Research on P300 Mode Enhancement and Recognition for Portable Brain Computer Interface

脑机接口（Braincomputerinterface，BCI）是一种使人可以通过脑电信号与外部环境进行直接交互的技术。它不依赖于肌肉或神经系统，而是通过特定模式的脑电和外部的辅助设备，与外界完成信息交互，为人类通过用意识去控制和改变外界环境提供了一种可行方案。 近年来，脑机接口的研究进入了快速发展时期。一些基于EEG成分（如P300、SSVEP、Mu波等）的BCI研究取得了较大的进步。安全、实时、操作简单的脑机接口技术正逐步走出实验室。在这种背景下，如何将脑机接口技术应用于现实生活成为目前研究的热点和难点之一，也是BCI研究未来发展的一个重要方向。 传统的脑电采集设备价格昂贵、采集时间长...Brain computer interface (BCI) is a way to enable people to interact with the external environment by analyzing their EEG(Electroencephalography) signals. It does not rely on the muscles or the nervous system itself, but realizes the interaction between the human brain with the outside world through EEG and external auxiliary equipment, that is to say, the brain computer interface provides a possi...学位：工程硕士院系专业：信息科学与技术学院_工程硕士(计算机技术)学号：3152014115330

Carbon fiber electrodes for in vivo neural recording

Multi-channel micro electrodes for neural recording is a growing field that thrives on novel materials and fabrication techniques offered by micro fabrication technology. The material and the design of microelectrodes have a critical role on the quality of neural signals recorded. The neural signals collected by chronic implantation of these devices in experimental animals reveal new information about the brain functions and guide the development of new diagnostic and treatment options for neurological disorders. Ideally, a microelectrode should meet two important criteria: longevity after implantation and minimal tissue insult. Carbon fibers` high tensile strength and flexibility allow fabrication of micro-scale electrodes that can withstand mechanical challenges in mobile parts of the CNS. Although there are studies showing carbon fibers’ superior qualities as a potential electrode material, these studies are mostly restricted to the brain cortex. There is a need for microelectrode designs that can survive long implantation times in the moving parts of the CNS like the spinal cord. In this study, carbon fiber microelectrode (CFME) bundles were developed and tested in the spinal cord of experimental animals for neural recording. Neural data analysis revealed that desheathing the tips of the fibers decreased spike counts, but increased signal-to-noise ratios. Triple carbon fibers in parallel did not improve the signal quality as much as desheathing. Lastly, immunohistochemistry showed that electrode tips were splayed in tissue after implantation and each had a small footprint with mild encapsulation around. These results are very promising for the use of carbon fiber bundle electrodes for chronic neural recording in survival studies

AI as IA: The use and abuse of artificial intelligence (AI) for human enhancement through intellectual augmentation (IA)

This paper offers an overview of the prospects and ethics of using AI to achieve human enhancement, and more broadly what we call intellectual augmentation (IA). After explaining the central notions of human enhancement, IA, and AI, we discuss the state of the art in terms of the main technologies for IA, with or without brain-computer interfaces. Given this picture, we discuss potential ethical problems, namely inadequate performance, safety, coercion and manipulation, privacy, cognitive liberty, authenticity, and fairness in more detail. We conclude that while there are very significant technical hurdles to real human enhancement through AI, and significant ethical problems, there are also significant benefits that may realistically be achieved in ways that are consonant with a rights-based ethics as well. We also highlight the specific concerns that apply particularly to applications of AI for "sheer" IA (more realistic in the near term), and to enhancement applications, respectively

Emerging ExG-based NUI Inputs in Extended Realities : A Bottom-up Survey

Incremental and quantitative improvements of two-way interactions with extended realities (XR) are contributing toward a qualitative leap into a state of XR ecosystems being efficient, user-friendly, and widely adopted. However, there are multiple barriers on the way toward the omnipresence of XR; among them are the following: computational and power limitations of portable hardware, social acceptance of novel interaction protocols, and usability and efficiency of interfaces. In this article, we overview and analyse novel natural user interfaces based on sensing electrical bio-signals that can be leveraged to tackle the challenges of XR input interactions. Electroencephalography-based brain-machine interfaces that enable thought-only hands-free interaction, myoelectric input methods that track body gestures employing electromyography, and gaze-tracking electrooculography input interfaces are the examples of electrical bio-signal sensing technologies united under a collective concept of ExG. ExG signal acquisition modalities provide a way to interact with computing systems using natural intuitive actions enriching interactions with XR. This survey will provide a bottom-up overview starting from (i) underlying biological aspects and signal acquisition techniques, (ii) ExG hardware solutions, (iii) ExG-enabled applications, (iv) discussion on social acceptance of such applications and technologies, as well as (v) research challenges, application directions, and open problems; evidencing the benefits that ExG-based Natural User Interfaces inputs can introduceto the areaof XR.Peer reviewe

Emerging ExG-based NUI Inputs in Extended Realities : A Bottom-up Survey

Incremental and quantitative improvements of two-way interactions with extended realities (XR) are contributing toward a qualitative leap into a state of XR ecosystems being efficient, user-friendly, and widely adopted. However, there are multiple barriers on the way toward the omnipresence of XR; among them are the following: computational and power limitations of portable hardware, social acceptance of novel interaction protocols, and usability and efficiency of interfaces. In this article, we overview and analyse novel natural user interfaces based on sensing electrical bio-signals that can be leveraged to tackle the challenges of XR input interactions. Electroencephalography-based brain-machine interfaces that enable thought-only hands-free interaction, myoelectric input methods that track body gestures employing electromyography, and gaze-tracking electrooculography input interfaces are the examples of electrical bio-signal sensing technologies united under a collective concept of ExG. ExG signal acquisition modalities provide a way to interact with computing systems using natural intuitive actions enriching interactions with XR. This survey will provide a bottom-up overview starting from (i) underlying biological aspects and signal acquisition techniques, (ii) ExG hardware solutions, (iii) ExG-enabled applications, (iv) discussion on social acceptance of such applications and technologies, as well as (v) research challenges, application directions, and open problems; evidencing the benefits that ExG-based Natural User Interfaces inputs can introduceto the areaof XR.Peer reviewe

Interface Cérebro Máquina de Baixo Custo como Tecnologia Assistiva

TCC(graduação) - Universidade Federal de Santa Catarina. Campus Araranguá. Engenharia da Computação.A presente monografia apresenta a fundamentação e o desenvolvimento de um sistema de comunicação direta de baixo custo entre o cérebro humano e um computador. Utilizando como base o dispositivo Emotiv Insight, é possível capturar, analisar e interpretar a semântica dos sinais do cérebro humano e mapear esses sinais em ações num computador. Como prova de conceito, propõe-se um protótipo onde o usuário do sistema possa comandar um teclado virtual apenas através de seus sinais cerebrais. O mérito do trabalho, além é claro do conhecimento adquirido nesta área da fronteira do conhecimento humano, é permitir que indivíduos com deficiências motoras severas possam ter uma melhor qualidade de vida ao serem capazes de interagir com seu as pessoas e o ambiente. Na elaboração do trabalho são apresentados e descritas as interfaces cérebro máquina (\textit{Brain Computer Interface} - BCI), o sensor Insight é analisado e avaliado quantitativamente e, finalmente, os algoritmos e a arquitetura do teclado virtual elaborado são explicitados

Desenvolvimento e avaliação de um sistema de espectroscopia funcional de infravermelho próximo para detecção de movimento intencional com base na atividade cerebral

Dissertação (Mestrado em Engenharia Biomédica)–Programa de pós-graduação em Engenharia Biomédica, Universidade de Brasília, Brasília, 2020.Pessoas que possuem doenças do neurônio motor têm dificuldades de interagir e de se comunicar com o ambiente ao seu redor. Umas das doenças do neurônio motor mais comuns é a Esclerose Lateral Amiotrófica (ELA), em que os acometidos pela doença perdem a capacidade de se comunicar verbalmente. Em um estágio avançado da ELA, chamado de Síndrome do Encarceramento Total, do inglês Complete Locked-In State (CLIS), os pacientes perdem o controle de todas as resposta musculares voluntárias, porém possuem um estado de consciência normal. Uma das alternativas para pessoas que possuem essas síndromes é a utilização de uma Interface Cérebro Computador (ICC) como mecanismo de comunicação. ICCs são sistemas eletrônicos que tentam discernir padrões em sinais de atividades encefalográficas para utilizá-los como auxílio à seres humanos com mobilidades reduzidas. Dentre as técnicas utilizadas para captar esses sinais encefalográficos, a Espectroscopia Funcional de Infravermelho Próximo, do inglês functional Near Infrared Spectroscopy (fNIRS), tem sido objeto crescente de estudo nos últimos anos. A fNIRS é uma técnica não-invasiva, que utiliza uma abordagem óptica para adquirir tais sinais. Seu principio baseia-se em mensurar as taxas de oxigenação e desoxigenação do fluxo sanguíneo no córtex cerebral. Ela situa-se em um meio termo entre as técnicas de Eletroencefalografia (EEG) e Imagem por Ressonância Magnética Funcional, do inglês functional Magnetic Resonance Imaging (fMRI), porém com maior flexibilidade e menor risco à saúde de quem a utiliza. Nesse contexto, a pesquisa propôs a desenvolver um sistema eletrônico de aquisição multicanal de sinais de fNIRS, e avaliá-lo em um cenário de classificação de sinais reais de humanos, buscando diferenciar movimentos intencionais de não-intencionais. A metodologia consistiu em projetar a instrumentação de aquisição, implementar um modelo de classificação SVM, e coletar sinais do córtex cerebral de humanos, com base em um protocolo experimental aprovado por um Comitê de Ética. O processo de classificação foi realizado utilizando um modelo preditivo do tipo SVM, com kernel gaussiano. Para estimar melhor as métricas de desempenho do modelo, foi utilizada a técnica de validação cruzada K-Fold, com k=5. Ao todo foram avaliados três cenários distintos de classificação. Participaram das coletas, no total, cinco voluntários. Cada sessão de coleta teve duração de 6 minutos, onde cada participante foi instruído a passar metade do tempo em repouso e a outra metade realizando movimentos sequenciais com as mãos. O primeiro consistiu em adquirir sinais com apenas um canal, formado por uma topologia simples de uma fonte de luz com um fotodetector. Os sinais foram coletados em duas sessões em um mesmo dia, com condições de iluminação controladas. Cada sessão utilizou uma fonte de emissão distinta uma da outra. A acurácia média obtida ficou superior a 90% para os dois participantes. O segundo experimento avaliou um cenário de classificação com a aquisição de 10 canais simultaneamente, adquiridos com 3 voluntários, em um mesmo dia cada. Os 10 canais foram gerados utilizando duas fontes de emissão em conjunto com cinco fotodetectores. A acurácia média para esse cenário foi de 98%, indicando que o modelo conseguiu discernir bem a presença da ausência de movimento. O último experimento teve como objetivo avaliar o desempenho de classificação com sinais coletados em dias distintos para um mesmo participante, simulando condições de iluminação distintas. Para tal, foram repetidas as coletas com os últimos 3 voluntários, dois dias após às primeiras sessões. O modelo foi treinado com os sinais da primeira sessão, e a inferência foi feita com os sinais da segunda sessão. Nesse cenário, as métricas de desempenho obtidas revelaram que não foi possível discernir com boa acurácia as classes avaliadas. No geral, os resultados obtidos com os experimentos foram similares aos de trabalhos da literatura levantada, validando o sistema de aquisição implementado.People who suffers from motor neuron diseases have difficulties to interact and communicate with the environment around them. One of the most common motor neuron diseases is Amyotrophic Lateral Sclerosis (ALS), in which those affected by the disease lose the ability to communicate verbally. In an advanced stage of ALS, called Complete Locked-In State (CLIS), patients lose control of all voluntary muscle responses, but still have a normal conscious state. One of the possible alternatives for people who have these syndromes is a Brain Computer Interface (BCI), for use as a communication mechanism. BCIs are electronic systems that try to discern patterns in signals of encephalographic activities, using these patterns as an aid to humans with reduced mobility. Among the techniques used to capture these encephalographic signals, the functional Near Infrared Spectroscopy (fNIRS) has been an object of increasing study in recent years. fNIRS is a non-invasive technique that uses an optical approach to acquire such signals. Its principle is based on measuring the oxygenation and deoxygenation rates of blood flow in the cerebral cortex. It situates between the techniques of Electroencephalography (EEG) and functional Magnetic Resonance Imaging (fMRI), but with greater flexibility and less risk to the health of those who use it. In this context, the research proposed to develop an electronic system for multichannel acquisition of fNIRS signals, and to evaluate it in a scenario of classification of real human signals, seeking to differentiate intentional from unintentional movements. The methodology consisted of designing the acquisition instrumentation, implementing an SVM classification model, and collecting signals from the human cerebral cortex, based on an experimental protocol approved by a Human Ethics Committee. The classification process was performed using a predictive model of the SVM type, with Gaussian kernel. To better estimate the model's performance metrics, the K-Fold cross-validation technique was used, with k=5. Three different classification scenarios were evaluated, and five volunteers participated of the experiments. Each acquisition session lasted 6 minutes, and each participant was instructed to spend half the time at rest and the other half to perform sequential hand movements. The first consisted of acquiring signals with only one channel, formed by a simple topology of one light source with one photodetector. The signals were collected in two sessions on the same day, with controlled lighting conditions. Each session used a different emission source. The average accuracy obtained was greater than 90% for the two participants. The second experiment evaluated a classification scenario with the acquisition simultaneously of 10 channels, acquired with 3 volunteers, on the same day each. The 10 channels were generated using two emission sources together with five photodetectors. The average accuracy for this scenario was 98%, indicating that the model was able to discern well the presence of the absence of movement. The last experiment aimed to evaluate the classification performance with signals collected on different days for the same participant, simulating different lighting conditions. For this, collections were repeated with the last 3 volunteers, two days after the first sessions. The model was trained with the signals from the first session, and the inference was made with the signals from the second session. In this scenario, the performance metrics obtained revealed that it was not possible to discern the evaluated classes, with the adopted methodology. In general, the results obtained with the experiments were similar to those of studies in the literature, validating the implemented acquisition system

Uso de características espectrales y temporales para clasificación de tareas mentales en señales de electroencefalografía

Tesis (Magíster en Neurorehabilitación)Este estudio busca validar un método de clasificación de tareas mentales a partir de la extracción de características de una señal de electroencefalografía, lo que permitiría su implementación en interfaz cerebro-computador (ICC). Para esto se utilizará una base de datos propia obtenida a partir de los registros de un grupo de personas sanas, las cuales desarrollan tareas mentales contrapuestas. A cada señal se le extraerán un set de características, las que serán usadas para entrenar y validar un grupo de clasificadores, con el objetivo de reconocer una tarea de imaginería motora determinada. El objetivo de este proyecto es desarrollar un método que permita identificar las tareas mentales a partir de trabajos de imaginería motora, obtenidos a partir del registro de electroencefalografía (EEG). En este estudio, se buscó determinar si el uso de las características seleccionadas de la señal de electroencefalograma permite una correcta clasificación con indicadores de especificidad, sensibilidad, valor predictivo positivo, valor predictivo negativo y precisión; y además se usaron tres clasificadores de distintos tipos para validar este procedimiento y para determinar cuál de ellos tenía un mejor desempeño frente a estas características específicas. Las características seleccionadas en el dominio del tiempo fueron: Desviación estándar, varianza, media, moda, mediana, kurtosis, Skewness. En el dominio de la frecuencia se utilizaron la frecuencia máxima, frecuencia mediana, frecuencia media y fase. Los clasificadores utilizados para este estudio fueron del tipo Naive Bayes, SMO y Dagging. El método desarrollado mostró, dependiendo del clasificador, valores promedio altos de especificidad, sensibilidad y precisión. Para el clasificador Naive Bayes se obtuvieron valores promedio de Sensibilidad de 0,6; Especificidad 0,7 y Precisión 0,6. Para el clasificador SMO la Sensibilidad es de 0,8; la Especificidad de 0,8 y la Precisión de 0,7. Para el Clasificador Dagging el valor promedio de Sensibilidad fue de 0,7; el de Especificidad de 0,8 y el de Precisión de 0,7. Además al graficar la curva ROC se obtiene como resultado que el mejor clasificador para este tipo de características es el de tipo SMO. En conclusión, el método desarrollado en este estudio fue capaz de diferenciar dos tareas mentales, por lo que podría ser usado en interfaz cerebro-computador. Además, los valores de sensibilidad, especificidad, valores predictivos positivos y negativos y la presición son valores óptimos en comparación al estado del arte, que presentan valores de presición cercanos al 70% en las distintas modalidades más utilizadas (Mohd Zaizu Ilyas, 2015). Y finalmente se pudo comprobar que el clasificador que mejor desempeño tiene frente a estas caracteristicas seleccionadas es el de tipo SMO

Developing new techniques to analyse and classify EEG signals

A massive amount of biomedical time series data such as Electroencephalograph (EEG), electrocardiography (ECG), Electromyography (EMG) signals are recorded daily to monitor human performance and diagnose different brain diseases. Effectively and accurately analysing these biomedical records is considered a challenge for researchers. Developing new techniques to analyse and classify these signals can help manage, inspect and diagnose these signals. In this thesis novel methods are proposed for EEG signals classification and analysis based on complex networks, a statistical model and spectral graph wavelet transform. Different complex networks attributes were employed and studied in this thesis to investigate the main relationship between behaviours of EEG signals and changes in networks attributes. Three types of EEG signals were investigated and analysed; sleep stages, epileptic and anaesthesia. The obtained results demonstrated the effectiveness of the proposed methods for analysing these three EEG signals types. The methods developed were applied to score sleep stages EEG signals, and to analyse epileptic, as well as anaesthesia EEG signals. The outcomes of the project will help support experts in the relevant medical fields and decrease the cost of diagnosing brain diseases