7,148 research outputs found
Active C4 electrodes for local field potential recording applications
Extracellular neural recording, with multi-electrode arrays (MEAs), is a powerful method used to study neural function at the network level. However, in a high density array, it can be costly and time consuming to integrate the active circuit with the expensive electrodes. In this paper, we present a 4 mm × 4 mm neural recording integrated circuit (IC) chip, utilizing IBM C4 bumps as recording electrodes, which enable a seamless active chip and electrode integration. The IC chip was designed and fabricated in a 0.13 μm BiCMOS process for both in vitro and in vivo applications. It has an input-referred noise of 4.6 μV rms for the bandwidth of 10 Hz to 10 kHz and a power dissipation of 11.25 mW at 2.5 V, or 43.9 μW per input channel. This prototype is scalable for implementing larger number and higher density electrode arrays. To validate the functionality of the chip, electrical testing results and acute in vivo recordings from a rat barrel cortex are presented.R01 NS072385 - NINDS NIH HHS; 1R01 NS072385 - NINDS NIH HH
The effect of voluntary modulation of the sensory-motor rhythm during different mental tasks on H reflex
Objectives:
The aim of this study was to explore the possibility of the short-term modulation of the soleus H reflex through self-induced modulation of the sensory-motor rhythm (SMR) as measured by electroencephalography (EEG) at Cz.
Methods:
Sixteen healthy participants took part in one session of neuromodulation. Motor imagery and mental math were strategies for decreasing SMR, while neurofeedback was used to increase SMR. H reflex of the soleus muscle was elicited by stimulating tibial nerve when SMR reached a pre-defined threshold and was averaged over 5 trials.
Results:
Neurofeedback and mental math both resulted in the statistically significant increase of H reflex (p = 1.04·10− 6 and p = 5.47·10− 5 respectively) while motor imagery produced the inconsistent direction of H reflex modulation (p = 0.57). The average relative increase of H reflex amplitude was for neurofeedback 19.0 ± 5.4%, mental math 11.1 ± 3.6% and motor imagery 2.6 ± 1.0%. A significant negative correlation existed between SMR amplitude and H reflex for all tasks at Cz and C4.
Conclusions:
It is possible to achieve a short-term modulation of H reflex through short-term modulation of SMR. Various mental tasks dominantly facilitate H reflex irrespective of direction of SMR modulation.
Significance:
Improving understanding of the influence of sensory-motor cortex on the monosynaptic reflex through the self-induced modulation of cortical activity
An Electrocorticographic Brain Interface in an Individual with Tetraplegia
Brain-computer interface (BCI) technology aims to help individuals with disability to control assistive devices and reanimate paralyzed limbs. Our study investigated the feasibility of an electrocorticography (ECoG)-based BCI system in an individual with tetraplegia caused by C4 level spinal cord injury. ECoG signals were recorded with a high-density 32-electrode grid over the hand and arm area of the left sensorimotor cortex. The participant was able to voluntarily activate his sensorimotor cortex using attempted movements, with distinct cortical activity patterns for different segments of the upper limb. Using only brain activity, the participant achieved robust control of 3D cursor movement. The ECoG grid was explanted 28 days post-implantation with no adverse effect. This study demonstrates that ECoG signals recorded from the sensorimotor cortex can be used for real-time device control in paralyzed individuals
Data-driven multivariate and multiscale methods for brain computer interface
This thesis focuses on the development of data-driven multivariate and multiscale methods
for brain computer interface (BCI) systems. The electroencephalogram (EEG), the
most convenient means to measure neurophysiological activity due to its noninvasive nature,
is mainly considered. The nonlinearity and nonstationarity inherent in EEG and its
multichannel recording nature require a new set of data-driven multivariate techniques to
estimate more accurately features for enhanced BCI operation. Also, a long term goal
is to enable an alternative EEG recording strategy for achieving long-term and portable
monitoring.
Empirical mode decomposition (EMD) and local mean decomposition (LMD), fully
data-driven adaptive tools, are considered to decompose the nonlinear and nonstationary
EEG signal into a set of components which are highly localised in time and frequency. It
is shown that the complex and multivariate extensions of EMD, which can exploit common
oscillatory modes within multivariate (multichannel) data, can be used to accurately
estimate and compare the amplitude and phase information among multiple sources, a
key for the feature extraction of BCI system. A complex extension of local mean decomposition
is also introduced and its operation is illustrated on two channel neuronal
spike streams. Common spatial pattern (CSP), a standard feature extraction technique
for BCI application, is also extended to complex domain using the augmented complex
statistics. Depending on the circularity/noncircularity of a complex signal, one of the
complex CSP algorithms can be chosen to produce the best classification performance
between two different EEG classes.
Using these complex and multivariate algorithms, two cognitive brain studies are
investigated for more natural and intuitive design of advanced BCI systems. Firstly, a Yarbus-style auditory selective attention experiment is introduced to measure the user
attention to a sound source among a mixture of sound stimuli, which is aimed at improving
the usefulness of hearing instruments such as hearing aid. Secondly, emotion experiments
elicited by taste and taste recall are examined to determine the pleasure and displeasure
of a food for the implementation of affective computing. The separation between two
emotional responses is examined using real and complex-valued common spatial pattern
methods.
Finally, we introduce a novel approach to brain monitoring based on EEG recordings
from within the ear canal, embedded on a custom made hearing aid earplug. The new
platform promises the possibility of both short- and long-term continuous use for standard
brain monitoring and interfacing applications
A 256-input micro-electrode array with integrated cmos amplifiers for neural signal recording
Thesis (Ph.D.)--Boston UniversityThe nervous system communicates and processes information through its basic structural units -- individual neurons (nerve cells). Neurons convey neural information via electrical and chemical signals, which makes electrophysiological recording techniques very important in the study of neurophysiology. Specifically, active microelectrode arrays (MEAs) with amplifiers integrated on the same substrate are used because they provide a very powerful neural electrical recording technique that can be directly interfaced to acute slices and cell cultures. 2D planer electrodes are typically used for recording from neural cultures in vitro, while in vivo recording in live animals invariably requires the use of 3D electrodes.
I have designed an active MEA with neural amplifiers and 3D electrodes, all integrated on a single chip. The electrodes are commercially available 3D C4 (Controlled Collapse Chip Connect) flip-chip bonding solder balls that have a diameter of 100 µm and a pitch of 200 µm. An active MEA neural recording chip -- the Multiple-Input Neural Sensor (MINS) chip -- was designed and fabricated using the IBM BiCMOS 8HP 0.13 µm technology. The MINS IC has 256 input channels that are time-division multiplexed into two output pads. Each channel was designed to work at a 20 kHz frame rate with a total voltage gain of 60 dB per channel with an input-referred noise voltage of 5.3 µVrms over 10 Hz to 10 kHz.
The entire MINS chip has an area of 4 x 4 mm^2 with 256 input C4s plus 20 wire-bond pads on two adjacent edges of the chip for power, control, and outputs. The fabricated MINS chips are wire-bonded to standard pin grid array (PGA), open-top PGA, and custom-designed printed circuit board (PCB) packages for electrical, in vitro, and in vivo testing, respectively. After process variation correction, the voltage gain of the 256 neural amplifiers, measured in vitro across several chips, has a mean value of 58.7 dB and a standard deviation of 0.37 dB. Measurements done with the electrical testing package demonstrate that the MINS IC has a flat frequency response from 0.05 Hz to 1.4 MHz, an input-referred noise voltage of 4.6 µVrms over 10 Hz to 10 kHz, an output voltage swing as large as 1.5 V peak-to-peak, and a total power consumption of 11.25 mW, or 43.9 µW per input channel
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Efficiency evaluation of external environments control using bio-signals
There are many types of bio-signals with various control application prospects. This dissertation regards possible application domain of electroencephalographic signal. The implementation of EEG signals, as a source of information used for control of external devices, became recently a growing concern in the scientific world. Application of electroencephalographic signals in Brain-Computer Interfaces (BCI) (variant of Human-Computer Interfaces (HCI)) as an implement, which enables direct and fast communication between the human brain and an external device, has become recently very popular.
Currently available on the market, BCI solutions require complex signal processing methodology, which results in the need of an expensive equipment with high computing power.
In this work, a study on using various types of EEG equipment in order to apply the most appropriate one was conducted. The analysis of EEG signals is very complex due to the presence of various internal and external artifacts. The signals are also sensitive to disturbances and non-stochastic, what makes the analysis a complicated task. The research was performed on customised (built by the author of this dissertation) equipment, on professional medical device and on Emotiv EPOC headset.
This work concentrated on application of an inexpensive, easy to use, Emotiv EPOC headset as a tool for gaining EEG signals. The project also involved application of embedded system platform - TS-7260. That solution caused limits in choosing an appropriate signal processing method, as embedded platforms characterise with a little efficiency and low computing power. That aspect was the most challenging part of the whole work.
Implementation of the embedded platform enables to extend the possible future application of the proposed BCI. It also gives more flexibility, as the platform is able to simulate various environments.
The study did not involve the use of traditional statistical or complex signal processing methods. The novelty of the solution relied on implementation of the basic mathematical operations. The efficiency of this method was also presented in this dissertation. Another important aspect of the conducted study is that the research was carried out not only in a laboratory, but also in an environment reflecting real-life conditions.
The results proved efficiency and suitability of the implementation of the proposed solution in real-life environments. The further study will focus on improvement of the signal-processing method and application of other bio-signals - in order to extend the possible applicability and ameliorate its effectiveness
Personality cannot be predicted from the power of resting state EEG
In the present study we asked whether it is possible to decode personality
traits from resting state EEG data. EEG was recorded from a large sample of
subjects (N = 309) who had answered questionnaires measuring personality trait
scores of the 5 dimensions as well as the 10 subordinate aspects of the Big
Five. Machine learning algorithms were used to build a classifier to predict
each personality trait from power spectra of the resting state EEG data. The
results indicate that the five dimensions as well as their subordinate aspects
could not be predicted from the resting state EEG data. Finally, to demonstrate
that this result is not due to systematic algorithmic or implementation
mistakes the same methods were used to successfully classify whether the
subject had eyes open or eyes closed and whether the subject was male or
female. These results indicate that the extraction of personality traits from
the power spectra of resting state EEG is extremely noisy, if possible at all.Comment: 14 pages, 4 figure
Brain activity on encoding different textures EEG signal acquisition with ExoAtlet®
Powered exoskeletons play a crucial role in the rehabilitation field improving the quality
of life for those who need them. Thus, being a major contribution for patients integration
into society, providing them with more autonomy and freedom.
In spite of these positive outcomes, a thorough description of the brain correlates connected to exoskeleton control is still needed. For instance, the perception of different
pavement textures when wearing an exoskeleton is probably going to cause changes in
cerebral activity, which could impact both sensory encoding and Brain-Computer Interface (BCI) control.
Therefore, the main goal of this work is to describe the brain activity response to different
textured pavements using ExoAtlet ® powered exoskeleton. In order to measure, process, analyze and classify the impact of different textures on neurophysiological rhythms,
4-minute signals were recorded by Electroencephalogram (EEG) with a 16-channel cap
(actiCAP by Brain Products).
Each of the three experimental subjects was instructed to walk in place on four different
types of pavement (flat, carpet, foam, and rubber circles) with and without the exoskeleton, for a total of eight different experimental conditions. A counterbalanced design was
applied, and informed consent was obtained from participants (Committee for Health
Sciences of the Universidade Católica Portuguesa - 99/2022). Additionally, four machine
learning methods, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Linear
Discriminant Analysis (LDA), and Artificial Neural Network (ANN), were selected in order to analyze three distinct classification problems.
This study found that there were changes associated with the delta frequency band for
electrodes C3 and C4, and when comparing the classifiers performance, LDA presented
the best accuracy across the three classification problems involving all subjects.
Thereby, this work concludes that the results are consistent with the hypothesis that sensory processing of pavement textures during exoskeleton control induces neural changes
and delta variations of the C3 and C4 electrodes. Additionally, LDA demonstrated the
best performance across the three classifications of subject-independent problems.Os exoesqueletos motorizados desempenham um papel crucial no campo da reabilitação,
melhorando a qualidade de vida das pessoas que deles necessitam. Deste modo, são um
contributo importante para que os pacientes com condições físicas limitadas sejam mais
facilmente integrados na sociedade, proporcionando-lhes mais autonomia e liberdade.
Embora esta tecnologia tenha os seus aspetos positivos, ainda existe a necessidade de descrever os correlatos cerebrais direcionados para o controlo do exoesqueleto. Por exemplo, a percepção de diferentes pavimentos quando se usa um exoesqueleto vai provavelmente causar alterações na actividade cerebral, o que pode ter impacto tanto na codificação sensorial como no controlo da interface cérebro-máquina (BCI).
Deste modo, o principal objetivo deste trabalho é descrever a atividade cerebral às diferentes texturas dos pavimentos, utilizando o exoesqueleto ExoAtlet ®. A fim de medir, processar, analisar e classificar o impacto de diferentes texturas em ritmos neurofisiológicos,
foram registados sinais de 4 minutos atravês the Eletroencefalograma (EEG) com uma
touca de 16 canais (actiCAP by Brain Products).
Cada um dos três voluntários foi instruído a dar passos no lugar em quatro tipos diferentes
de pavimento (plano, alcatifa, espuma, e círculos de borracha) com e sem o exosqueleto,
num total de oito condições experimentais diferentes. Foi aplicado um desenho contrabalançado e foi obtido o consentimento informado dos participantes (Comissão para as Ciências da Saúde da Universidade Católica Portuguesa - 99/2022). Adicionalmente, foram
selecionados quatro classificadores: máquinas de vetores de suporte (SVM), k-vizinhos
mais próximos (KNN), análise discriminante linear (LDA) e redes neuronais artificiais
(ANN) para analisar três problemas de classificação distintos.
Os resultados obtidos por este estudo demonstraram que existiam alterações associadas
à banda de frequência delta para os eléctrodos C3 e C4 e, ao comparar o desempenho dos
classificadores, o LDA apresentou a melhor exatidão nos três problemas de classificação
envolvendo todos os sujeitos.
Assim, estes resultados são consistentes com a hipótese de que o processamento sensorial
dos pavimentos durante o controlo do exoesqueleto induz alterações neuronais
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