Impact of age, VR, immersion, and spatial resolution on classifier performance for a MI-based BCI

There are many factors outlined in the signal processing pipeline that impact brain–computer interface (BCI) performance, but some methodological factors do not depend on signal processing. Nevertheless, there is a lack of research assessing the effect of such factors. Here, we investigate the impact of VR, immersiveness, age, and spatial resolution on the classifier performance of a Motor Imagery (MI) electroencephalography (EEG)-based BCI in naïve participants. We found significantly better performance for VR compared to non-VR (15 electrodes: VR 77.48 ± 6.09%, non-VR 73.5 ± 5.89%, p = 0.0096; 12 electrodes: VR 73.26 ± 5.2%, non-VR 70.87 ± 4.96%, p = 0.0129; 7 electrodes: VR 66.74 ± 5.92%, non-VR 63.09 ± 8.16%, p = 0.0362) and better performance for higher electrode quantity, but no significant differences were found between immersive and non immersive VR. Finally, there was not a statistically significant correlation found between age and classifier performance, but there was a direct relation found between spatial resolution (electrode quantity) and classifier performance (r = 1, p = 0.0129, VR; r = 0.99, p = 0.0859, non-VR).info:eu-repo/semantics/publishedVersio

EEG sensorimotor rhythms' variation and functional connectivity measures during motor imagery: linear relations and classification approaches

FUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULO - FAPESPFINANCIADORA DE ESTUDOS E PROJETOS - FINEPCONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO - CNPQCOORDENAÇÃO DE APERFEIÇOAMENTO DE PESSOAL DE NÍVEL SUPERIOR - CAPESHands motor imagery (MI) has been reported to alter synchronization patterns amongst neurons, yielding variations in the mu and beta bands' power spectral density (PSD) of the electroencephalography (EEG) signal. These alterations have been used in the field of brain-computer interfaces (BCI); in an attempt to assign distinct MI tasks to commands of such a system. Recent studies have highlighted that inforniation may be missing if knowledge about brain functional connectivity is not considered. In this work, we modeled the brain as a graph in which each EEG electrode represents a node. Our goal was to understand if there exists any I near correlation between variations in the synchronization patterns that is, variations in the PSD of mu and beta bands induced by MI and alterations in the corresponding functional networks. Moreover, we (I) explored the feasibility of using functional connectivity parameters as features fora classifier in the context of an MI-BCI; (2) investigated three different types of feature selection (FS) techniques; and (3) compared our approach to a more traditional method using the signal PSD as classifier inputs. Ten healthy subjects participated in this study. We observed significant correlations (p < 0.05) with values ranging from 0.4 to 0.9 between PSD variations and functional network alterations for some electrodes, prominently in the beta band. The PSD method performed better for data classification, with mean accuracies of (90 +/- 8)% and (87 +/- 7)% for the mu and beta band, respectively, versus (83 +/- 8)% and (83 +/- 7)% for the same bands for the graph method. Moreover, the number of features for the graph method was considerably larger. However, results for both methods were relatively close, and even overlapped when the uncertainties of the accuracy rates were considered. Further investigation regarding a careful exploration of other graph metrics may provide better alternatives.5115FUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULO - FAPESPFINANCIADORA DE ESTUDOS E PROJETOS - FINEPCONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO - CNPQCOORDENAÇÃO DE APERFEIÇOAMENTO DE PESSOAL DE NÍVEL SUPERIOR - CAPESFUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULO - FAPESPFINANCIADORA DE ESTUDOS E PROJETOS - FINEPCONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO - CNPQCOORDENAÇÃO DE APERFEIÇOAMENTO DE PESSOAL DE NÍVEL SUPERIOR - CAPES2013/07559-3Sem informaçãoSem informaçãoSem informaçã

Brain Network and Abnormal Hemispheric Asymmetry Analyses to Explore the Marginal Differences in Glucose Metabolic Distributions Among Alzheimer's Disease, Parkinson's Disease Dementia, and Lewy Body Dementia

Facilitating accurate diagnosis and ensuring appropriate treatment of dementia subtypes, including Alzheimer's disease (AD), Parkinson's disease dementia (PDD), and Lewy body dementia (DLB), is clinically important. However, the differences in glucose metabolic distribution among these three dementia subtypes are minor, which can result in difficulties in diagnosis by visual assessment or traditional quantification methods. Here, we explored this issue using novel approaches, including brain network and abnormal hemispheric asymmetry analyses. We generated 18F-labeled fluorodeoxyglucose (18F-FDG) positron emission tomography (PET) images from patients with AD, PDD, and DLB, and healthy control (HC) subjects (n = 22, 18, 22, and 22, respectively) from Huashan hospital, Shanghai, China. Brain network properties were measured and between-group differences evaluated using graph theory. We also calculated and explored asymmetry indices for the cerebral hemispheres in the four groups, to explore whether differences between the two hemispheres were characteristic of each group. Our study revealed significant differences in the network properties of the HC and AD groups (small-world coefficient, 1.36 vs. 1.28; clustering coefficient, 1.48 vs. 1.59; characteristic path length, 1.57 vs. 1.64). In addition, differing hub regions were identified in the different dementias. We also identified rightward asymmetry in the hemispheric brain networks of patients with AD and DLB, and leftward asymmetry in the hemispheric brain networks of patients with PDD, which were attributable to aberrant topological properties in the corresponding hemispheres

Imaging plus X: multimodal models of neurodegenerative disease

PURPOSE OF REVIEW: This article argues that the time is approaching for data-driven disease modelling to take centre stage in the study and management of neurodegenerative disease. The snowstorm of data now available to the clinician defies qualitative evaluation; the heterogeneity of data types complicates integration through traditional statistical methods; and the large datasets becoming available remain far from the big-data sizes necessary for fully data-driven machine-learning approaches. The recent emergence of data-driven disease progression models provides a balance between imposed knowledge of disease features and patterns learned from data. The resulting models are both predictive of disease progression in individual patients and informative in terms of revealing underlying biological patterns. RECENT FINDINGS: Largely inspired by observational models, data-driven disease progression models have emerged in the last few years as a feasible means for understanding the development of neurodegenerative diseases. These models have revealed insights into frontotemporal dementia, Huntington's disease, multiple sclerosis, Parkinson's disease and other conditions. For example, event-based models have revealed finer graded understanding of progression patterns; self-modelling regression and differential equation models have provided data-driven biomarker trajectories; spatiotemporal models have shown that brain shape changes, for example of the hippocampus, can occur before detectable neurodegeneration; and network models have provided some support for prion-like mechanistic hypotheses of disease propagation. The most mature results are in sporadic Alzheimer's disease, in large part because of the availability of the Alzheimer's disease neuroimaging initiative dataset. Results generally support the prevailing amyloid-led hypothetical model of Alzheimer's disease, while revealing finer detail and insight into disease progression. SUMMARY: The emerging field of disease progression modelling provides a natural mechanism to integrate different kinds of information, for example from imaging, serum and cerebrospinal fluid markers and cognitive tests, to obtain new insights into progressive diseases. Such insights include fine-grained longitudinal patterns of neurodegeneration, from early stages, and the heterogeneity of these trajectories over the population. More pragmatically, such models enable finer precision in patient staging and stratification, prediction of progression rates and earlier and better identification of at-risk individuals. We argue that this will make disease progression modelling invaluable for recruitment and end-points in future clinical trials, potentially ameliorating the high failure rate in trials of, e.g., Alzheimer's disease therapies. We review the state of the art in these techniques and discuss the future steps required to translate the ideas to front-line application.This is an open access article distributed under the terms of the Creative Commons Attribution-Non Commercial-No Derivatives License 4.0 (CCBY-NC-ND), where it is permissible to download and share the work provided it is properly cited. The work cannot be changed in any way or used commercially without permission from the journal. http://creativecommons.org/licenses/by-nc-nd/4.0

Smell and Anosmia in the Aesthetic: Appreciation of Gardens

In his Critique of the Power of Judgment, Kant defined the garden as a visual art and considered that smell plays no role in its aesthetic appreciation. If the Kantian thesis were right, then a person who has no sense of smell (who suffers from anosmia) would not be impaired in his or her aesthetic appreciation of gardens. At the same time, a visually impaired person could not appreciate the beauty of gardens, although he or she could perceive them through hearing, smell, taste, and touch. In this paper I discuss the role of smell and anosmia in the aesthetic appreciation of gardens. I accept the Kantian idea that the appreciation of a garden is the appreciation of its form, but I also defend that, at least in some cases, smell can belong to the form of gardens and, consequently, the ability or inability to smell influences their aesthetic appreciation

Simultaneous odour-face presentation strengthens hedonic evaluations and event-related potential responses influenced by unpleasant odour

open access articleOdours alter evaluations of concurrently presented visual stimuli, such as faces. Stimulus onset asynchrony (SOA) is known to affect evaluative priming in various sensory modalities. However, effects of SOA on odour priming of visual stimuli are not known. The present study aimed to analyse whether subjective and cortical activation changes during odour priming would vary as a function of SOA between odours and faces. Twenty-eight participants rated faces under pleasant, unpleasant, and no-odour conditions using visual analogue scales. In half of trials, faces appeared one-second after odour offset (SOA 1). In the other half of trials, faces appeared during the odour pulse (SOA 2). EEG was recorded continuously using a 128-channel system, and event-related potentials (ERPs) to face stimuli were evaluated using statistical parametric mapping (SPM). Faces presented during unpleasant-odour stimulation were rated significantly less pleasant than the same faces presented one-second after offset of the unpleasant odour. Scalp-time clusters in the late-positive-potential (LPP) time-range showed an interaction between odour and SOA effects, whereby activation was stronger for faces presented simultaneously with the unpleasant odour, compared to the same faces presented after odour offset. Our results highlight stronger unpleasant odour priming with simultaneous, compared to delayed, odour-face presentation. Such effects were represented in both behavioural and neural data. A greater cortical and subjective response during simultaneous presentation of faces and unpleasant odour may have an adaptive role, allowing for a prompt and focused behavioural reaction to a concurrent stimulus if an aversive odour would signal danger, or unwanted social interaction

TOWARDS THE AUTOMATISATION OF A FOREIGN LANGUAGE: SENSORIMOTOR DRILLING, THE STRUCTURATION OF LINGUISTIC INPUT ON THE BASIS OF PROCESSING DEMANDS AND SENSORY CHUNKING

The current study presents the results of a treatment that sought to improve the 3rd person singular -s of the present simple tense. Sixty-four EFL learners from three different primary schools participated in the experiment. Learners were divided into a control group and two experimental groups. Whereas the control group followed its own school instruction, the two experimental groups followed a treatment that was based on neuroscience and psychology and that integrated innovative pedagogical techniques (©2018, 2019, Verónica Mendoza Fernández): sensorimotor drilling, the structuration of linguistic input on the basis of processing demands and sensory chunking. Learners carried out four pretest-postest tasks. Here are presented the results of one of the tasks: oral sentence transformation. The findings of the study indicated that statistical significance was reached by the two experimental groups only. Article visualizations

Pleasant and unpleasant odour-face combinations influence face and odour perception: An event-related potential study.

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Odours alter evaluations of concurrent visual stimuli. However, neural mechanisms underlying the effects of congruent and incongruent odours on facial expression perception are not clear. Moreover, the influence of emotional faces on odour perception is not established. We investigated the effects of one pleasant and one unpleasant odour paired with happy and disgusted faces, on subjective ratings and ERP responses to faces. Participants rated the pleasantness of happy and disgusted faces that appeared during 3 s pleasant or unpleasant odour pulses, or without odour. Odour pleasantness and intensity ratings were recorded in each trial. EEG was recorded continuously using a 128-channel system. Happy and disgusted faces paired with pleasant and unpleasant odour were rated as more or less pleasant, respectively, compared to the same faces presented in the other odour conditions. Odours were rated as more pleasant when paired with happy faces, and unpleasant odour was rated more intense when paired with disgusted faces. Unpleasant odour paired with disgusted faces also decreased inspiration. Odour-face interactions were evident in the N200 and N400 components. Our results reveal bi-directional effects of odours and faces, and suggest that odour-face interactions may be represented in ERP components. Pairings of unpleasant odour and disgusted faces resulted in stronger hedonic ratings, ERP changes, increased odour intensity ratings and respiratory adjustment. This finding likely represents heightened adaptive responses to multimodal unpleasant stimuli, prompting appropriate behaviour in the presence of danger

The Design Matters: How to Detect Neural Correlates of Baby Body Odors

Functional magnetic resonance imaging of body odors is challenging due to methodological obstacles of odor presentation in the scanner and low intensity of body odors. Hence, few imaging studies investigated neural responses to body odors. Those differ in design characteristics and have shown varying results. Evidence on central processing of baby body odors has been scarce but might be important in order to detect neural correlates of bonding in mothers. A suitable paradigm for investigating perception of baby body odors has still to be established. We compared neural responses to baby body odors in a new to a conventional block design in a sample of ten normosmic mothers. For the new short design, 6 s of continuous odor presentation were followed by 19 s baseline and 13 repetitions were performed. For the conventional long design, 15 s of pulsed odor presentation were followed by 30 s of baseline and eight repetitions were performed. Neural responses were observed in brain structures related to basal and higher-order olfactory processing, such as insula, orbitofrontal cortex, and amygdala. Neural responses following the short design were significantly higher in comparison to the long design. This effect was based on higher number of repetitions but affected olfactory areas differently. The BOLD signal in the primary olfactory structures was enhanced by short and continuous stimulation, secondary structures did profit from longer stimulations with many repetitions. The short design is recommended as a suitable paradigm in order to detect neuronal correlates of baby body odors