In this paper, we propose a novel Convolutional Neural Network (CNN) approach for the classification of raw dry-EEG signals without any data pre-processing. To illustrate effectiveness our approach, utilise Steady State Visual Evoked Potential (SSVEP) paradigm as use case. SSVEP can be utilised to allow people with severe physical disabilities such Complete Locked-In Syndrome or Amyotrophic Lateral Sclerosis aided via BCI applications, it requires only subject fixate upon sensory stimuli...

Despite significant recent progress in the area of Brain-Computer Interface (BCI), there are numerous shortcomings associated with collecting Electroencephalography (EEG) signals real-world environments. These include, but not limited to, subject and session data variance, long arduous calibration processes predictive generalisation issues across different subjects or sessions. This implies that many downstream applications, including Steady State Visual Evoked Potential (SSVEP) based...

Brain-computer interfaces (BCI) harnessing steady state visual evoked potentials (SSVEPs) manipulate the frequency and phase of stimuli to generate predictable oscillations in neural activity. For BCI spellers, are matched with alphanumeric characters allowing users select target numbers letters. Advances spellers can, part, be accredited subject-specific optimization, including; 1) custom electrode arrangements; 2) filter sub-band assessments; 3) stimulus parameter tuning. Here, we apply...

This paper addresses the challenge of humanoid robot teleoperation in a natural indoor environment via Brain-Computer Interface (BCI). We leverage deep Convolutional Neural Network (CNN) based image and signal understanding to facilitate both real-time bject detection dry-Electroencephalography (EEG) human cortical brain bio-signals decoding. employ recent advances dry-EEG technology stream collect waveforms from subjects while they fixate on variable Steady State Visual Evoked Potential...

A machine learning model capable of extracting structural information from XANES spectra is introduced. This approach, analogous to a Fourier transform EXAFS spectra, can predict first coordination shell bond-lengths with median error 0.1 Å.

We demonstrate uncertainty quantification for deep neural network predictions of transition metal X-ray absorption near-edge structure spectra. Our results not only provide accurate spectral predictions, but reliably assess when the model fails.

Human imagination and intention can be read by using the Electroencephalography(EEG)-Based Brain Computer Interface(BCI) for Motor Imagery. The systems reads human brain which consists of left or right imaginary movement decide actual movement. It is useful especially disable people to help their daily life. But signals include unwanted other features together with signal. challenges are remove extract accurate feature classify signal accurately best classification method. This paper applied...

Recently, substantial progress has been made in the area of Brain-Computer Interface (BCI) using modern machine learning techniques to decode and interpret brain signals. While Electroencephalography (EEG) provided a non-invasive method interfacing with human brain, acquired data is often heavily subject session dependent. This makes seamless incorporation such into realworld applications intractable as variance can lead long tedious calibration requirements cross-subject generalisation...

This paper presents the unscented Kalman filter (UKF) to BCI signal processing classify EEG-based motor imagery signals. UKF is applied common spatio-spectral pattern (CSSP) filters improve feature data extracted from system. The CSSP used extract related features by applying spatial and spectral Linear discriminant analysis (LDA) as classification method discriminate between class 1 (left hand) 2 (right in performance criteria of results are accuracy, kappa value, training time confidence...

This paper presents the unscented Kalman filter (UKF) to BCI signal processing classify EEG-based motor imagery signals. UKF is applied common spatio-spectral pattern (CSSP) filters improve feature data extracted from system. The CSSP used extract related features by applying spatial and spectral Linear discriminant analysis (LDA) as classification method discriminate between class 1 (left hand) 2 (right in performance criteria of results are accuracy, kappa value, training time confidence...

Social distancing in public spaces has become an essential aspect helping to reduce the impact of COVID-19 pandemic. Exploiting recent advances machine learning, there have been many studies literature implementing social via object detection through use surveillance cameras spaces. However, date, no study distance measurement on transport. The transport setting some unique challenges, including low-resolution images and camera locations that can lead partial occlusion passengers, which make...

Recently, substantial progress has been made in the area of Brain-Computer Interface (BCI) using modern machine learning techniques to decode and interpret brain signals. While Electroencephalography (EEG) provided a non-invasive method interfacing with human brain, acquired data is often heavily subject session dependent. This makes seamless incorporation such into real-world applications intractable as variance can lead long tedious calibration requirements cross-subject generalisation...

Social distancing in public spaces has become an essential aspect helping to reduce the impact of COVID-19 pandemic. Exploiting recent advances machine learning, there have been many studies literature implementing social via object detection through use surveillance cameras spaces. However, no study distance measurement on transport date. The setting some unique challenges, including low-resolution images and physical camera locations that can lead partial occlusion passengers, making it...

Passenger behaviour on public transport has become a source of great interest in the wake COVID-19 pandemic. Operators are interested employing new methods to monitor vehicle utilisation and passenger behaviour. One way do this is through use Machine Learning, using CCTV footage that already being captured from vehicles. However, one limitations Learning it requires large amounts annotated training data, which not always available. In poster, we present technique uses 3D models generate...