3,660 research outputs found
MODIFICATION AND EVALUATION OF A BRAIN COMPUTER INTERFACE SYSTEM TO DETECT MOTOR INTENTION
It is widely understood that neurons within the brain produce electrical activity, and electroencephalography—a technique used to measure biopotentials with electrodes placed upon the scalp—has been used to observe it. Today, scientists and engineers work to interface these electrical neural signals with computers and machines through the field of Brain-Computer Interfacing (BCI). BCI systems have the potential to greatly improve the quality of life of physically handicapped individuals by replacing or assisting missing or debilitated motor functions. This research thus aims to further improve the efficacy of the BCI based assistive technologies used to aid physically disabled individuals. This study deals with the testing and modification of a BCI system that uses the alpha and beta bands to detect motor intention by weighing online EEG output against a calibrated threshold
Information Theoretic Approaches for Motor-Imagery BCI Systems: Review and Experimental Comparison
Brain computer interfaces (BCIs) have been attracting a great interest in recent years.
The common spatial patterns (CSP) technique is a well-established approach to the spatial filtering
of the electroencephalogram (EEG) data in BCI applications. Even though CSP was originally
proposed from a heuristic viewpoint, it can be also built on very strong foundations using information
theory. This paper reviews the relationship between CSP and several information-theoretic
approaches, including the Kullback–Leibler divergence, the Beta divergence and the Alpha-Beta
log-det (AB-LD)divergence. We also revise other approaches based on the idea of selecting those
features that are maximally informative about the class labels. The performance of all the methods
will be also compared via experiments.Gobierno Español MICINN TEC2014-53103-
A Tutorial on EEG Signal Processing Techniques for Mental State Recognition in Brain-Computer Interfaces
International audienceThis chapter presents an introductory overview and a tutorial of signal processing techniques that can be used to recognize mental states from electroencephalographic (EEG) signals in Brain-Computer Interfaces. More particularly, this chapter presents how to extract relevant and robust spectral, spatial and temporal information from noisy EEG signals (e.g., Band Power features, spatial filters such as Common Spatial Patterns or xDAWN, etc.), as well as a few classification algorithms (e.g., Linear Discriminant Analysis) used to classify this information into a class of mental state. It also briefly touches on alternative, but currently less used approaches. The overall objective of this chapter is to provide the reader with practical knowledge about how to analyse EEG signals as well as to stress the key points to understand when performing such an analysis
Tikhonov Regularization Enhances EEG-based Spatial Filtering for Single Trial Regression
International audienceIn the field of Brain-Computer Interfaces (BCI), robust methods for the decoding of continuous brain states are of great interest as new application fields are arising. When capturing brain activity by an elec-troencephalogram (EEG), the Source Power Comodulation (SPoC) algorithm allows to compute spatial filters for the decoding of a continuous variable. However, dealing with high-dimensional EEG data that suffer from low signal-to-noise ratio, the method reveals instabilities for small training data sets and is prone to overfitting. In this paper, we introduce a framework for applying Tikhonov regularization to the SPoC approach in order to restrict the solution space of filters. Our findings show that an additional trace normalization of the included covariance matrices is a necessary prerequisite to tune the sensitivity of the resulting algorithm. In an offline analysis with data from N=18 subjects, the introduced trace normalized and Tihonov regularized SPoC variant (NTR-SPoC) outperforms the standard SPoC method for the majority of individuals. With this proof-of-concept study, a generalizable regularization framework for SPoC has been established which allows to implement a variety of different regularization strategies in the future
Electroencephalograph (EEG) signal processing techniques for motor imagery Brain Computer interface systems
Brain-Computer Interface (BCI) system provides a channel for the brain to
control external devices using electrical activities of the brain without using the
peripheral nervous system. These BCI systems are being used in various medical
applications, for example controlling a wheelchair and neuroprosthesis devices for
the disabled, thereby assisting them in activities of daily living. People suffering
from Amyotrophic Lateral Sclerosis (ALS), Multiple Sclerosis and completely locked
in are unable to perform any body movements because of the damage of the
peripheral nervous system, but their cognitive function is still intact. BCIs operate
external devices by acquiring brain signals and converting them to control
commands to operate external devices. Motor-imagery (MI) based BCI systems, in
particular, are based on the sensory-motor rhythms which are generated by the
imagination of body limbs. These signals can be decoded as control commands in
BCI application. Electroencephalogram (EEG) is commonly used for BCI applications
because it is non-invasive. The main challenges of decoding the EEG signal are
because it is non-stationary and has a low spatial resolution. The common spatial
pattern algorithm is considered to be the most effective technique for
discrimination of spatial filter but is easily affected by the presence of outliers.
Therefore, a robust algorithm is required for extraction of discriminative features
from the motor imagery EEG signals.
This thesis mainly aims in developing robust spatial filtering criteria which
are effective for classification of MI movements. We have proposed two approaches
for the robust classification of MI movements. The first approach is for the
classification of multiclass MI movements based on the thinICA (Independent
Component Analysis) and mCSP (multiclass Common Spatial Pattern Filter) method.
The observed results indicate that these approaches can be a step towards the
development of robust feature extraction for MI-based BCI system.
The main contribution of the thesis is the second criterion, which is based on
Alpha- Beta logarithmic-determinant divergence for the classification of two class
MI movements. A detailed study has been done by obtaining a link between the AB
log det divergence and CSP criterion. We propose a scaling parameter to enable a
similar way for selecting the respective filters like the CSP algorithm. Additionally,
the optimization of the gradient of AB log-det divergence for this application was
also performed. The Sub-ABLD (Subspace Alpha-Beta Log-Det divergence)
algorithm is proposed for the discrimination of two class MI movements. The
robustness of this algorithm is tested with both the simulated and real data from BCI
competition dataset. Finally, the resulting performances of the proposed algorithms
have been favorably compared with other existing algorithms
Enhancing brain-computer interfacing through advanced independent component analysis techniques
A Brain-computer interface (BCI) is a direct communication system between a brain
and an external device in which messages or commands sent by an individual do not
pass through the brain’s normal output pathways but is detected through brain signals.
Some severe motor impairments, such as Amyothrophic Lateral Sclerosis, head
trauma, spinal injuries and other diseases may cause the patients to lose their muscle
control and become unable to communicate with the outside environment. Currently
no effective cure or treatment has yet been found for these diseases. Therefore using a
BCI system to rebuild the communication pathway becomes a possible alternative
solution. Among different types of BCIs, an electroencephalogram (EEG) based BCI
is becoming a popular system due to EEG’s fine temporal resolution, ease of use,
portability and low set-up cost. However EEG’s susceptibility to noise is a major
issue to develop a robust BCI. Signal processing techniques such as coherent
averaging, filtering, FFT and AR modelling, etc. are used to reduce the noise and
extract components of interest. However these methods process the data on the
observed mixture domain which mixes components of interest and noise. Such a
limitation means that extracted EEG signals possibly still contain the noise residue or
coarsely that the removed noise also contains part of EEG signals embedded.
Independent Component Analysis (ICA), a Blind Source Separation (BSS)
technique, is able to extract relevant information within noisy signals and separate the
fundamental sources into the independent components (ICs). The most common
assumption of ICA method is that the source signals are unknown and statistically
independent. Through this assumption, ICA is able to recover the source signals.
Since the ICA concepts appeared in the fields of neural networks and signal
processing in the 1980s, many ICA applications in telecommunications, biomedical
data analysis, feature extraction, speech separation, time-series analysis and data
mining have been reported in the literature. In this thesis several ICA techniques are
proposed to optimize two major issues for BCI applications: reducing the recording
time needed in order to speed up the signal processing and reducing the number of
recording channels whilst improving the final classification performance or at least
with it remaining the same as the current performance. These will make BCI a more
practical prospect for everyday use.
This thesis first defines BCI and the diverse BCI models based on different
control patterns. After the general idea of ICA is introduced along with some
modifications to ICA, several new ICA approaches are proposed. The practical work
in this thesis starts with the preliminary analyses on the Southampton BCI pilot
datasets starting with basic and then advanced signal processing techniques. The
proposed ICA techniques are then presented using a multi-channel event related
potential (ERP) based BCI. Next, the ICA algorithm is applied to a multi-channel
spontaneous activity based BCI. The final ICA approach aims to examine the
possibility of using ICA based on just one or a few channel recordings on an ERP
based BCI.
The novel ICA approaches for BCI systems presented in this thesis show that ICA
is able to accurately and repeatedly extract the relevant information buried within
noisy signals and the signal quality is enhanced so that even a simple classifier can
achieve good classification accuracy. In the ERP based BCI application, after multichannel
ICA the data just applied to eight averages/epochs can achieve 83.9%
classification accuracy whilst the data by coherent averaging can reach only 32.3%
accuracy. In the spontaneous activity based BCI, the use of the multi-channel ICA
algorithm can effectively extract discriminatory information from two types of singletrial
EEG data. The classification accuracy is improved by about 25%, on average,
compared to the performance on the unpreprocessed data. The single channel ICA
technique on the ERP based BCI produces much better results than results using the
lowpass filter. Whereas the appropriate number of averages improves the signal to
noise rate of P300 activities which helps to achieve a better classification. These
advantages will lead to a reliable and practical BCI for use outside of the clinical
laboratory
Microstimulation and multicellular analysis: A neural interfacing system for spatiotemporal stimulation
Willfully controlling the focus of an extracellular stimulus remains a significant challenge in the development of neural prosthetics and therapeutic devices. In part, this challenge is due to the vast set of complex interactions between the electric fields induced by the microelectrodes and the complex morphologies and dynamics of the neural tissue. Overcoming such issues to produce methodologies for targeted neural stimulation requires a system that is capable of (1) delivering precise, localized stimuli a function of the stimulating electrodes and (2) recording the locations and magnitudes of the resulting evoked responses a function of the cell geometry and membrane dynamics. In order to improve stimulus delivery, we developed microfabrication technologies that could specify the electrode geometry and electrical properties. Specifically, we developed a closed-loop electroplating strategy to monitor and control the morphology of surface coatings during deposition, and we implemented pulse-plating techniques as a means to produce robust, resilient microelectrodes that could withstand rigorous handling and harsh environments. In order to evaluate the responses evoked by these stimulating electrodes, we developed microscopy techniques and signal processing algorithms that could automatically identify and evaluate the electrical response of each individual neuron. Finally, by applying this simultaneous stimulation and optical recording system to the study of dissociated cortical cultures in multielectode arrays, we could evaluate the efficacy of excitatory and inhibitory waveforms. Although we found that the proximity of the electrode is a poor predictor of individual neural excitation thresholds, we have shown that it is possible to use inhibitory waveforms to globally reduce excitability in the vicinity of the electrode. Thus, the developed system was able to provide very high resolution insight into the complex set of interactions between the stimulating electrodes and populations of individual neurons.Ph.D.Committee Chair: Stephen P. DeWeerth; Committee Member: Bruce Wheeler; Committee Member: Michelle LaPlaca; Committee Member: Robert Lee; Committee Member: Steve Potte
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