As a measure of the brain's electrical activity, electroencephalography (EEG) is the primary signal of interest for brain-computer-interfaces (BCI). BCIs offer a communication pathway between a brain and an external device, translating thought into action with suitable processing. EEG data is the most common signal source for such technologies. However, artefacts induced in BCIs in the real-world context can severely degrade their performance relative to their in-laboratory performance. In most cases, the recorded signals are so heavily corrupted by noise that they are unusable and restrict BCI's broader applicability. To realise the use of portable BCIs capable of high-quality performance in a real-world setting, we use Generative Adversarial Networks (GANs) that can adopt both supervised and unsupervised learning approaches. Although our approach is supervised, the same model can be used for unsupervised tasks such as data augmentation/imputation in the low resource setting. Exploiting recent advancements in Generative Adversarial Networks (GAN), we construct a pipeline capable of denoising artefacts from EEG time series data. In the case of denoising data, it maps noisy EEG signals to clean EEG signals, given the nature of the respective artefact. We demonstrate the capability of our network on a toy dataset and a benchmark EEG dataset developed explicitly for deep learning denoising techniques. Our datasets consist of an artificially added mains noise (50/60 Hz) artefact dataset and an open-source EEG benchmark dataset with two artificially added artefacts. Artificially inducing myogenic and ocular artefacts for the benchmark dataset allows us to present qualitative and quantitative evidence of the GANs denoising capabilities and rank it among the current gold standard deep learning EEG denoising techniques. We show the power spectral density (PSD), signal-to-noise ratio (SNR), and other classical time series similarity measures for quantitative metrics and compare our model to those previously used in the literature. To our knowledge, this framework is the first example of a GAN capable of EEG artefact removal and generalisable to more than one artefact type. Our model has provided a competitive performance in advancing the state-of-the-art deep learning EEG denoising techniques. Furthermore, given the integration of AI into wearable technology, our method would allow for portable EEG devices with less noisy and more stable brain signals.

Currently many autonomous vehicles require a person to monitor the system and take over when an unexpected or unusual event occurs. A person may be able to monitor multiple such vehicles but a question arises as to how many, and how to measure the cognitive requirements. Brain Computer Interfaces (BCI) operating passively could aid in assisting remote operators in such tasks but there is as yet significant research to be undertaken before such technology becomes robust and effective. To this end we describe a platform for acquisition of multi-modal data for passive hybrid Brain Computer Interface (phBCI) development purposes. The open source system integrates electroencephalography (EEG), computer vision and a wearable inertial measurement unit (IMU) along with timestamped event markers for a subject engaged in a set of driving-related tasks as applied to blended control of multiple vehicles. The vehicular control task is realised both with graded complexity simulations and physical scale autonomous vehicles. This platform has the following significant advantages: reduced experimental variability due to data acquisition system implementation decisions; ease of reproduction of experiments through shareable configuration information; and acceleration of open science dataset accumulation. Consequently this freely available open source platform has the potential to enhance the reproducibility of passive hybrid BCI experimental research.