On the Usage of Linear Regression Models to Reconstruct Limb Kinematics from Low Frequency EEG Signals

Several works have reported on the reconstruction of 2D/3D limb kinematics from low-frequency EEG signals using linear regression models based on positive correlation values between the recorded and the reconstructed trajectories. This paper describes the mathematical properties of the linear model and the correlation evaluation metric that may lead to a misinterpretation of the results of this type of decoders. Firstly, the use of a linear regression model to adjust the two temporal signals (EEG and velocity profiles) implies that the relevant component of the signal used for decoding (EEG) has to be in the same frequency range as the signal to be decoded (velocity profiles). Secondly, the use of a correlation to evaluate the fitting of two trajectories could lead to overly-optimistic results as this metric is invariant to scale. Also, the correlation has a non-linear nature that leads to higher values for sinus/cosinus-like signals at low frequencies. Analysis of these properties on the reconstruction results was carried out through an experiment performed in line with previous studies, where healthy participants executed predefined reaching movements of the hand in 3D space. While the correlations of limb velocity profiles reconstructed from low-frequency EEG were comparable to studies in this domain, a systematic statistical analysis revealed that these results were not above the chance level. The empirical chance level was estimated using random assignments of recorded velocity profiles and EEG signals, as well as combinations of randomly generated synthetic EEG with recorded velocity profiles and recorded EEG with randomly generated synthetic velocity profiles. The analysis shows that the positive correlation results in this experiment cannot be used as an indicator of successful trajectory reconstruction based on a neural correlate. Several directions are herein discussed to address the misinterpretation of results as well as the implications on previous invasive and non-invasive works

Determining Patterns in Neural Activity for Reaching Movements Using Nonnegative Matrix Factorization

We propose the use of nonnegative matrix factorization (NMF) as a model-independent methodology to analyze neural activity. We demonstrate that, using this technique, it is possible to identify local spatiotemporal patterns of neural activity in the form of sparse basis vectors. In addition, the sparseness of these bases can help infer correlations between cortical firing patterns and behavior. We demonstrate the utility of this approach using neural recordings collected in a brain-machine interface (BMI) setting. The results indicate that, using the NMF analysis, it is possible to improve the performance of BMI models through appropriate pruning of inputs

Determining Patterns in Neural Activity for Reaching Movements Using Nonnegative Matrix Factorization

We propose the use of nonnegative matrix factorization (NMF) as a model-independent methodology to analyze neural activity. We demonstrate that, using this technique, it is possible to identify local spatiotemporal patterns of neural activity in the form of sparse basis vectors. In addition, the sparseness of these bases can help infer correlations between cortical firing patterns and behavior. We demonstrate the utility of this approach using neural recordings collected in a brain-machine interface (BMI) setting. The results indicate that, using the NMF analysis, it is possible to improve the performance of BMI models through appropriate pruning of inputs.</p

Аналіз електроенцефалограм людини, отриманих під час емоційних стимулів

Об’єктом розгляду є електрична активність головного мозку людини. Предмет роботи – методи аналізу електроенцефалограм під час дії різноманітних стимулів. Метою роботи є вивчення природи виникнення електричних сигналів мозку, методи їх реєстрації та аналізу для дослідження реакції на візуальні емоційнонавантажені стимули. У першому розділі описуються загальні поняття про природу виникнення електричного сигналу мозку людини, а також нейрофізіологічні ознаки присутності різних частотних складових сигналу за певних станів людини. У другому розділі наведено принципи реєстрації сигналів електроенцефалограми (ЕЕГ) та описано пристрої, що здатні це виконувати. Також розглянуто опис основної системи накладання сенсорів (електродів) на голову людини. В кінці розділу наведено приклад компактного 8-канального енцефалографа власної розробки, що здатен реєструвати сигнали ЕЕГ та передавати їх по бездротовому зв’язку на мобільні прилади (смартфон, планшет). Третій розділ описує основні математичні методи аналізу ЕЕГ сигналів. Основними є методи спектрального та вейвлет-аналізу та аналіз детрендових коливань, за допомогою яких можна отримати детальне представлення про роботу мозку, шляхом виявлення різноманітних патернів в частотних діапазонах. У четвертому розділі описується практичне застосування методів спектрального та Detrended Moving Average аналізів на експериментальній базі даних ЕЕГ для 48 здорових волонтерів, запис ЕЕГ для яких проводився під час демонстрації певних емоційнонавантажених візуальних стимулів. Також в цьому розділі наведені результати виконаного аналізу разом з їх нейрофізіологічним тлумаченням.An important place in the study of brain activity is occupied by the study of its electrical potentials. Electroencephalography (EEG) is a method of graphical recording of brain biopotentials, which allows analyzing its physiological maturity and condition, the presence of focal lesions, general brain disorders and their nature. It consists of recording and analyzing the total bioelectric activity of the brain — an electroencephalogram (EEG). EEG can be taken from the scalp, from the surface of the brain, as well as from deep brain structures. As a rule, an electroencephalogram is understood as a surface recording, that is, made from the intact head surface. EEG is most often used to diagnose epilepsy, which causes EEG disorders. It is also used to diagnose sleep disorders, deep anesthesia, coma, encephalopathy, and brain death. EEG was used as the main method for diagnosing tumors, stroke, and other focal brain diseases, but when it became possible to obtain high-resolution anatomical images using magnetic resonance imaging (MRI) and computed tomography (CT) techniques, the use of EEG declined. Despite its limited resolution, the EEG continues to be a valuable tool for research and diagnosis. The object of consideration is the electrical activity of the human brain. The subject of the work is methods of analyzing electroencephalograms during the action of various stimuli. The aim of the work is to study the nature of the occurrence of electrical signals of the brain, methods of their registration and analysis to study the response to visual emotional stimuli. The first chapter describes general concepts about the nature of the occurrence of an electrical signal in the human brain, as well as neurophysiological signs of the presence of various frequency components of the signal in certain human states. The second chapter describes the principles of recording electroencephalogram signals and describes devices that can perform this. The description of the main system for applying sensors (electrodes) to the human head is also considered. At the end of the section, an example of a compact 8-channel encephalograph of our own design is given, which is able to register EEG signals and transmit them wirelessly to mobile devices (smartphone, tablet). The third section describes the basic mathematical methods for analyzing EEG signals. The main methods are spectral and wavelet analysis and detrended oscillation analysis, which can be used to get a detailed picture of brain function by identifying various patterns in frequency ranges. The fourth section describes the practical application of spectral and Detrended Moving Average analysis methods on an experimental EEG database. Here, initially the EEG records were made for 48 healthy volunteers whose EEG recording was performed while demonstrating certain emotionally loaded visual stimuli. Stimuli were selected from the International Affective Pictures System (IAPS) based on their average emotional valence values. In order to assess the induced changes of the brain’s electrical activity, the EEG-bands were subdivided in a following way: 1 [3.5, 5.8], 2 [5.9, 7.4], 1 [7.5, 9.4], 2 [9.5, 10.7], 3 [10.8, 13.5], 1 [13.6, 25], 2 [25.1, 40] Hz. As a result, Power Spectral Density (PSD) were visualized as a map on the schematic figure of the head used to render the statistical significance test, demonstrating that variations in powers for our signals were caused by non-identical forms of visual effect rather than being an accident. These details were also shown in the heads charts. The study of changes in power spectrum density showed neurodynamics triggered by visual stimulation experience. However, when comparing PSD values obtained during the presentation of the first and second neutral series, it was discovered that when processing neutral images followed by negative stimuli, a well-defined activation focus developed in the left parietal region of the cortex in the 2 subband. The DMA algorithm revealed statistically important variations in the left temporal and frontal regions of the cortex, which were marked by more pronounced activation during the perception of neutral faces in the presence of positive images. This may be the start of a new path of improved inner focus and meaningful emotional experiences. As a result, the sex-related aspects of the emotional valence effect on neutral face perception were discovered by analyzing EEG-based brain neurodynamics in the mechanism in perception in human faces of various modalities. The stimulation of two large cognitive networks in the brain: mental or theta-network and cognitive beta- network, was the key distinction

Desarrollo de una herramienta basada en redes de asociación para la ayuda al diagnóstico de la enfermedad del Alzheimer

La enfermedad de Alzheimer (EA) es el tipo de demencia más común en el mundo occidental y se espera que su prevalencia vaya en aumento como resultado del incremento en la esperanza de vida. Está asociada a un deterioro de las funciones cognitivas que afectan significativamente a la calidad de vida de las personas que la sufren. Obtener un diagnóstico temprano es clave para alargar la autonomía del paciente, darle tiempo para tomar decisiones acerca de su futuro y reducir significativamente los costes asociados a la enfermedad. Al no existir un biomarcador de la EA fácilmente accesible en la práctica clínica diaria, los especialistas en neurología se ven obligados a recurrir a un diagnóstico clínico, en base a los síntomas que presenta el paciente, una exploración neurológica y múltiples pruebas complementarias indicadas en el consenso médico vigente. Esto hace del diagnóstico un proceso complejo y con cierto grado de subjetividad. Además, algunas de estas pruebas tienen un coste elevado, no se encuentran ampliamente disponibles o son invasivas. Por lo tanto, la realización de pruebas a toda la población de riesgo para obtener un diagnóstico temprano es insostenible, tanto organizativa como económicamente. El objetivo de este Trabajo Fin de Grado consiste en encontrar nuevas aproximaciones metodológicas que permitan obtener un diagnóstico más económico, sencillo, objetivo, no invasivo y fácilmente escalable a toda la población de riesgo. Para ello, se han establecido cuatro sistemas de clasificación: (i) sujetos patológicos vs. no patológicos, (ii) controles vs. enfermos de Alzheimer, (iii) controles vs. pacientes con deterioro cognitivo leve vs. enfermos de Alzheimer, y (iv) controles vs. pacientes con deterioro cognitivo leve vs. enfermos de Alzheimer con distinto grado de severidad (leve, moderada y severa). Para cada sistema, en función de las variables incluidas, se distinguen tres modelos: reducido, ampliado y completo.Alzheimer’s disease (AD) is the most common type of dementia in the Western world and its prevalence is expected to grow because of prolongation in the average lifespan. It is associated with a deterioration of the cognitive functions that significantly affects the quality of life of patients. Early diagnosis is critical to extending patients’ autonomy, giving them time to make decisions about their future and thus reducing the costs associated with the disease. In the absence of an accessible AD biomarker in clinical practice, neurologists use a clinical diagnosis based on symptoms, neurological exploration, and multiple complementary tests prescribed by the current medical consensus. This makes diagnosis a complex and subjective process. Furthermore, some of these tests are expensive, poorly available, or invasive. Therefore, testing the entire at-risk population for an early diagnosis is not sustainable, neither economically nor organisationally. The objective of this final degree project is to find new methodological approaches to obtain a less expensive, simpler, more objective, and non-invasive diagnosis which is scalable to the entire risk population. For this purpose, four classification systems have been established: (i) pathological subjects vs. non-pathological, (ii) controls vs. AD patients, (iii) controls vs. mild cognitive impairment patients vs. AD patients, and (iv) controls vs. mild cognitive impairment patients vs. AD patients with different severity degrees (mild, moderate, and severe). For each system, depending on the variables included, we can distinguish three models: reduced, extended, and complete.Grado en Ingeniería de Tecnologías de Telecomunicació

De animais a máquinas : humanos tecnicamente melhores nos imaginários de futuro da convergência tecnológica

Dissertação (mestrado)—Universidade de Brasília, Instituto de Ciências Sociais, Departamento de Sociologia, 2020.O tema desta investigação é discutir os imaginários sociais de ciência e tecnologia que emergem a partir da área da neuroengenharia, em sua relação com a Convergência Tecnológica de quatro disciplinas: Nanotecnologia, Biotecnologia, tecnologias da Informação e tecnologias Cognitivas - neurociências- (CT-NBIC). Estas áreas desenvolvem-se e são articuladas por meio de discursos que ressaltam o aprimoramento das capacidades físicas e cognitivas dos seres humanos, com o intuito de construir uma sociedade melhor por meio do progresso científico e tecnológico, nos limites das agendas de pesquisa e desenvolvimento (P&D). Objetivos: Os objetivos nesse cenário, são discutir as implicações éticas, econômicas, políticas e sociais deste modelo de sistema sociotécnico. Nos referimos, tanto as aplicações tecnológicas, quanto as consequências das mesmas na formação dos imaginários sociais, que tipo de relações se estabelecem e como são criadas dentro desse contexto. Conclusão: Concluímos na busca por refletir criticamente sobre as propostas de aprimoramento humano mediado pela tecnologia, que surgem enquanto parte da agenda da Convergência Tecnológica NBIC. No entanto, as propostas de melhoramento humano vão muito além de uma agenda de investigação. Há todo um quadro de referências filosóficas e políticas que defendem o aprimoramento da espécie, vertentes estas que se aliam a movimentos trans-humanistas e pós- humanistas, posições que são ao mesmo tempo éticas, políticas e econômicas. A partir de nossa análise, entendemos que ciência, tecnologia e política estão articuladas, em coprodução, em relação às expectativas de futuros que são esperados ou desejados. Ainda assim, acreditamos que há um espaço de diálogo possível, a partir do qual buscamos abrir propostas para o debate público sobre questões de ciência e tecnologia relacionadas ao aprimoramento da espécie humana.Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)The subject of this research is to discuss the social imaginaries of science and technology that emerge from the area of neuroengineering in relation with the Technological Convergence of four disciplines: Nanotechnology, Biotechnology, Information technologies and Cognitive technologies -neurosciences- (CT-NBIC). These areas are developed and articulated through discourses that emphasize the enhancement of human physical and cognitive capacities, the intuition it is to build a better society, through the scientific and technological progress, at the limits of the research and development (R&D) agendas. Objectives: The objective in this scenery, is to discuss the ethic, economic, politic and social implications of this model of sociotechnical system. We refer about the technological applications and the consequences of them in the formation of social imaginaries as well as the kind of social relations that are created and established in this context. Conclusion: We conclude looking for critical reflections about the proposals of human enhancement mediated by the technology. That appear as a part of the NBIC technologies agenda. Even so, the proposals of human enhancement go beyond boundaries that an investigation agenda. There is a frame of philosophical and political references that defend the enhancement of the human beings. These currents that ally to the transhumanism and posthumanism movements, positions that are ethic, politic and economic at the same time. From our analysis, we understand that science, technology and politics are articulated, are in co-production, regarding the expected and desired futures. Even so, we believe that there is a space of possible dialog, from which we look to open proposals for the public discussion on questions of science and technology related to enhancement of human beings

EURASIP Journal on Applied Signal Processing 2005:19, 3113–3121 c ○ 2005 Hindawi Publishing Corporation Determining Patterns in Neural Activity for Reaching Movements Using Nonnegative Matrix Factorization

We propose the use of nonnegative matrix factorization (NMF) as a model-independent methodology to analyze neural activity. We demonstrate that, using this technique, it is possible to identify local spatiotemporal patterns of neural activity in the form of sparse basis vectors. In addition, the sparseness of these bases can help infer correlations between cortical firing patterns and behavior. We demonstrate the utility of this approach using neural recordings collected in a brain-machine interface (BMI) setting. The results indicate that, using the NMF analysis, it is possible to improve the performance of BMI models through appropriate pruning of inputs