Meta Heuristics based Machine Learning and Neural Mass Modelling Allied to Brain Machine Interface

New understanding of the brain function and increasing availability of low-cost-non-invasive electroencephalograms (EEGs) recording devices have made brain-computer-interface (BCI) as an alternative option to augmentation of human capabilities by providing a new non-muscular channel for sending commands, which could be used to activate electronic or mechanical devices based on modulation of thoughts. In this project, our emphasis will be on how to develop such a BCI using fuzzy rule-based systems (FRBSs), metaheuristics and Neural Mass Models (NMMs). In particular, we treat the BCI system as an integrated problem consisting of mathematical modelling, machine learning and classification. Four main steps are involved in designing a BCI system: 1) data acquisition, 2) feature extraction, 3) classification and 4) transferring the classification outcome into control commands for extended peripheral capability. Our focus has been placed on the first three steps. This research project aims to investigate and develop a novel BCI framework encompassing classification based on machine learning, optimisation and neural mass modelling. The primary aim in this project is to bridge the gap of these three different areas in a bid to design a more reliable and accurate communication path between the brain and external world. To achieve this goal, the following objectives have been investigated: 1) Steady-State Visual Evoked Potential (SSVEP) EEG data are collected from human subjects and pre-processed; 2) Feature extraction procedure is implemented to detect and quantify the characteristics of brain activities which indicates the intention of the subject.; 3) a classification mechanism called an Immune Inspired Multi-Objective Fuzzy Modelling Classification algorithm (IMOFM-C), is adapted as a binary classification approach for classifying binary EEG data. Then, the DDAG-Distance aggregation approach is proposed to aggregate the outcomes of IMOFM-C based binary classifiers for multi-class classification; 4) building on IMOFM-C, a preference-based ensemble classification framework known as IMOFM-CP is proposed to enhance the convergence performance and diversity of each individual component classifier, leading to an improved overall classification accuracy of multi-class EEG data; and 5) finally a robust parameterising approach which combines a single-objective GA and a clustering algorithm with a set of newly devised objective and penalty functions is proposed to obtain robust sets of synaptic connectivity parameters of a thalamic neural mass model (NMM). The parametrisation approach aims to cope with nonlinearity nature normally involved in describing multifarious features of brain signals

A cost effective approach for the practical realisation of a demonstration platform for brain machine interface

Over the last two decades, human brain functions have attracted a significant attention among researchers across a broad engineering spectrum. The most important field among the others, is Brain Computer Interface (BCI) which is a direct functional interaction between a human brain and external devices. In the past, the set-up for BCI research is costly and complex. In this paper, a cost effective way of implementing and designing a demonstration platform for BCI research is presented, featuring a low-cost hardware implementation based on an open-source electronics platform Arduino® with the view of being compatible with MATLAB® and Simulink®, and a commercial non-invasive electroencephalogram (EEG) recording device, Emotive®. Due to the compatibility with MATLAB® and Simulink®, and the chosen EEG logging device, the developed hardware and software platform can work seamlessly with several widely accepted BCI and EEG signal processing open-source software within the BCI research community, such as EEGLAB and OpenViBE. With the two-way communication and hardware-in-the-loop concept embedded within the design process, the developed platform can be tuned in an online fashion, which bears the long-term objective of investigating a holistic human-in-the-loop feedback control mechanism so that human and machines can collaborate in a more intelligent and natural way. The presented approach can be beneficial for BCI practitioners to set up their first inexpensive test rig and carry out fast prototyping in related activities

Modelling passengers in air-rail multimodality

Air-rail mobility has the potential to play a significant role in addressing European mobility challenges such as emissions reduction and capacity shortages. Rail can complement the air network in different ways: enlarging airport catchment areas, supporting operations in case of disruption or replacing air links to obtain environmental benefits. There is, however, still a need to better understand the potential role of rail when substituting current air links both from a strategic and a tactical mobility perspective, particularly when passenger connections are con- sidered. This was initially assessed, considering passengers’ door-to-door itineraries, as part of the Modus project (H2020 - SESAR 2020) with an innovative approach towards data driven, integrated air-rail modelling. Further considerations, such as the evaluation of strategic and tactical multimodal solutions, will be explored in the MultiModX project (Horizon Europe - SESAR 3). This discussion paper presents the modelling challenges addressed in Modus and the approach defined for MultiModX to evaluate and model multimodal door-to-door solutions

A robust evolutionary optimisation approach for parameterising a neural mass model

In this paper, a robust optimisation approach is introduced for parameterising a thalamic neural mass model that simulates brain oscillations such as observed in electroencephalogram and local field potentials. In a previous work, the model was informed by physiological attributes of the Lateral Geniculate Nucleus in mammals and rodents; the synaptic connectivity parameters in the model were set manually by trial and error to oscillate within the alpha band (8–13 Hz). However, such manual techniques constrain modelling approaches involving a larger parameter space, for example towards exploring alternative parameter sets that may underlie similar brain states under different environmental conditions and owing to inter-individual differences. In this work, we implement a robust optimisation technique that is based on single-objective Genetic Algorithms, and incorporate newly devised objective and penalty functions for tackling the stochastic nature of the model input. Furthermore, a clustering algorithm is employed to identify robust and distinct parameter regions that will mimic spontaneous changes in thalamic circuit parameters under similar brain states due to environmental and inter-individual differences. The results from our study suggest that multiple robust and distinct parameter regions indeed exist, and the model shows consistent dominant frequency of oscillation within the alpha band corresponding to all of these identified parameter sets