151 research outputs found
Decoding Working-Memory Load During n-Back Task Performance from High Channel NIRS Data
Near-infrared spectroscopy (NIRS) can measure neural activity through blood
oxygenation changes in the brain in a wearable form factor, enabling unique
applications for research in and outside the lab. NIRS has proven capable of
measuring cognitive states such as mental workload, often using machine
learning (ML) based brain-computer interfaces (BCIs). To date, NIRS research
has largely relied on probes with under ten to several hundred channels,
although recently a new class of wearable NIRS devices with thousands of
channels has emerged. This poses unique challenges for ML classification, as
NIRS is typically limited by few training trials which results in severely
under-determined estimation problems. So far, it is not well understood how
such high-resolution data is best leveraged in practical BCIs and whether
state-of-the-art (SotA) or better performance can be achieved. To address these
questions, we propose an ML strategy to classify working-memory load that
relies on spatio-temporal regularization and transfer learning from other
subjects in a combination that has not been used in previous NIRS BCIs. The
approach can be interpreted as an end-to-end generalized linear model and
allows for a high degree of interpretability using channel-level or cortical
imaging approaches. We show that using the proposed methodology, it is possible
to achieve SotA decoding performance with high-resolution NIRS data. We also
replicated several SotA approaches on our dataset of 43 participants wearing a
3198 dual-channel NIRS device while performing the n-Back task and show that
these existing methods struggle in the high-channel regime and are largely
outperformed by the proposed method. Our approach helps establish high-channel
NIRS devices as a viable platform for SotA BCI and opens new applications using
this class of headset while also enabling high-resolution model imaging and
interpretation.Comment: 29 pages, 9 figures. Under revie
EEG-Based Quantification of Cortical Current Density and Dynamic Causal Connectivity Generalized across Subjects Performing BCI-Monitored Cognitive Tasks.
Quantification of dynamic causal interactions among brain regions constitutes an important component of conducting research and developing applications in experimental and translational neuroscience. Furthermore, cortical networks with dynamic causal connectivity in brain-computer interface (BCI) applications offer a more comprehensive view of brain states implicated in behavior than do individual brain regions. However, models of cortical network dynamics are difficult to generalize across subjects because current electroencephalography (EEG) signal analysis techniques are limited in their ability to reliably localize sources across subjects. We propose an algorithmic and computational framework for identifying cortical networks across subjects in which dynamic causal connectivity is modeled among user-selected cortical regions of interest (ROIs). We demonstrate the strength of the proposed framework using a "reach/saccade to spatial target" cognitive task performed by 10 right-handed individuals. Modeling of causal cortical interactions was accomplished through measurement of cortical activity using (EEG), application of independent component clustering to identify cortical ROIs as network nodes, estimation of cortical current density using cortically constrained low resolution electromagnetic brain tomography (cLORETA), multivariate autoregressive (MVAR) modeling of representative cortical activity signals from each ROI, and quantification of the dynamic causal interaction among the identified ROIs using the Short-time direct Directed Transfer function (SdDTF). The resulting cortical network and the computed causal dynamics among its nodes exhibited physiologically plausible behavior, consistent with past results reported in the literature. This physiological plausibility of the results strengthens the framework's applicability in reliably capturing complex brain functionality, which is required by applications, such as diagnostics and BCI
Blobs versus bars:psychophysical evidence supports two types of orientation response in human color vision
The classic hypothesis of Livingstone and Hubel (1984, 1987) proposed two types of color pathways in primate visual cortex based on recordings from single cells: a segregated, modularpathway that signals color but provides little information about shape or form and a second pathway that signals color differences and so defines forms without the need to specify their colors. A major problem has been to reconcile this neurophysiological hypothesis with the behavioral data. A wealth of psychophysical studies has demonstrated that color vision has orientation-tuned responses and little impairment on form related tasks, but these have not revealed any direct evidence for nonoriented mechanisms. Here we use a psychophysical method of subthreshold summation across orthogonal orientations for isoluminant red-green gratings in monocular and dichoptic viewing conditions to differentiate between nonoriented and orientation-tuned responses to color contrast. We reveal nonoriented color responses at low spatial frequencies (0.25-0.375 c/deg) under monocular conditions changing to orientation-tuned responses at higher spatial frequencies (1.5 c/deg) and under binocular conditions. We suggest that two distinct pathways coexist in color vision at the behavioral level, revealed at different spatial scales: one is isotropic, monocular, and best equipped for the representation of surface color, and the other is orientation-tuned, binocular, and selective for shape and form. This advances our understanding of the organization of the neural pathways involved in human color vision and provides a strong link between neurophysiological and behavioral data
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Decoding Depth of Meditation: Electroencephalography Insights From Expert Vipassana Practitioners.
BACKGROUND: Meditation practices have demonstrated numerous psychological and physiological benefits, but capturing the neural correlates of varying meditative depths remains challenging. In this study, we aimed to decode self-reported time-varying meditative depth in expert practitioners using electroencephalography (EEG). METHODS: Expert Vipassana meditators (n = 34) participated in 2 separate sessions. Participants reported their meditative depth on a personally defined 1 to 5 scale using both traditional probing and a novel spontaneous emergence method. EEG activity and effective connectivity in theta, alpha, and gamma bands were used to predict meditative depth using machine/deep learning, including a novel method that fused source activity and connectivity information. RESULTS: We achieved significant accuracy in decoding self-reported meditative depth across unseen sessions. The spontaneous emergence method yielded improved decoding performance compared with traditional probing and correlated more strongly with postsession outcome measures. Best performance was achieved by a novel machine learning method that fused spatial, spectral, and connectivity information. Conventional EEG channel-level methods and preselected default mode network regions fell short in capturing the complex neural dynamics associated with varying meditation depths. CONCLUSIONS: This study demonstrates the feasibility of decoding personally defined meditative depth using EEG. The findings highlight the complex, multivariate nature of neural activity during meditation and introduce spontaneous emergence as an ecologically valid and less obtrusive experiential sampling method. These results have implications for advancing neurofeedback techniques and enhancing our understanding of meditative practices
Understanding walking and cycling:summary of key findings and recommendations
It is widely recognized that there is a need to increase levels of active and sustainable travel in British urban areas. The Understanding Walking and Cycling (UWAC) project, funded by the EPSRC, has examined the factors influencing everyday travel decisions and proposes a series of policy measures to increase levels of walking and cycling for short trips in urban areas. A wide range of both quantitative and qualitative data were collected in four English towns (Lancaster, Leeds, Leicester and Worcester), including a questionnaire survey, spatial analysis of the built environment, interviews (static and whilst mobile) and detailed ethnographies. Key findings of the research are that whilst attitudes to walking and cycling are mostly positive or neutral, many people who would like to engage in more active travel fail to do so due to a combination of factors. These can be summarised as:
Concerns about the physical environment, especially with regard to safety when walking or cycling;
The difficulty of fitting walking and cycling into complex household routines
(especially with young children);
The perception that walking and cycling are in some ways abnormal things to do. It is suggested that policies to increase levels of walking and cycling should focus not only on improving infrastructure (for instance through fully segregated cycle routes along main roads and restriction on vehicle speeds), but also must tackle broader social, economic, cultural and legal factors that currently inhibit walking and cycling. Together, such changes can create an environment in which driving for short trips in urban areas is seen as abnormal and walking or cycling seem the obvious choices. A joint project by by Lancaster University, Oxford Brookes University and the University of Leeds
Economic Analysis of Improving Cold Tolerance in Rice in Australia
The occurrence of low night temperatures during reproductive development is one of the factors most limiting rice yields in southern Australia. Yield losses due to cold temperature are the result of incomplete pollen formation and subsequent floret sterility. Researchers have found that in 75% of years, rice farmers suffer losses between 0.5 and 2.5 t/ha. Research is being undertaken to identify overseas rice varieties, that are cold tolerant under the local weather conditions and by using those genotypes as parent material, develop cold tolerance varieties of rice. A yield simulation model was used to measure reduction in losses due to cold at different minimum threshold temperatures, while the SAMBOY Rice model was used to measure the costs and returns of a breeding program for cold tolerance. The results of the economic analysis reveal that new cold tolerant varieties would lead to significant increase in financial benefits through reduction in losses due to cold, and an increase in yield from the better use on nitrogen by the cold tolerant varieties. The returns to investment on the research project are estimated to be high
Real-time neuroimaging and cognitive monitoring using wearable dry EEG
GoalWe present and evaluate a wearable high-density dry-electrode EEG system and an open-source software framework for online neuroimaging and state classification.MethodsThe system integrates a 64-channel dry EEG form factor with wireless data streaming for online analysis. A real-time software framework is applied, including adaptive artifact rejection, cortical source localization, multivariate effective connectivity inference, data visualization, and cognitive state classification from connectivity features using a constrained logistic regression approach (ProxConn). We evaluate the system identification methods on simulated 64-channel EEG data. Then, we evaluate system performance, using ProxConn and a benchmark ERP method, in classifying response errors in nine subjects using the dry EEG system.ResultsSimulations yielded high accuracy (AUC = 0.97 ± 0.021) for real-time cortical connectivity estimation. Response error classification using cortical effective connectivity [short-time direct-directed transfer function (sdDTF)] was significantly above chance with similar performance (AUC) for cLORETA (0.74 ±0.09) and LCMV (0.72 ±0.08) source localization. Cortical ERP-based classification was equivalent to ProxConn for cLORETA (0.74 ±0.16) but significantly better for LCMV (0.82 ±0.12) .ConclusionWe demonstrated the feasibility for real-time cortical connectivity analysis and cognitive state classification from high-density wearable dry EEG.SignificanceThis paper is the first validated application of these methods to 64-channel dry EEG. This study addresses a need for robust real-time measurement and interpretation of complex brain activity in the dynamic environment of the wearable setting. Such advances can have broad impact in research, medicine, and brain-computer interfaces. The pipelines are made freely available in the open-source SIFT and BCILAB toolboxes
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