Decoding steady-state visual evoked potentials from electrocorticography

We report on a unique electrocorticography (ECoG) experiment in which Steady-State Visual Evoked Potentials (SSVEPs) to frequency-and phase-tagged stimuli were recorded from a large subdural grid covering the entire right occipital cortex of a human subject. The paradigm is popular in EEG-based Brain Computer Interfacing where selectable targets are encoded by different frequency-and/or phase-tagged stimuli. We compare the performance of two state-of-the-art SSVEP decoders on both ECoG-and scalp-recorded EEG signals, and show that ECoG-based decoding is more accurate for very short stimulation lengths (i.e., less than 1 s). Furthermore, whereas the accuracy of scalp-EEG decoding bene fi ts from a multi-electrode approach, to address interfering EEG responses and noise, ECoG decoding enjoys only a marginal improvement as even a single electrode, placed over the posterior part of the primary visual cortex, seems to suf fi ce. This study shows, for the fi rst time, that EEG-based SSVEP decoders can in principle be applied to ECoG, and can be expected to yield faster decoding speeds using less electrodes

Data Analytics in Steady-State Visual Evoked Potential-based Brain-Computer Interface: A Review

Electroencephalograph (EEG) has been widely applied for brain-computer interface (BCI) which enables paralyzed people to directly communicate with and control of external devices, due to its portability, high temporal resolution, ease of use and low cost. Of various EEG paradigms, steady-state visual evoked potential (SSVEP)-based BCI system which uses multiple visual stimuli (such as LEDs or boxes on a computer screen) flickering at different frequencies has been widely explored in the past decades due to its fast communication rate and high signal-to-noise ratio. In this paper, we review the current research in SSVEP-based BCI, focusing on the data analytics that enables continuous, accurate detection of SSVEPs and thus high information transfer rate. The main technical challenges, including signal pre-processing, spectrum analysis, signal decomposition, spatial filtering in particular canonical correlation analysis and its variations, and classification techniques are described in this paper. Research challenges and opportunities in spontaneous brain activities, mental fatigue, transfer learning as well as hybrid BCI are also discussed

On Tackling Fundamental Constraints in Brain-Computer Interface Decoding via Deep Neural Networks

A Brain-Computer Interface (BCI) is a system that provides a communication and control medium between human cortical signals and external devices, with the primary aim to assist or to be used by patients who suffer from a neuromuscular disease. Despite significant recent progress in the area of BCI, there are numerous shortcomings associated with decoding Electroencephalography-based BCI signals in real-world environments. These include, but are not limited to, the cumbersome nature of the equipment, complications in collecting large quantities of real-world data, the rigid experimentation protocol and the challenges of accurate signal decoding, especially in making a system work in real-time. Hence, the core purpose of this work is to investigate improving the applicability and usability of BCI systems, whilst preserving signal decoding accuracy. Recent advances in Deep Neural Networks (DNN) provide the possibility for signal processing to automatically learn the best representation of a signal, contributing to improved performance even with a noisy input signal. Subsequently, this thesis focuses on the use of novel DNN-based approaches for tackling some of the key underlying constraints within the area of BCI. For example, recent technological improvements in acquisition hardware have made it possible to eliminate the pre-existing rigid experimentation procedure, albeit resulting in noisier signal capture. However, through the use of a DNN-based model, it is possible to preserve the accuracy of the predictions from the decoded signals. Moreover, this research demonstrates that by leveraging DNN-based image and signal understanding, it is feasible to facilitate real-time BCI applications in a natural environment. Additionally, the capability of DNN to generate realistic synthetic data is shown to be a potential solution in reducing the requirement for costly data collection. Work is also performed in addressing the well-known issues regarding subject bias in BCI models by generating data with reduced subject-specific features. The overall contribution of this thesis is to address the key fundamental limitations of BCI systems. This includes the unyielding traditional experimentation procedure, the mandatory extended calibration stage and sustaining accurate signal decoding in real-time. These limitations lead to a fragile BCI system that is demanding to use and only suited for deployment in a controlled laboratory. Overall contributions of this research aim to improve the robustness of BCI systems and enable new applications for use in the real-world

Past, Present, and Future of EEG-Based BCI Applications

An electroencephalography (EEG)-based brain–computer interface (BCI) is a system that provides a pathway between the brain and external devices by interpreting EEG. EEG-based BCI applications have initially been developed for medical purposes, with the aim of facilitating the return of patients to normal life. In addition to the initial aim, EEG-based BCI applications have also gained increasing significance in the non-medical domain, improving the life of healthy people, for instance, by making it more efficient, collaborative and helping develop themselves. The objective of this review is to give a systematic overview of the literature on EEG-based BCI applications from the period of 2009 until 2019. The systematic literature review has been prepared based on three databases PubMed, Web of Science and Scopus. This review was conducted following the PRISMA model. In this review, 202 publications were selected based on specific eligibility criteria. The distribution of the research between the medical and non-medical domain has been analyzed and further categorized into fields of research within the reviewed domains. In this review, the equipment used for gathering EEG data and signal processing methods have also been reviewed. Additionally, current challenges in the field and possibilities for the future have been analyzed

Novas estratégias de pré-processamento, extração de atributos e classificação em sistemas BCI

Orientador: Romis Ribeiro de Faissol AttuxTese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de ComputaçãoResumo: As interfaces cérebro-computador (BCIs) visam controlar um dispositivo externo, utilizando diretamente os sinais cerebrais do usuário. Tais sistemas requerem uma série de etapas para processar e extrair atributos relevantes dos sinais observados para interpretar correta e eficientemente as intenções do usuário. Embora o campo tenha se desenvolvido continuamente e algumas dificuldades tenham sido superadas, ainda é necessário aumentar a capacidade de uso, melhorando sua capacidade de classificação e aumentando a confiabilidade de sua resposta. O objetivo clássico da pesquisa de BCI é apoiar a comunicação e o controle para usuários com comunicação prejudicada devido a doenças ou lesões. Aplicações típicas das BCI são a operação de cursores de interface, programas de escrita de texto ou dispositivos externos, como cadeiras de rodas, robôs e diferentes tipos de próteses. O usuário envia informações moduladas para a BCI, realizando tarefas mentais que produzem padrões cerebrais distintos. A BCI adquire sinais do cérebro do usuário e os traduz em comunicação adequada. Esta tese tem como objetivo desenvolver uma comunicação BCI não invasiva mais rápida e confiável baseada no estudo de diferentes técnicas que atuam nas etapas de processamento do sinal, considerando dois aspectos principais, a abordagem de aprendizado de máquina e a redução da complexidade na tarefa de aprendizado dos padrões mentais pelo usuário. A pesquisa foi focada em dois paradigmas de BCI, Imagética Motora (IM) e o potencial relacionado ao evento P300. Algoritmos de processamento de sinais para a detecção de ambos os padrões cerebrais foram aplicados e avaliados. O aspecto do pré-processamento foi a primeira perspectiva estudada, considerando como destacar a resposta dos fenômenos cerebrais, em relação ao ruído e a outras fontes de informação que talvez distorçam o sinal de EEG; isso em si é um passo que influenciará diretamente a resposta dos seguintes blocos de processamento e classificação. A Análise de Componente Independente (ICA) foi usada em conjunto com métodos de seleção de atributos e diferentes classificadores para separar as fontes originais relacionadas à dessincronização produzida pelo fenômeno de IM; esta foi uma tentativa de criar um tipo de filtro espacial que permitisse o sinal ser pré-processado, reduzindo a influência do ruído. Além disso, os resultados dos valores de classificação foram analisados considerando a comparação com métodos padrão de pré-processamento, como o filtro CAR. Os resultados mostraram que é possível separar os componentes relacionados à atividade motora. A proposta da ICA, em média, foi 4\% mais alta em porcentagem de precisão de classificação do que os resultados obtidos usando o CAR, ou quando nenhum filtro foi usado. O papel dos métodos que estudam a conectividade de diferentes áreas do cérebro foi avaliado como a segunda contribuição deste trabalho; Isso permitiu considerar aspectos que contemplam a complexidade da resposta cerebral de um usuário. A área da BCI precisa de uma interpretação mais profunda do que acontece no nível do cérebro em vários dos fenômenos estudados. A técnica utilizada para construir grafos de conectividade funcional foi a correntropia, esta medida foi utilizada para quantificar a similaridade; uma comparação foi feita usando também, as medidas de correlação de Spearman e Pearson. A conectividade funcional relaciona diferentes áreas do cérebro analisando sua atividade cerebral, de modo que o estudo do grafo foi avaliado utilizando três medidas de centralidade, onde a importância de um nó na rede é medida. Também, dois tipos de classificadores foram testados, comparando os resultados no nível de precisão de classificação. Em conclusão, a correntropia pode trazer mais informações para o estudo da conectividade do que o uso da correlação simples, o que trouxe melhorias nos resultados da classificação, especialmente quando ela foi utilizada com o classificador ELM. Finalmente, esta tese demonstra que os BCIs podem fornecer comunicação efetiva em uma aplicação onde a predição da resposta de classificação foi modelada, o que permitiu a otimização dos parâmetros do processamento de sinal realizado usando o filtro espacial xDAWN e um classificador FLDA para o problema do speller P300, buscando a melhor resposta para cada usuário. O modelo de predição utilizado foi Bayesiano e confirmou os resultados obtidos com a operação on-line do sistema, permitindo otimizar os parâmetros tanto do filtro quanto do classificador. Desta forma, foi visto que usando filtros com poucos canais de entrada, o modelo otimizado deu melhores resultados de acurácia de classificação do que os valores inicialmente obtidos ao treinar o filtro xDAWN para os mesmos casos. Os resultados obtidos mostraram que melhorias nos métodos do transdutor BCI, no pré-processamento, extração de características e classificação constituíram a base para alcançar uma comunicação BCI mais rápida e confiável. O avanço nos resultados da classificação foi obtido em todos os casos, comparado às técnicas que têm sido amplamente utilizadas e já mostraram eficácia para esse tipo de problema. No entanto, ainda há aspectos a considerar da resposta dos sujeitos para tipos específicos de paradigmas, lembrando que sua resposta pode variar ao longo de diferentes dias e as implicações reais disso na definição e no uso de diferentes métodos de processamento de sinalAbstract: Brain-computer interfaces (BCIs) aim to control an external device by directly employing user's brain signals. Such systems require a series of steps to process and extract relevant features from the observed signals to correctly and efficiently interpret the user's intentions. Although the field has been continuously developing and some difficulties have been overcome, it is still necessary to increase usability by enhancing their classification capacity and increasing the reliability of their response. The classical objective of BCI research is to support communication and control for users with impaired communication due to illness or injury. Typical BCI applications are the operation of interface cursors, spelling programs or external devices, such as wheelchairs, robots and different types of prostheses. The user sends modulated information to the BCI by engaging in mental tasks that produce distinct brain patterns. The BCI acquires signals from the user¿s brain and translates them into suitable communication. This thesis aims to develop faster and more reliable non-invasive BCI communication based on the study of different techniques that serve in the signal processing stages, considering two principal aspects, the machine learning approach, and the reduction of the complexity in the task of learning the mental patterns by the user. Research was focused on two BCI paradigms, Motor Imagery (MI) and the P300 event related potential (ERP). Signal processing algorithms for the detection of both brain patterns were applied and evaluated. The aspect of the pre-processing was the first perspective studied to consider how to highlight the response of brain phenomena, in relation to noise and other sources of information that maybe distorting the EEG signal; this in itself is a step that will directly influence the response of the following blocks of processing and classification. The Independent Component Analysis (ICA) was used in conjunction with feature selection methods and different classifiers to separate the original sources that are related to the desynchronization produced by MI phenomenon; an attempt was made to create a type of spatial filter that pre-processed the signal, reducing the influence of the noise. Furthermore, some of the classifications values were analyzed considering comparison when used other standard pre-processing methods, as the CAR filter. The results showed that it is possible to separate the components related to motor activity. The ICA proposal on average were 4\% higher in percent of classification accuracy than those obtained using CAR, or when no filter was used. The role of methods that study the connectivity of different brain areas were evaluated as the second contribution of this work; this allowed to consider aspects that contemplate the complexity of the brain response of a user. The area of BCI needs a deeper interpretation of what happens at the brain level in several of the studied phenomena. The technique used to build functional connectivity graphs was correntropy, this quantity was used to measure similarity, a comparison was made using also, the Spearman and Pearson correlation. Functional connectivity relates different brain areas activity, so the study of the graph was evaluated using three measures of centrality of graph, where the importance of a node in the network is measured. In addition, two types of classifiers were tested, comparing the results at the level of classification precision. In conclusion, the correntropy can bring more information for the study of connectivity than the use of the simple correlation, which brought improvements in the classification results especially when it was used with the ELM classifier. Finally, this thesis demonstrates that BCIs can provide effective communication in an application where the prediction of the classification response was modeled, which allowed the optimization of the parameters of the signal processing performed using the xDAWN spatial filter and a FLDA classifier for the problem of the P300 speller, seeking the best response for each user. The prediction model used was Bayesian and confirmed the results obtained with the on-line operation of the system, thus allowing to optimize the parameters of both the filter and the classifier. In this way it was seen that using filters with few inputs the optimized model gave better results of acuraccy classification than the values initially obtained when the training ofthe xDAWN filter was made for the same cases. The obtained results showed that improvements in the BCI transducer, pre-processing, feature extraction and classification methods constituted the basis to achieve faster and more reliable BCI communication. The advance in the classification results were obtained in all cases, compared to techniques that have been widely used and had already shown effectiveness for this type of problemsDoutoradoEngenharia de ComputaçãoDoutora em Engenharia Elétrica153311/2014-2CNP

A machine learning approach to taking EEG-based brain-computer interfaces out of the lab

Despite being a subject of study for almost three decades, non-invasive brain- computer interfaces (BCIs) are still trapped in the laboratory. In order to move into more common use, it is necessary to have systems that can be reliably used over time with a minimum of retraining. My research focuses on machine learning methods to minimize necessary retraining, as well as a data science approach to validate processing pipelines more robustly. Via a probabilistic transfer learning method that scales well to large amounts of data in high dimensions it is possible to reduce the amount of calibration data needed for optimal performance. However, a good model still requires reliable features that are resistant to recording artifacts. To this end we have also investigated a novel feature of the electroencephalogram which is predictive of multiple types of brain-related activity. As cognitive neuroscience literature suggests, shifts in the peak frequency of a neural oscillation – hereafter referred to as frequency modulation – can be predictive of activity in standard BCI tasks, which we validate for the first time in multiple paradigms. Finally, in order to test the robustness of our techniques, we have built a codebase for reliable comparison of pipelines across over fifteen open access EEG datasets

Adaptive nonlinear multivariate brain connectivity analysis of motor imagery movements using graph theory

Recent studies on motor imagery (MI)-based brain computer interaction (BCI) reported that the interaction of spatially separated brain areas in forms of functional or effective connectivity leads to a better insight of brain neural patterns during MI movements and can provide useful features for BCIs. However, existing studies suffer from unrealistic assumptions or technical weaknesses for processing brain signals, such as stationarity, linearity and bivariate analysis framework. Besides, volume conduction effect as a critical challenge in this area and the role of subcortical regions in connectivity analysis have not been considered and studied well. In this thesis, the neurophysiological connectivity patterns of healthy human brain during different MI movements are deeply investigated. At first, an adaptive nonlinear multivariate statespace model known as dual extended Kalman filter is proposed for connectivity pattern estimation. Several frequency domain functional and effective connectivity estimators are developed for nonlinear non-stationary signals. Evaluation results show superior parameter tracking performance and hence more accurate connectivity analysis by the proposed model. Secondly, source-space time-varying nonlinear multivariate brain connectivity during feet, left hand, right hand and tongue MI movements is investigated in a broad frequency range by using the developed connectivity estimators. Results reveal the similarities and the differences between MI tasks in terms of involved regions, density of interactions, distribution of interactions, functional connections and information flows. Finally, organizational principles of brain networks of MI movements measured by all considered connectivity estimators are extensively explored by graph theoretical approach where the local and global graph structures are quantified by computing different graph indexes. Results report statistical significant differences between and within the MI tasks by using the graph indexes extracted from the networks formed particularly by normalized partial directed coherence. This delivers promising distinctive features of the MI tasks for non-invasive BCI applications