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The reliability (precision) and agreement (accuracy) of anthropometric measurements based on manually placed 3D landmarks using the RealSense D415 were investigated in this paper. Thirty facial palsy patients, with their face in neutral (resting) position, were recorded simultaneously with the RealSense and a professional 3dMD imaging system. First the RealSense depth accuracy was determined. Subsequently, two observers placed 14 facial landmarks on the 3dMD and RealSense image, assessing the distance between landmark placement. The respective intra- and inter-rater Euclidean distance between the landmark placements was 0.84 mm (±0.58) and 1.00 mm (±0.70) for the 3dMD landmarks and 1.32 mm (±1.27) and 1.62 mm (±1.42) for the RealSense landmarks. From these landmarks 14 anthropometric measurements were derived. The intra- and inter-rater measurements had an overall reliability of 0.95 (0.87 - 0.98) and 0.93 (0.85 - 0.97) for the 3dMD measurements, and 0.83 (0.70 - 0.91) and 0.80 (0.64 - 0.89) for the RealSense measurements, respectively, expressed as the intra-class correlation coefficient. Determined by the Bland-Altman analysis, the agreement between the RealSense measurements and 3dMD measurements was on average -0.90 mm (-4.04 - 2.24) and -0.89 mm (-4.65 - 2.86) for intra- and inter-rater agreement, respectively. Based on the reported reliability and agreement of the RealSense measurements, the RealSense D415 can be considered as a viable option to perform objective 3D anthropomorphic measurements on the face in a neutral position, where a low-cost and portable camera is required.Mental fatigue deteriorates ability to perform daily activities - known as time-on-task (TOT) effect and becomes a common complaint in contemporary society. However, an applicable technique for fatigue detection/prediction is hindered due to substantial inter-subject differences in behavioural impairment and brain activity. Here, we developed a fully cross-validated, data-driven analysis framework incorporating multivariate regression model to explore the feasibility of utilizing functional connectivity (FC) to predict the fatigue-related behavioural impairment at individual level. EEG was recorded from 40 healthy adults as they performed a 30-min high-demanding sustained attention task. FC were constructed in different frequency bands using three widely-adopted methods (including coherence, phase log index (PLI), and partial directed coherence (PDC)) and contrasted between the most vigilant and fatigued states. The differences of individual FC (diff (FC)) were considered as features; whereas the TOT slop across the course of task and the differences of reaction time ( ∆ RT) between the most vigilant and fatigued states were chosen to represent behavioural impairments. Behaviourally, we found substantial inter-subject differences of impairments. Tosedostat concentration Furthermore, we achieved significantly high accuracies for individualized prediction of behavioural impairments using diff(PDC). The identified top diff(PDC) features contributing to the individualized predictions were found mainly in theta and alpha bands. Further interrogation of diff(PDC) features revealed distinct patterns between the TOT slop and ∆ RT prediction models, highlighting the complex neural mechanisms of mental fatigue. Overall, the current findings extended conventional brain-behavioural correlation analysis to individualized prediction of fatigue-related behavioural impairments, thereby moving a step forward towards development of applicable techniques for quantitative fatigue monitoring in real-world scenarios.Electroencephalography (EEG) data are difficult to obtain due to complex experimental setups and reduced comfort with prolonged wearing. This poses challenges to train powerful deep learning model with the limited EEG data. Being able to generate EEG data computationally could address this limitation. We propose a novel Wasserstein Generative Adversarial Network with gradient penalty (WGAN-GP) to synthesize EEG data. This network addresses several modeling challenges of simulating time-series EEG data including frequency artifacts and training instability. We further extended this network to a class-conditioned variant that also includes a classification branch to perform event-related classification. We trained the proposed networks to generate one and 64-channel data resembling EEG signals routinely seen in a rapid serial visual presentation (RSVP) experiment and demonstrated the validity of the generated samples. We also tested intra-subject cross-session classification performance for classifying the RSVP target events and showed that class-conditioned WGAN-GP can achieve improved event-classification performance over EEGNet.
Classification of the neural activity of the brain is a well known problem in the field of brain computer interface. Machine learning based approaches for classification of brain activities do not reveal the underlying dynamics of the human brain.

Since eigen decomposition has been found useful in a variety of applications, we conjecture that change of brain states would manifest in terms of changes in the invariant spaces spanned by eigen vectors as well as amount of variance along them. Based on this, our first approach is to track the brain state transitions by analysing invariant space variations over time. Whereas, our second approach analyses sub-band characteristic response vector formed using eigen values along with the eigen vectors to capture the dynamics.

We have taken two real time EEG datasets to demonstrate the efficacy of proposed approaches. It has been observed that in case of unimodal experiment, invariant spaces explicitly show the transitions of brain states. Whereas sub-band characteristic response vector approach gives better performance in the case of cross-modal conditions.

Evolution of invariant spaces along with the eigen values may help in understanding and tracking the brain state transitions.

The proposed approaches can track the activity transitions in real time. They do not require any training dataset.
The proposed approaches can track the activity transitions in real time. They do not require any training dataset.
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