Brain-Computer Interfaces using Electrocorticography and Surface Stimulation

The brain connects to, modulates, and receives information from every organ in the body. As such, brain-computer interfaces (BCIs) have vast potential for diagnostics, medical therapies, and even augmentation or enhancement of normal functions. BCIs provide a means to explore the furthest corners of what it means to think, to feel, and to act—to experience the world and to be who you are. This work focuses on the development of a chronic bi-directional BCI for sensorimotor restoration through the use of separable frequency bands for recording motor intent and providing sensory feedback via electrocortical stimulation. Epidural cortical surface electrodes are used to both record electrocorticographic (ECoG) signals and provide stimulation without adverse effects associated with penetration through the protective dural barrier of brain. Chronic changes in electrode properties and signal characteristics are discussed, which inform optimal electrode designs and co-adaptive algorithms for decoding high-dimensional information. Additionally, a multi-layered approach to artifact suppression is presented, which includes a systems-level design of electronics, signal processing, and stimulus waveforms. The results of this work are relevant to a wider range of applications beyond ECoG and BCIs that involve closed-loop recording and stimulation throughout the body. By enabling simultaneous recording and stimulation through the techniques described here, responsive therapies can be developed that are tuned to individual patients and provide precision therapies at exactly the right place and time. This has the potential to improve targeted therapeutic outcomes while reducing undesirable side effects

State-dependent modulation of cortico-spinal networks

Beta-band rhythm (13-30 Hz) is a dominant oscillatory activity in the sensorimotor system. Numerous studies reported on links between motor performance and the cortical and cortico-spinal beta rhythm. However, these studies report divergent beta-band frequencies and are, additionally, based on differently performed motor-tasks (e.g., motor imagination, muscle contraction, reach, grasp, and attention). This diversity blurs the role of beta in the sensorimotor system. It consequently challenges the development of beta-band activity-dependent stimulation protocols in the sensorimotor system. In this vein, we studied the functional role of beta-band cortico-cortical and cortico-spinal networks during a motor learning task. We studied how the contribution of cortical and spinal beta changes in the course of learning, and how this modulation is affected by afferent feedback to the sensorimotor system. We furthermore researched the relationship to motor performance. Consider that we made our study in the absence of any residual movement to allow our findings to be translated into rehabilitation programs for severely affected stroke patients. This thesis, at first, investigates evoked responses after transcranial magnetic stimulation (TMS). This revealed two different beta-band networks, i.e., in the low and high beta-band reflecting cortical and cortico-spinal activity. We, then, used a broader frequency range in the beta-band to trigger passive opening of the hand (peripheral feedback) or cortical stimulation (cortical feedback). While a unilateral hemispheric increase in cortico-spinal synchronization was observed in the group with peripheral feedback, a bilateral hemispheric increase in cortico-cortical and cortico-spinal synchronization was observed for the group with cortical feedback. An improvement in motor performance was found in the peripheral group only. Additionally, an enhancement in the directed cortico-spinal synchronization from cortex to periphery was observed for the peripheral group. Similar neurophysiological and behavioral changes were observed for stroke patients receiving peripheral feedback. The results 6 suggest two different mechanisms for beta-band activity-dependent protocols depending on the feedback modality. While the peripheral feedback appears to increase the synchronization among neural groups, cortical stimulation appears to recruit dormant neurons and to extend the involved motor network. These findings may provide insights regarding the mechanism behind novel activity-dependent protocols. It also highlights the importance of afferent feedback for motor restoration in beta-band activity-dependent rehabilitation programs

Bidirectional Neural Interface Circuits with On-Chip Stimulation Artifact Reduction Schemes

Bidirectional neural interfaces are tools designed to “communicate” with the brain via recording and modulation of neuronal activity. The bidirectional interface systems have been adopted for many applications. Neuroscientists employ them to map neuronal circuits through precise stimulation and recording. Medical doctors deploy them as adaptable medical devices which control therapeutic stimulation parameters based on monitoring real-time neural activity. Brain-machine-interface (BMI) researchers use neural interfaces to bypass the nervous system and directly control neuroprosthetics or brain-computer-interface (BCI) spellers. In bidirectional interfaces, the implantable transducers as well as the corresponding electronic circuits and systems face several challenges. A high channel count, low power consumption, and reduced system size are desirable for potential chronic deployment and wider applicability. Moreover, a neural interface designed for robust closed-loop operation requires the mitigation of stimulation artifacts which corrupt the recorded signals. This dissertation introduces several techniques targeting low power consumption, small size, and reduction of stimulation artifacts. These techniques are implemented for extracellular electrophysiological recording and two stimulation modalities: direct current stimulation for closed-loop control of seizure detection/quench and optical stimulation for optogenetic studies. While the two modalities differ in their mechanisms, hardware implementation, and applications, they share many crucial system-level challenges. The first method aims at solving the critical issue of stimulation artifacts saturating the preamplifier in the recording front-end. To prevent saturation, a novel mixed-signal stimulation artifact cancellation circuit is devised to subtract the artifact before amplification and maintain the standard input range of a power-hungry preamplifier. Additional novel techniques have been also implemented to lower the noise and power consumption. A common average referencing (CAR) front-end circuit eliminates the cross-channel common mode noise by averaging and subtracting it in analog domain. A range-adapting SAR ADC saves additional power by eliminating unnecessary conversion cycles when the input signal is small. Measurements of an integrated circuit (IC) prototype demonstrate the attenuation of stimulation artifacts by up to 42 dB and cross-channel noise suppression by up to 39.8 dB. The power consumption per channel is maintained at 330 nW, while the area per channel is only 0.17 mm2. The second system implements a compact headstage for closed-loop optogenetic stimulation and electrophysiological recording. This design targets a miniaturized form factor, high channel count, and high-precision stimulation control suitable for rodent in-vivo optogenetic studies. Monolithically integrated optoelectrodes (which include 12 µLEDs for optical stimulation and 12 electrical recording sites) are combined with an off-the-shelf recording IC and a custom-designed high-precision LED driver. 32 recording and 12 stimulation channels can be individually accessed and controlled on a small headstage with dimensions of 2.16 x 2.38 x 0.35 cm and mass of 1.9 g. A third system prototype improves the optogenetic headstage prototype by furthering system integration and improving power efficiency facilitating wireless operation. The custom application-specific integrated circuit (ASIC) combines recording and stimulation channels with a power management unit, allowing the system to be powered by an ultra-light Li-ion battery. Additionally, the µLED drivers include a high-resolution arbitrary waveform generation mode for shaping of µLED current pulses to preemptively reduce artifacts. A prototype IC occupies 7.66 mm2, consumes 3.04 mW under typical operating conditions, and the optical pulse shaping scheme can attenuate stimulation artifacts by up to 3x with a Gaussian-rise pulse rise time under 1 ms.PHDElectrical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/147674/1/mendrela_1.pd

A Python-based Brain-Computer Interface Package for Neural Data Analysis

Anowar, Md Hasan, A Python-based Brain-Computer Interface Package for Neural Data Analysis. Master of Science (MS), December, 2020, 70 pp., 4 tables, 23 figures, 74 references. Although a growing amount of research has been dedicated to neural engineering, only a handful of software packages are available for brain signal processing. Popular brain-computer interface packages depend on commercial software products such as MATLAB. Moreover, almost every brain-computer interface software is designed for a specific neuro-biological signal; there is no single Python-based package that supports motor imagery, sleep, and stimulated brain signal analysis. The necessity to introduce a brain-computer interface package that can be a free alternative for commercial software has motivated me to develop a toolbox using the python platform. In this thesis, the structure of MEDUSA, a brain-computer interface toolbox, is presented. The features of the toolbox are demonstrated with publicly available data sources. The MEDUSA toolbox provides a valuable tool to biomedical engineers and computational neuroscience researchers

Wired, wireless and wearable bioinstrumentation for high-precision recording of bioelectrical signals in bidirectional neural interfaces

It is widely accepted by the scientific community that bioelectrical signals, which can be used for the identification of neurophysiological biomarkers indicative of a diseased or pathological state, could direct patient treatment towards more effective therapeutic strategies. However, the design and realisation of an instrument that can precisely record weak bioelectrical signals in the presence of strong interference stemming from a noisy clinical environment is one of the most difficult challenges associated with the strategy of monitoring bioelectrical signals for diagnostic purposes. Moreover, since patients often have to cope with the problem of limited mobility being connected to bulky and mains-powered instruments, there is a growing demand for small-sized, high-performance and ambulatory biopotential acquisition systems in the Intensive Care Unit (ICU) and in High-dependency wards. Furthermore, electrical stimulation of specific target brain regions has been shown to alleviate symptoms of neurological disorders, such as Parkinson’s disease, essential tremor, dystonia, epilepsy etc. In recent years, the traditional practice of continuously stimulating the brain using static stimulation parameters has shifted to the use of disease biomarkers to determine the intensity and timing of stimulation. The main motivation behind closed-loop stimulation is minimization of treatment side effects by providing only the necessary stimulation required within a certain period of time, as determined from a guiding biomarker. Hence, it is clear that high-quality recording of local field potentials (LFPs) or electrocorticographic (ECoG) signals during deep brain stimulation (DBS) is necessary to investigate the instantaneous brain response to stimulation, minimize time delays for closed-loop neurostimulation and maximise the available neural data. To our knowledge, there are no commercial, small, battery-powered, wearable and wireless recording-only instruments that claim the capability of recording ECoG signals, which are of particular importance in closed-loop DBS and epilepsy DBS. In addition, existing recording systems lack the ability to provide artefact-free high-frequency (> 100 Hz) LFP recordings during DBS in real time primarily because of the contamination of the neural signals of interest by the stimulation artefacts. To address the problem of limited mobility often encountered by patients in the clinic and to provide a wide variety of high-precision sensor data to a closed-loop neurostimulation platform, a low-noise (8 nV/√Hz), eight-channel, battery-powered, wearable and wireless multi-instrument (55 × 80 mm2) was designed and developed. The performance of the realised instrument was assessed by conducting both ex vivo and in vivo experiments. The combination of desirable features and capabilities of this instrument, namely its small size (~one business card), its enhanced recording capabilities, its increased processing capabilities, its manufacturability (since it was designed using discrete off-the-shelf components), the wide bandwidth it offers (0.5 – 500 Hz) and the plurality of bioelectrical signals it can precisely record, render it a versatile tool to be utilized in a wide range of applications and environments. Moreover, in order to offer the capability of sensing and stimulating via the same electrode, novel real-time artefact suppression methods that could be used in bidirectional (recording and stimulation) system architectures are proposed and validated. More specifically, a novel, low-noise and versatile analog front-end (AFE), which uses a high-order (8th) analog Chebyshev notch filter to suppress the artefacts originating from the stimulation frequency, is presented. After defining the system requirements for concurrent LFP recording and DBS artefact suppression, the performance of the realised AFE is assessed by conducting both in vitro and in vivo experiments using unipolar and bipolar DBS (monophasic pulses, amplitude ranging from 3 to 6 V peak-to-peak, frequency 140 Hz and pulse width 100 µs). Under both in vitro and in vivo experimental conditions, the proposed AFE provided real-time, low-noise and artefact-free LFP recordings (in the frequency range 0.5 – 250 Hz) during stimulation. Finally, a family of tunable hardware filter designs and a novel method for real-time artefact suppression that enables wide-bandwidth biosignal recordings during stimulation are also presented. This work paves the way for the development of miniaturized research tools for closed-loop neuromodulation that use a wide variety of bioelectrical signals as control signals.Open Acces

VLSI Circuits for Bidirectional Neural Interfaces

Medical devices that deliver electrical stimulation to neural tissue are important clinical tools that can augment or replace pharmacological therapies. The success of such devices has led to an explosion of interest in the field, termed neuromodulation, with a diverse set of disorders being targeted for device-based treatment. Nevertheless, a large degree of uncertainty surrounds how and why these devices are effective. This uncertainty limits the ability to optimize therapy and gives rise to deleterious side effects. An emerging approach to improve neuromodulation efficacy and to better understand its mechanisms is to record bioelectric activity during stimulation. Understanding how stimulation affects electrophysiology can provide insights into disease, and also provides a feedback signal to autonomously tune stimulation parameters to improve efficacy or decrease side-effects. The aims of this work were taken up to advance the state-of-the-art in neuro-interface technology to enable closed-loop neuromodulation therapies. Long term monitoring of neuronal activity in awake and behaving subjects can provide critical insights into brain dynamics that can inform system-level design of closed-loop neuromodulation systems. Thus, first we designed a system that wirelessly telemetered electrocorticography signals from awake-behaving rats. We hypothesized that such a system could be useful for detecting sporadic but clinically relevant electrophysiological events. In an 18-hour, overnight recording, seizure activity was detected in a pre-clinical rodent model of global ischemic brain injury. We subsequently turned to the design of neurostimulation circuits. Three critical features of neurostimulation devices are safety, programmability, and specificity. We conceived and implemented a neurostimulator architecture that utilizes a compact on-chip circuit for charge balancing (safety), digital-to-analog converter calibration (programmability) and current steering (specificity). Charge balancing accuracy was measured at better than 0.3%, the digital-to-analog converters achieved 8-bit resolution, and physiological effects of current steering stimulation were demonstrated in an anesthetized rat. Lastly, to implement a bidirectional neural interface, both the recording and stimulation circuits were fabricated on a single chip. In doing so, we implemented a low noise, ultra-low power recording front end with a high dynamic range. The recording circuits achieved a signal-to-noise ratio of 58 dB and a spurious-free dynamic range of better than 70 dB, while consuming 5.5 μW per channel. We demonstrated bidirectional operation of the chip by recording cardiac modulation induced through vagus nerve stimulation, and demonstrated closed-loop control of cardiac rhythm

Comparative analysis of TMS-EEG signal using different approaches in healthy subjects

openThe integration of transcranial magnetic stimulation with electroencephalography (TMS-EEG) represents a useful non-invasive approach to assess cortical excitability, plasticity and intra-cortical connectivity in humans in physiological and pathological conditions. However, biological and environmental noise sources can contaminate the TMS-evoked potentials (TEPs). Therefore, signal preprocessing represents a fundamental step in the analysis of these potentials and is critical to remove artefactual components while preserving the physiological brain activity. The objective of the present study is to evaluate the effects of different signal processing pipelines, (namely Leodori et al., Rogasch et al., Mutanen et al.) applied on TEPs recorded in five healthy volunteers after TMS stimulation of the primary motor cortex (M1) of the dominant hemisphere. These pipelines were used and compared to remove artifacts and improve the quality of the recorded signals, laying the foundation for subsequent analyses. Various algorithms, such as Independent Component Analysis (ICA), SOUND, and SSP-SIR, were used in each pipeline. Furthermore, after signal preprocessing, current localization was performed to map the TMS-induced neural activation in the cortex. This methodology provided valuable information on the spatial distribution of activity and further validated the effectiveness of the signal cleaning pipelines. Comparing the effects of the different pipelines on the same dataset, we observed considerable variability in how the pipelines affect various signal characteristics. We observed significant differences in the effects on signal amplitude and in the identification and characterisation of peaks of interest, i.e., P30, N45, P60, N100, P180. The identification and characteristics of these peaks showed variability, especially with regard to the early peaks, which reflect the cortical excitability of the stimulated area and are the more affected by biological and stimulation-related artifacts. Despite these differences, the topographies and source localisation, which are the most informative and useful in reconstructing signal dynamics, were consistent and reliable between the different pipelines considered. The results suggest that the existing methodologies for analysing TEPs produce different effects on the data, but are all capable of reproducing the dynamics of the signal and its components. Future studies evaluating different signal preprocessing methods in larger populations are needed to determine an appropriate workflow that can be shared through the scientific community, in order to make the results obtained in different centres comparable

Closed-loop approaches for innovative neuroprostheses

The goal of this thesis is to study new ways to interact with the nervous system in case of damage or pathology. In particular, I focused my effort towards the development of innovative, closed-loop stimulation protocols in various scenarios: in vitro, ex vivo, in vivo

EEG source analysis during circular rhythmic human arm movements

Tese de mestrado integrado, Engenharia Biomédica e Biofísica (Engenharia Clínica e Instrumentação Médica) Universidade de Lisboa, Faculdade de Ciências, 2017Decoding arm movement trajectory from brain signals would allow motor impaired people to control an arm prosthetic. Studies show that we can estimate a vector that points in the direction of arm movements based on single motor neuron activity - the population vector. This type of recording requires the surgical insertion of electrodes in the cerebral cortex. Although such invasive recordings would offer high spatial resolution, noninvasive recording have the advantage of high temporal resolution and no need for surgery. Researchers have managed to decode movement properties from noninvasive brain signals with similar accuracy as from invasive recordings. But can we find a noninvasive analogous of the population vector, a vector that points in the direction of the arm movement? This was the motivation for this thesis. To approach this question we acquired EEG, EOG and kinematic data from 12 healthy subjects while they performed a rhythmic circular right arm movement. We analyzed the data in the time and frequency domains. In the time domain we explored mainly the data averaged over cycles. We found a pattern that looked as if the potentials in the scalp rotated with the arm. To better visualize this rotation, we fit one dipole per time-stamp in the averaged cycle data of each subject to describe the scalp’s potentials. The dipoles rotated along the cycle for all subjects, most of them in the same direction and plane of rotation, with exception for two subjects whose rotation was opposite and three subjects with a slightly different rotation plan. In the frequency domain, we used the Source Power Comodulation algorithm (SPoC), an algorithm that searches for components whose power correlates with a target variable, in our case, the arm kinematics. By applying this algorithm to 20-24 Hz band-pass filtered data, we found two components per subject, each calculated with different kinematic target variables. The results show components that when applied to the non band-pass filtered data, created signals whose power spectrum highly correlated with the given targets (the average of the absolute correlations being 85.5%). The physiological reason for both these phenomena is not entirely understood. To find the analogous of the population vector there is still a long way to go, and we hope this thesis was a first step towards it.O cérebro controla direta ou indiretamente todas as ações do corpo humano, entre elas o nosso movimento. O movimento é uma capacidade fundamental ao ser humano e, por essa mesma razão, indivíduos que sofram de incapacidades motoras têm uma redução considerável da sua qualidade de vida. Uma interface cérebro-computador (mais conhecida pelo seu nome em inglês brain-computer interface (BCI)) é um sistema que permite o controlo de dispositivos externos usando sinais cerebrais. Esta tecnologia é particularmente interessante para pessoas com incapacidade motora uma vez que não necessita de input físico e poderia ser usada para controlar uma neuroprótese ou um braço robótico. Existem várias estratégias que possibilitam o controlo destes sistemas, mas para o controlo de uma prótese do braço seria preferível usar uma estratégia natural, que não implicasse uma aprendizagem exaustiva por parte do utilizador. Para esse fim, é necessário descodificar vários parâmetros motores de acordo com a intenção do utilizador, como por exemplo, a direção do braço. A possibilidade de um dia conseguir descodificar sinais cerebrais para o controlo de dispositivos externos já começa a ganhar forma, mas ainda não é possível a um nível suficientemente eficaz. Usando métodos invasivos de aquisição de sinais cerebrais que requerem cirurgia para implantar elétrodos no córtex cerebral, Georgopoulos et al. conseguiram distinguir entre movimentos direcionais (em 8 direções num plano horizontal) em macacos. Nessas experiências criou o conceito de vetor de população (population vector) que é um vetor calculado a partir da atividade de neurónios motores que tem a particularidade de apontar na direção do movimento executado. Já no campo dos métodos de aquisição não-invasivos podemos destacar o eletroencefalograma (EEG) e o magnetoencefalograma (MEG) que adquirem sinais elétricos e magnéticos (respetivamente) com sensores colocados fora do crânio. Vários investigadores usaram estes métodos de aquisição para descodificar sinais cerebrais durante tarefas de movimento direcionais usando regressões lineares em sinais de baixa frequência, e modulações em frequência para sinais na gama dos 50-90 Hz (banda de frequência ϒ) e em frequência mais baixas para os 10-30 Hz (bandas de frequência α e β). Algo que ainda não foi estudado é a possibilidade de encontrar um análogo ao vetor população usando métodos não-invasivos. Este não teria os mesmos princípios do vetor de Georgopoulos, uma vez que nos é impossível inferir a atividade de neurónios singulares em métodos não-invasivos, mas teria o mesmo objetivo: apontar na direção do movimento executado. Para explorar este conceito realizámos aquisição de dados EEG, eletrooculograma (EOG) e dados cinéticos do braço direito de 12 sujeitos saudáveis, enquanto estes executavam um movimento rítmico, circular, no sentido dos ponteiros do relógio num plano vertical à sua frente. Durante a aquisição, os sujeitos focaram o seu olhar numa cruz mostrada através de um monitor colocado a sua frente, de forma a minimizar os movimentos oculares. Adicionalmente, uma divisória foi colocada perto do lado direito da face de cada sujeito impedindo os mesmos de observarem o seu braço enquanto realizavam o movimento requisitado, não obtendo assim qualquer feedback visual do seu membro superior. Os dados cinéticos foram adquiridos com um sensor Kinect para a Xbox 360 que ao longo da experiência localizou as junções do braço direito dos sujeitos. Os dados cinéticos foram filtrados com um passa-banda 0.3-0.8 Hz e, ao longo dos ciclos do braço, os pontos extremos do braço (i.e., os máximos e mínimos nas coordenadas vertical e horizontal) foram anotados nos dados para possibilitar a associação dos sinais cerebrais com a trajetória do braço em cada ciclo. Para cada sujeito os canais EEG ruidosos foram interpolados, os dados foram referenciados à média comum de todos os canais, e os sinais foram filtrados numa banda de frequência 0.25-100 Hz e com um filtro tapa banda nos 50 e nos 100 Hz, este último para rejeitar o ruído de fundo. Os sinais de EEG e EOG foram separados em épocas conforme a posição do braço, sendo que cada época passou então a consistir num ciclo do braço completo que começa no ponto mais alto da coordenada vertical. Cada época foi depois temporalmente distorcida para que todas tivessem a mesma duração. As épocas com artefactos foram rejeitadas da análise usando métodos automáticos de rejeição. Independent Component Analysis (ICA) foi utilizada para identificar e posteriormente rejeitar componentes independentes referentes a movimentos musculares e oculares. Por fim, os dados foram explorados em ambos os domínios de tempo e frequência. No domínio do tempo, estudámos mais especificamente a média das épocas de EEG e EOG durante os ciclos do braço. Uma vez que sinais não-invasivos são muito sujeitos a ruído, a média elimina artefactos singulares e acentua os sinais que aparecem constantemente nos dados. Os sinais do ciclo médio mostraram um padrão interessante para todos os sujeitos; um comportamento rotacional ao longo da rotação do braço direito. Para acompanhar a rotação dos potenciais, procurámos por um dipolo que descrevesse a distribuição topográfica a cada ponto do tempo. A rotação dos potenciais do EEG ao longo do ciclo médio foram verificados com a rotação da direção do dipolo ao longo do ciclo. A grande maioria dos sujeitos obteve um dipolo a rodar no mesmo sentido no mesmo plano (segundo a regra da mão direita, com um vetor de rotação a apontar para a zona frontal esquerda do cérebro). Cinco sujeitos foram a exceção, 2 desses cujo dipolo rodava no sentido contrário, e os restantes 3 sujeitos cujo dipolo rodava no mesmo sentido, mas num plano ligeiramente diferente. Em todos os sujeitos o dipolo ajustado rodava, de forma relativamente uniforme. No domínio da frequência, estudámos em particular a banda de frequência dos 20 aos 24 Hz. Escolheuse esta banda de frequência pois demonstrou os resultados mais interessantes e já tinha sido utilizada em estudos prévios. Usámos um algoritmo chamado SPoC (Source Power Comodulation) que encontra componentes de atividade cerebral cuja amplitude em frequência correlacione com uma variável alvo. Como variável alvo usámos os dados cinéticos do braço direito, e como input os dados cerebrais filtrados por um filtro passa-banda (20-24 Hz). Os resultados traduziram-se numa série de componentes cuja amplitude correlacionava ou anti-correlacionava com o movimento do braço, muitas delas com projecções topográficas consistentes com as áreas cerebrais motoras. Encontraram-se algumas semelhanças entre os padrões de ativação das componentes do SPoC dos vários sujeitos, ainda que os resultados variassem entre cada um. Ao projetar as componentes aos dados não-filtrados pelo passa-banda, verificamos que as modelações em frequência de facto correlacionam com as variáveis-alvo como esperado, com uma média da norma das correlações de todos os sujeitos a 85,5%. No domínio temporal, ainda que recorrendo à média de todos os ciclos (épocas), este é o primeiro estudo que demonstra de forma não-invasiva, a existência de um dipolo com comportamento rotacional ao longo da rotação do braço. Para o seu uso em tecnologias de BCI, é necessário encontrar o mesmo fenómeno em épocas únicas, tornando possível uma classificação em single-trial e em tempo real. No que toca aos resultados no domínio da frequência, a procura por componentes cuja fonte poderia estar envolvida na criação do movimento circular foi também bem-sucedida. Este estudo abriu portas para uma série de investigações futuras. Para trabalhos posteriores destaco a necessidade de uma análise estatística, de usar mais do que um dipolo para descrever a distribuição de potenciais no domínio temporal, de explorar os dados em cada movimento e não apenas a sua média, e de explorar paradigmas semelhantes durante o movimento do braço esquerdo. Os resultados desta tese serviram, portanto, como primeiro passo na direção de encontrar o análogo não-invasivo do vetor de população

Development and characterization of an intracortical closed-loop brain-computer interface

Intracortical brain-computer interfaces (BCI) have the potential to restore motor function to people with paralysis by extracting movement intent signals directly from motor cortex. While current technology has allowed individuals to perform simple object interactions with robotic arms, such demonstrations have depended exclusively on visual feedback. Additional forms of sensory feedback may lessen the dependence on vision and allow for more dexterous control. Intracortical microstimulation (ICMS) has been proposed as a method of adding somatosensory feedback to BCI by directly stimulating somatosensory cortex to evoke tactile sensations referred to the hand. Our lab recently demonstrated that ICMS can elicit graded and focal tactile sensations in an individual with spinal cord injury (SCI). However, several challenges must be resolved to demonstrate the viability of ICMS as a technique for incorporating sensory feedback in a closed-loop BCI. First, microstimulation generates large voltage transients that appear as artifacts in the neural recordings used for BCI control. These artifacts can corrupt the recorded signal throughout the entire stimulus train, and must be eliminated to allow for continuous BCI decoding. Second, it is unknown whether the sensations elicited by ICMS can be perceived quickly enough for use as a feedback signal. Here, I present several aspects of the development of a closed-loop BCI system, including a method for artifact rejection and the characterization of simple reaction times to ICMS of human somatosensory cortex. A human participant with tetraplegia due to SCI was implanted with four microelectrode arrays in primary motor and somatosensory cortices. I implemented a robust method of artifact rejection that preserves neural data as soon as 750 microseconds after each stimulus pulse by applying signal blanking and an appropriate digital filter. I validated this method by comparing BCI performance with and without ICMS and found that performance was maintained with ICMS and artifact rejection. Next, I characterized simple reaction times to single-channel ICMS, and found that responses to ICMS were comparable, and often faster, than responses to electrical stimulation on the hand. These findings suggest that ICMS is a viable method to provide feedback in a closed-loop BCI