Classification of EEG signals on standing, walking and running dataset using LSTM-RNN

Undoubtedly one of the most important strands of the brain-computer interface (BCI) method is an alternate communication method via brain signals. BCI converts electroencephalogram (EEG) signals from a perception of activity in the brain into user action utilising software and hardware. BCI has piqued the interest of researchers in a wide range of disciplines, such as cognitive science, deep learning, pattern matching, drug treatment medicine, etc. Patients suffering from neuro and cognitive disorders can be assisted through BCI, potentially enabling communication via gestures or just mental imagination. In this paper, a novel combination of Discrete Wavelet Transform (DWT) for extracting the best features and Long Short-Term Memory (LSTM) based Recurrent Neural Network (RNN) is adopted for classifying the EEG signals acquired during standing, walking and running on a treadmill. The dataset used is freely downloaded from Open Science Framework repository. The proposed DWT-LSTMRNN method delivers 96.7% accuracy while classifying four different signals, and thus has the potential to be investigated further on BCI competition datasets that will pave way for a real-time application

Affective recognition from EEG signals: an integrated data-mining approach

Emotions play an important role in human communication, interaction, and decision making processes. Therefore, considerable efforts have been made towards the automatic identification of human emotions, in particular electroencephalogram (EEG) signals and Data Mining (DM) techniques have been then used to create models recognizing the affective states of users. However, most previous works have used clinical grade EEG systems with at least 32 electrodes. These systems are expensive and cumbersome, and therefore unsuitable for usage during normal daily activities. Smaller EEG headsets such as the Emotiv are now available and can be used during daily activities. This paper investigates the accuracy and applicability of previous affective recognition methods on data collected with an Emotiv headset while participants used a personal computer to fulfill several tasks. Several features were extracted from four channels only (AF3, AF4, F3 and F4 in accordance with the 10–20 system). Both Support Vector Machine and Naïve Bayes were used for emotion classification. Results demonstrate that such methods can be used to accurately detect emotions using a small EEG headset during a normal daily activity

An investigation into applying ontologies to the UK railway industry

The uptake of ontologies in the Semantic Web and Linked Data has proven their excellence in managing mass data. Referring to the movements of Linked Data, ontologies are applied to large complex systems to facilitate better data management. Some industries, e.g., oil and gas, have at-tempted to use ontologies to manage its internal data structure and man-agement. Researchers have dedicated to designing ontologies for the rail system, and they have discussed the potential benefits thereof. However, despite successful establishment in some industries and effort made from some research, plus the interest from major UK rail operation participants, there has not been evidence showing that rail ontologies are applied to the UK rail system. This thesis will analyse factors that hinder the application of rail ontolo-gies to the UK rail system. Based on concluded factors, the rest of the the-sis will present corresponding solutions. The demonstrations show how ontologies can fit in a particular task with improvements, aiming to pro-vide inspiration and insights for the future research into the application of ontology-based system in the UK rail system