EEG-Based User Reaction Time Estimation Using Riemannian Geometry Features

Riemannian geometry has been successfully used in many brain-computer interface (BCI) classification problems and demonstrated superior performance. In this paper, for the first time, it is applied to BCI regression problems, an important category of BCI applications. More specifically, we propose a new feature extraction approach for Electroencephalogram (EEG) based BCI regression problems: a spatial filter is first used to increase the signal quality of the EEG trials and also to reduce the dimensionality of the covariance matrices, and then Riemannian tangent space features are extracted. We validate the performance of the proposed approach in reaction time estimation from EEG signals measured in a large-scale sustained-attention psychomotor vigilance task, and show that compared with the traditional powerband features, the tangent space features can reduce the root mean square estimation error by 4.30-8.30%, and increase the estimation correlation coefficient by 6.59-11.13%.Comment: arXiv admin note: text overlap with arXiv:1702.0291

Multiclass Fuzzy Time-Delay Common Spatio-Spectral Patterns with Fuzzy Information Theoretic Optimization for EEG-Based Regression Problems in Brain-Computer Interface (BCI)

© 2019 IEEE. Electroencephalogram (EEG) signals are one of the most widely used noninvasive signals in brain-computer interfaces. Large dimensional EEG recordings suffer from poor signal-to-noise ratio. These signals are very much prone to artifacts and noise, so sufficient preprocessing is done on raw EEG signals before using them for classification or regression. Properly selected spatial filters enhance the signal quality and subsequently improve the rate and accuracy of classifiers, but their applicability to solve regression problems is quite an unexplored objective. This paper extends common spatial patterns (CSP) to EEG state space using fuzzy time delay and thereby proposes a novel approach for spatial filtering. The approach also employs a novel fuzzy information theoretic framework for filter selection. Experimental performance on EEG-based reaction time (RT) prediction from a lane-keeping task data from 12 subjects demonstrated that the proposed spatial filters can significantly increase the EEG signal quality. A comparison based on root-mean-squared error (RMSE), mean absolute percentage error (MAPE), and correlation to true responses is made for all the subjects. In comparison to the baseline fuzzy CSP regression one versus rest, the proposed Fuzzy Time-delay Common Spatio-Spectral filters reduced the RMSE on an average by 9.94%, increased the correlation to true RT on an average by 7.38%, and reduced the MAPE by 7.09%

Mental fatigue and working memory load estimation: Interaction and implications for EEG-based passive BCI

Current mental state monitoring systems, a.k.a. passive brain-computer interfaces (pBCI), allow one to perform a real-time assessment of an operator's cognitive state. In EEG-based systems, typical measurements for workload level assessment are band power estimates in several frequency bands. Mental fatigue, arising from growing time-on-task (TOT), can significantly affect the distribution of these band power features. However, the impact of mental fatigue on workload (WKL) assessment has not yet been evaluated. With this paper we intend to help fill in this lack of knowledge by analyzing the influence of WKL and TOT on EEG band power features, as well as their interaction and its impact on classification performance. Twenty participants underwent an experiment that modulated both their WKL (low/high) and time spent on the task (short/long). Statistical analyses were performed on the EEG signals, behavioral and subjective data. They revealed opposite changes in alpha power distribution between WKL and TOT conditions, as well as a decrease in WKL level discriminability with increasing TOT in both number of statistical differences in band power and classification performance. Implications for pBCI systems and experimental protocol design are discussed

On Riemannian tools for classification improvement in Brain-Computer Interfaces

A Brain Computer Interface (BCI) or Brain Machine Interface (BMI) is a device that allows the exchange of information between the brain of a person and a computer without the need of physical interaction. This technology promises to change the way in which we interact with machines, but it is not yet affordable, robust or quick enough to substitute other classic human to machine interfaces for the general public. This being said, the lack of need of interaction makes them a very promising solution that would provide people with severe motor disabilities with a new way of interacting with their surroundings, improving their quality of life. The most extended method of extracting information about brain activity and the one used for this project is the Electroencefalogram (EEG). This device consists of multiple electrodes mounted on a helmet-like structure that is placed on the user’s scalp. The electrodes detect the sum of action potentials from large populations of neurons on the brain’s cortex. The main advantages of this technique are the relative low cost of the device, portability, and the high temporal resolution and ease of use of a non invasive technique. This is not free of disadvantages, as the method suffers from a low signal to noise ratio, low robustness to interference, low spatial resolution and the effects of inter and intra session drift, that is, the movement of the electrodes during and between sessions produce variations on the acquisition of the signal. There are also multiple paradigms in the field of BCI, each one of them focusing on a different brain signal. This work is centered around the Motor Imagery Brain Computer Interface (MI-BCI), which differs from other BCIs in the fact that it directly decodes the intention of the user without the need of inducing a specific response in the brain by presenting an stimulus. This approach is considered to be more natural and can be more comfortable, but also requires a higher level of mental effort and proficiency from part of the user. The MI-BCI is based on a signal of unknown origin that is produced on the sensorymotor cortex, responsible for voluntary movements and touch among others, the Sensorimotor Rhythms (SMR). This signal is atenuated when the person performs or thinks about performing a movement, which is called an Event Related Desynchronization (ERD) and amplified when going back to the idling state, an Event Related Synchronization (ERS). As the brain is a distributed system, the origin of these events can be estimated and is related to the movement that the person imagined. In an implementation, these movements are limited to a discrete set of posibilities and each one is mapped to a computer instruction, allowing the unidirectional transfer of information between brain and machine. The classical machine learning approach to this problem has been to use very specific signal processing techniques to extract relevant features for this problem that can then be fed to a general classification algorithm. The main tecnique is known as Common Spatial Patterns (CSP) followed by classification with Linear Discriminant Analysis (LDA) or Support Vector Machine (SVM). This has some advantages such as a relative low requirement of training samples, but also lacks the capability of generalisation, and a system fine tuned for one user cannot be used for other users or even for another session from the same user reliably. In this work we study an alternative framework that uses the covariance matrices of the EEG signals as observations and exploits the Riemannian geometry of Symmetric Positive Definite (SPD) matrices to classify them in their natural space. This is not only a more general signal processing approach that has been used in other fields of research, but also opens the possibility of transfering some information between users and sessions, which may result in a more robust system or in a system that requires less data for training. This is crucial for the usability of MI-BCI because recording a training session before each use of the system is mentally exhausting and time consuming.Universidad de Sevilla. Máster Universitario en Ingeniería de Telecomunicació

Brain-Computer Interfaces using Machine Learning

This thesis explores machine learning models for the analysis and classification of electroencephalographic (EEG) signals used in Brain-Computer Interface (BCI) systems. The goal is 1) to develop a system that allows users to control home-automation devices using their mind, and 2) to investigate whether it is possible to achieve this, using low-cost EEG equipment. The thesis includes both a theoretical and a practical part. In the theoretical part, we overview the underlying principles of Brain-Computer Interface systems, as well as, different approaches for the interpretation and the classification of brain signals. We also discuss the emergent launch of low-cost EEG equipment on the market and its use beyond clinical research. We then dive into more technical details that involve signal processing and classification of EEG patterns using machine leaning. Purpose of the practical part is to create a brain-computer interface that will be able to control a smart home environment. As a first step, we investigate the generalizability of different classification methods, conducting a preliminary study on two public datasets of brain encephalographic data. The obtained accuracy level of classification on 9 different subjects was similar and, in some cases, superior to the reported state of the art. Having achieved relatively good offline classification results during our study, we move on to the last part, designing and implementing an online BCI system using Python. Our system consists of three modules. The first module communicates with the MUSE (a low-cost EEG device) to acquire the EEG signals in real time, the second module process those signals using machine learning techniques and trains a learning model. The model is used by the third module, that takes control of cloud-based home automation devices. Experiments using the MUSE resulted in significantly lower classification results and revealed the limitations of the low-cost EEG signal acquisition device for online BCIs

Online Mental Fatigue Monitoring via Indirect Brain Dynamics Evaluation

Driver mental fatigue leads to thousands of traffic accidents. The increasing quality and availability of low-cost electroencephalogram (EEG) systems offer possibilities for practical fatigue monitoring. However, non-data-driven methods, designed for practical, complex situations, usually rely on handcrafted data statistics of EEG signals. To reduce human involvement, we introduce a data-driven methodology for online mental fatigue detection: self-weight ordinal regression (SWORE). Reaction time (RT), referring to the length of time people take to react to an emergency, is widely considered an objective behavioral measure for mental fatigue state. Since regression methods are sensitive to extreme RTs, we propose an indirect RT estimation based on preferences to explore the relationship between EEG and RT, which generalizes to any scenario when an objective fatigue indicator is available. In particular, SWORE evaluates the noisy EEG signals from multiple channels in terms of two states: shaking state and steady state. Modeling the shaking state can discriminate the reliable channels from the uninformative ones, while modeling the steady state can suppress the task-nonrelevant fluctuation within each channel. In addition, an online generalized Bayesian moment matching (online GBMM) algorithm is proposed to online-calibrate SWORE efficiently per participant. Experimental results with 40 participants show that SWORE can maximally achieve consistent with RT, demonstrating the feasibility and adaptability of our proposed framework in practical mental fatigue estimation