This paper presents a new active electrode design for electroencephalogram (EEG) and
electrocardiogram (ECG) sensors based on inertial measurement units to remove motion
artefacts during signal acquisition. Rather than measuring motion data from a single source
for the entire recording unit, inertial measurement units are attached to each individual
EEG or ECG electrode to collect local movement data. This data is then used to remove
the motion artefact by using normalised least mean square adaptive filtering. Results show
that the proposed active electrode design can reduce motion contamination from EEG
and ECG signals in chest movement and head swinging motion scenarios. However, it is
found that the performance varies, necessitating the need for the algorithm to be paired
with more sophisticated signal processing to identify scenarios where it is beneficial in
terms of improving signal quality. The new instrumentation hardware allows data driven
artefact removal to be performed, providing a new data driven approach compared to
widely used blind-source separation methods, and helps enable in the wild EEG recordings
to be performed.

Motion artefact removal in electroencephalography and electrocardiography by using multichannel inertial measurement units and adaptive filtering.

Hazard perception during driving is essential in detecting and avoiding traffic incidents. In this study, eye movement analysis is used to measure the driver's hazard perception while engaged in different traffic scenarios on Australian roads. Participants were asked to observe recordings of four different traffic scenarios and react to hazards by clicking the mouse. The aim of this study is to examine three hypotheses; (1) the relationship between driver's experience and hazard identification, (2) the use of pupil dilation as a measure of hazard perception, and (3) the ability of gaze fixation points to determine the driver's perception of the hazard. The results show that eye movement dynamics and change in pupil diameter can provide good measures of the drivers' hazard perception and prediction.

Eye behaviour as a hazard perception measure

In this paper we propose a framework for communicating performance art to deaf, blind and deaf blind audiences and artists haptically through the sense of touch. This research opens doors for novel artistic trends relying mainly on the sense of touch. The paper investigates the design considerations dictated by solo and group dances as well as stage setup. Implementation scenarios for deaf blind audiences and performers are also discussed.

Haptically-Enabled Dance Visualisation Framework for Deafblind-Folded Audience and Artists

This paper develops a Meningioma Detection and Segmentation System (MDSS) using the proposed Meningioma Convolutional Neural Network (MENCNN) classifier. The main objective of this paper is to detect and locate the meningioma brain tumors using the proposed deep learning structure and segmentation algorithm. This proposed MDSS is designed with preprocessing of meningioma and healthy brain MRI images, feature computations and feature classification through the proposed MENCNN classifier and Meningioma Segmentation Algorithm. The noises in both meningioma and healthy brain images are removed using Mean Adaptive Filter (MAF) and the meningioma features are computed from the noise removed image. These meningioma features are classified by the proposed MENCNN classifier in order to obtain the classification results as either meningioma or healthy brain image. Finally, Meningioma Segmentation Algorithm (MSA) is proposed in this research work to segment the pixels belonging to the meningioma region. The proposed MDSS approach obtains 96.46% MSI, 97.75% MSR and 97.6% MSA on the set of meningioma images in Nanfang dataset. The proposed MDSS approach obtains 97.76% MSI, 98.03% MSR and 97.81% MSA on the set of meningioma images in Kaggle dataset. 

Detection and Segmentation of Meningioma Tumors Using the Proposed MENCNN Model

Persistence diagrams (PD)s play a central role in topological data analysis, and are used in an ever increasing variety of applications. The comparison of PD data requires computing distances among large sets of PDs, with metrics which are accurate, theoretically sound, and fast to compute. Especially for denser multi-dimensional PDs, such comparison metrics are lacking. While on the one hand, Wasserstein-type distances have high accuracy and theoretical guarantees, they incur high computational cost. On the other hand, distances between vectorizations such as Persistence Statistics (PS)s have lower computational cost, but lack the accuracy guarantees and theoretical properties of a true distance over PD space. In this work we introduce a class of pseudodistances called Extended Topological Pseudodistances (ETD)s, which have tunable complexity, and can approximate Sliced and classical Wasserstein distances at the high-complexity extreme, while being computationally lighter and close to Persistence Statistics at the lower complexity extreme, and thus allow users to interpolate between the two metrics. We build theoretical comparisons to show how to fit our new distances at an intermediate level between persistence vectorizations and Wasserstein distances. We also experimentally verify that ETDs outperform PSs in terms of accuracy and outperform Wasserstein and Sliced Wasserstein distances in terms of computational complexity. 

pdf/a-class-of-topological-pseudodistances-for-fast-comparison-1k37fkx1xf.pdf

A Class of Topological Pseudodistances for Fast Comparison of Persistence Diagrams

Driving behaviour prediction is a challenging problem due to the nonlinearity of human behaviour. Linear and nonlinear techniques have been used to solve this problem, and they provide good results presented in the performance of the current autonomous cars. However, they lack the ability to adapt to abruptness that happens because of the human factor. In this paper, we introduce a method to extract persistent homology barcode statistics. These statistics are useful as a representative of the driving process including the human behaviour. Human factor identification requires finding features that preserve certain properties against scalability, deformation, and abruptness. Topological Data Analysis (TDA) using persistent homology provides these features for driver behaviour prediction. We captured a driver's head motion as an experimental behavioural cue, combined it with captured simulated vehicle data (location and velocities). Barcodes are extracted using JavaPlex, then we extracted descriptive statistics to show the significance of these barcode as features for driver behaviour prediction. The correlation between the extracted features shows a promising start for a behavioural tracking applications using TDA.

Driving behaviour analysis using topological features

Adaptive filters are now becoming increasingly studied for their suitability in application to complex and non-stationary signals. Many adaptive filters utilise a reference input, that is used to form an estimate of the noise in the target signal. In this paper we discuss the application of adaptive filters for high electromyography contaminated electroencephalography data. We propose the use of multiple referential inputs instead of the traditional single input. These references are formed using multiple EMG sensors during an EEG experiment, each reference input is processed and ordered through firstly determining the Pearson’s r-squared correlation coefficient, from this a weighting metric is determined and used to scale and order the reference channels according to the paradigm shown in this paper. This paper presents the use and application of the Adaptive-Multi-Reference (AMR) Least Means Square adaptive filter in the domain of electroencephalograph signal acquisition.

Adaptive-Multi-Reference Least Means Squares Filter

Neural spike sorting framework using selected wavelets coefficients and k-means clustering

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Driving behaviour analysis using topological features

Adaptive-Multi-Reference Least Means Squares Filter

Neural spike sorting framework using selected wavelets coefficients and k-means clustering