized with footswitch signals. This approach was evaluated in an active knee orthosis with three variable gain patterns to obtain a suitable condition to implement a stance controller: two different gain patterns to support the knee in stance phase, and a third pattern for gait without knee support. The knee angle and torque were measured during the experimental protocol to compare both temporospatial parameters and kinematics data with other studies of gait with knee exoskeletons. The users rated scores related to their satisfaction with both the device and controller through QUEST questionnaires. Experimental results showed that the admittance controller proposed here offered knee support in 50% of the gait cycle, and the walking speed was not significantly different between the three gain patterns (p = 0.067). A positive effect of the controller on users regarding safety during gait was found with a score of 4 in a scale of 5. Therefore, the approach demonstrates good performance to adjust impedance components providing knee support in stance phase

Chromatin topology and the timing of enhancer function at the HoxD locus

The HoxD gene cluster is critical for proper limb formation in tetrapods. In the emerging limb buds, different subgroups of Hoxd genes respond first to a proximal regulatory signal, then to a distal signal that organizes digits. These two regulations are exclusive from one another and emanate from two distinct topologically associating domains (TADs) flanking HoxD, both containing a range of appropriate enhancer sequences. The telomeric TAD (T-DOM) contains several enhancers active in presumptive forearm cells and is divided into two sub-TADs separated by a CTCF-rich boundary, which defines two regulatory submodules. To understand the importance of this particular regulatory topology to control Hoxd gene transcription in time and space, we either deleted or inverted this sub-TAD boundary, eliminated the CTCF binding sites, or inverted the entire T-DOM to exchange the respective positions of the two sub-TADs. The effects of such perturbations on the transcriptional regulation of Hoxd genes illustrate the requirement of this regulatory topology for the precise timing of gene activation. However, the spatial distribution of transcripts was eventually resumed, showing that the presence of enhancer sequences, rather than either their exact topology or a particular chromatin architecture, is the key factor. We also show that the affinity of enhancers to find their natural target genes can overcome the presence of both a strong TAD border and an unfavorable orientation of CTCF sites

Dbx2 regulation in limbs suggests interTAD sharing of enhancers

BackgroundDuring tetrapod limb development, the HOXA13 and HOXD13 transcription factors are critical for the emergence and organization of the autopod, the most distal aspect where digits will develop. Since previous work had suggested that the Dbx2 gene is a target of these factors, we set up to analyze in detail this potential regulatory interaction.ResultsWe show that HOX13 proteins bind to mammalian‐specific sequences at the vicinity of the Dbx2 locus that have enhancer activity in developing digits. However, the functional inactivation of the DBX2 protein did not elicit any particular phenotype related to Hox genes inactivation in digits, suggesting either redundant or compensatory mechanisms. We report that the neighboring Nell2 and Ano6 genes are also expressed in distal limb buds and are in part controlled by the same Dbx2 enhancers despite being localized into two different topologically associating domains (TADs) flanking the Dbx2 locus.ConclusionsWe conclude that Hoxa13 and Hoxd genes cooperatively activate Dbx2 expression in developing digits through binding to mammalian specific regulatory sequences in the Dbx2 neighborhood. Furthermore, these enhancers can overcome TAD boundaries in either direction to co‐regulate a set of genes located in distinct chromatin domains.publishe

Adaptive Spatial Filter Based on Similarity Indices to Preserve the Neural Information on EEG Signals during On-Line Processing

This work presents a new on-line adaptive filter, which is based on a similarity analysis between standard electrode locations, in order to reduce artifacts and common interferences throughout electroencephalography (EEG) signals, but preserving the useful information. Standard deviation and Concordance Correlation Coefficient (CCC) between target electrodes and its correspondent neighbor electrodes are analyzed on sliding windows to select those neighbors that are highly correlated. Afterwards, a model based on CCC is applied to provide higher values of weight to those correlated electrodes with lower similarity to the target electrode. The approach was applied to brain computer-interfaces (BCIs) based on Canonical Correlation Analysis (CCA) to recognize 40 targets of steady-state visual evoked potential (SSVEP), providing an accuracy (ACC) of 86.44 ± 2.81%. In addition, also using this approach, features of low frequency were selected in the pre-processing stage of another BCI to recognize gait planning. In this case, the recognition was significantly (p < 0.01) improved for most of the subjects (ACC ¿ 74.79%), when compared with other BCIs based on Common Spatial Pattern, Filter Bank - Common Spatial Pattern, and Riemannian Geometry.This research was partially supported by the Spanish Government, Ministry of Economy and Competitiveness (grants NeuroMOD, DPI2015-68664-C4-1-R, and ESSENTIAL, DPI2015-72638-EXP). Authors would like to thank CNPq (304192/2016-3), CAPES (88887.095626/2015-01), and FAPES (72982608) from Brazil, Emerging Leaders in the America’s Program (ELAP, Canada), and SENESCYT (Ecuador) for supporting this researchPeer Reviewe