fMRI Brain-Computer Interfaces
TL;DR: A general architecture of an fMRI-BCI is presented, with descriptions of each of its subsystems, and factors influencing their performance, and a variety of approaches toward signal acquisition, preprocessing, analysis, and feedback are described.
Abstract: Brain-computer interfaces based on fMRI enable real-time feedback of circumscribed brain regions to learn volitional regulation of those regions. This is an emerging field of intense research, with potential for multiple applications in neuroscientific research in brain plasticity and reorganization, movement restoration due to stroke, clinical rehabilitation of emotional disorders, quality assurance of fMRI experiments, and teaching functional imaging. This article presents a general architecture of an fMRI-BCI, with descriptions of each of its subsystems, and factors influencing their performance. The study has attempted to describe and compare a variety of approaches toward signal acquisition, preprocessing, analysis, and feedback. Technological advancement in higher-field MRI scanners, data acquisition sequences and image reconstruction techniques, preprocessing algorithms to correct for artifacts, more intelligent and robust analysis and interpretation methods, and faster feedback and visualization technology are anticipated to make fMRI-BCI widely applicable. FMRI-BCI could potentially be used for training patients to learn self-regulation of specific brain areas for transferring them later on to a more portable EEG-BCI system. FMRI-BCI has the potential of establishing itself as a tool for neuroscientific research and experimentation and also as an aid for psychophysiological treatment.
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TL;DR: This document provides a review of the techniques and therapies used in gait rehabilitation after stroke and examines the possible benefits of including assistive robotic devices and brain-computer interfaces in this field, according to a top-down approach, in which rehabilitation is driven by neural plasticity.
Abstract: This document provides a review of the techniques and therapies used in gait rehabilitation after stroke. It also examines the possible benefits of including assistive robotic devices and brain-computer interfaces in this field, according to a top-down approach, in which rehabilitation is driven by neural plasticity. The methods reviewed comprise classical gait rehabilitation techniques (neurophysiological and motor learning approaches), functional electrical stimulation (FES), robotic devices, and brain-computer interfaces (BCI). From the analysis of these approaches, we can draw the following conclusions. Regarding classical rehabilitation techniques, there is insufficient evidence to state that a particular approach is more effective in promoting gait recovery than other. Combination of different rehabilitation strategies seems to be more effective than over-ground gait training alone. Robotic devices need further research to show their suitability for walking training and their effects on over-ground gait. The use of FES combined with different walking retraining strategies has shown to result in improvements in hemiplegic gait. Reports on non-invasive BCIs for stroke recovery are limited to the rehabilitation of upper limbs; however, some works suggest that there might be a common mechanism which influences upper and lower limb recovery simultaneously, independently of the limb chosen for the rehabilitation therapy. Functional near infrared spectroscopy (fNIRS) enables researchers to detect signals from specific regions of the cortex during performance of motor activities for the development of future BCIs. Future research would make possible to analyze the impact of rehabilitation on brain plasticity, in order to adapt treatment resources to meet the needs of each patient and to optimize the recovery process.
478 citations
TL;DR: The current status and future prospects of BCI technology and its clinical applications are discussed, BCI is defined, the BCI-relevant signals from the human brain are reviewed, and the functional components of BCIs are described.
Abstract: Braincomputer interfaces (BCIs) allow their users to communicate or control external devices using brain signals rather than the brain's normal output pathways of peripheral nerves and muscles. Motivated by the hope of restoring independence to severely disabled individuals and by interest in further extending human control of external systems, researchers from many fields are engaged in this challenging new work. BCI research and development has grown explosively over the past two decades. Efforts have begun recently to provide laboratory-validated BCI systems to severely disabled individuals for real-world applications. In this paper, we discuss the current status and future prospects of BCI technology and its clinical applications. We will define BCI, review the BCI-relevant signals from the human brain, and describe the functional components of BCIs. We will also review current clinical applications of BCI technology and identify potential users and potential applications. Lastly, we will discuss current limitations of BCI technology, impediments to its widespread clinical use, and expectations for the future.
439 citations
14 Jul 2009
TL;DR: An evaluation to assess the usability of the NeuroSky's MindSet by defining a model of attention to fuse attention signals with user-generated data in a Second Life assessment exercise suggests that the MS provides accurate readings regarding attention.
Abstract: This paper presents the results of a usability evaluation of the NeuroSky's MindSet (MS). Until recently most Brain Computer Interfaces (BCI) have been designed for clinical and research purposes partly due to their size and complexity. However, a new generation of consumer-oriented BCI has appeared for the video game industry. The MS, a headset with a single electrode, is based on electro-encephalogram readings (EEG) capturing faint electrical signals generated by neural activity. The electrical signal across the electrode is measured to determine levels of attention (based on Alpha waveforms) and then translated into binary data. This paper presents the results of an evaluation to assess the usability of the MS by defining a model of attention to fuse attention signals with user-generated data in a Second Life assessment exercise. The results of this evaluation suggest that the MS provides accurate readings regarding attention, since there is a positive correlation between measured and self-reported attention levels. The results also suggest there are some usability and technical problems with its operation. Future research is presented consisting of the definition a standardized reading methodology and an algorithm to level out the natural fluctuation of users' attention levels if they are to be used as inputs.
182 citations
Cites methods from "fMRI Brain-Computer Interfaces"
...In 2003, a taxonomy by Mason and Birch [5] identified MEG, PET and fMRI as unsuitable for BCI applications, due to the equipment required to perform and analyze the scan in real-time, but more recent attempts to use fMRI as a BCI input device have demonstrated significant future potential in this area [6]....
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TL;DR: In this review, fundamental principles, recent developments, applications and future directions and challenges of NIRS-based and fMRI-based BCIs are considered.
177 citations
Cites background from "fMRI Brain-Computer Interfaces"
...…self-induced alterations of brain activity in cortical and subcortical areas were observed (deCharms et al., 2004, 2005; Rota et al., 2008; Ruiz & Sitaram et al., 2008; Sitaram, 2007; Sitaram and Lee et al., 2008; Weiskopf & Mathiak et al., 2004; Weiskopf & Scharnowski et al., 2004; Weiskopf &…...
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04 Mar 2010
TL;DR: The efforts in developing brain-computer interfaces (BCIs) which synergistically integrate computer vision and human vision so as to construct a system for image triage are described and two architectures for this type of cortically coupled computer vision are described.
Abstract: Our society's information technology advancements have resulted in the increasingly problematic issue of information overload-i.e., we have more access to information than we can possibly process. This is nowhere more apparent than in the volume of imagery and video that we can access on a daily basis-for the general public, availability of YouTube video and Google Images, or for the image analysis professional tasked with searching security video or satellite reconnaissance. Which images to look at and how to ensure we see the images that are of most interest to us, begs the question of whether there are smart ways to triage this volume of imagery. Over the past decade, computer vision research has focused on the issue of ranking and indexing imagery. However, computer vision is limited in its ability to identify interesting imagery, particularly as ?interesting? might be defined by an individual. In this paper we describe our efforts in developing brain-computer interfaces (BCIs) which synergistically integrate computer vision and human vision so as to construct a system for image triage. Our approach exploits machine learning for real-time decoding of brain signals which are recorded noninvasively via electroencephalography (EEG). The signals we decode are specific for events related to imagery attracting a user's attention. We describe two architectures we have developed for this type of cortically coupled computer vision and discuss potential applications and challenges for the future.
151 citations
References
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TL;DR: The approach is predicated on an extension of the general linear model that allows for correlations between error terms due to physiological noise or correlations that ensue after temporal smoothing, and uses the effective degrees of freedom associated with the error term.
2,647 citations
TL;DR: How researchers are using multi-voxel pattern analysis methods to characterize neural coding and information processing in domains ranging from visual perception to memory search is reviewed.
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"fMRI Brain-Computer Interfaces" refers methods or result in this paper
...In contrast to the conventional analysis, recent work shows that the sensitivity of human neuroimaging may be improved by taking into account the spatial pattern of brain activity [32]‐[ 35 ]....
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...Several studies have previously reported offline classification of fMRI signals using various pattern-based methods such as multilayer neural networks [ 35 ], Fisher Linear Discriminant (FLD) classifier [36], and support vector machines (SVMs)....
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TL;DR: The RETROICOR method is found to perform well for both respiration‐ and cardiac‐induced noise without imposing spatial filtering on the correction.
Abstract: Respiration effects and cardiac pulsatility can induce signal modulations in functional MR image time series that increase noise and degrade the statistical significance of activation signals. A simple image-based correction method is described that does not have the limitations of k-space methods that preclude high spatial frequency correction. Low-order Fourier series are fit to the image data based on time of each image acquisition relative to the phase of the cardiac and respiratory cycles, monitored using a photoplethysmograph and pneumatic belt, respectively. The RETROICOR method is demonstrated using resting-state experiments on three subjects and compared with the k-space method. The method is found to perform well for both respiration- and cardiac-induced noise without imposing spatial filtering on the correction. Magn Reson Med 44:162‐167, 2000. © 2000 Wiley-Liss, Inc.
1,913 citations
"fMRI Brain-Computer Interfaces" refers methods in this paper
...Techniques have been developed to remove cardiorespiratory artifacts during offline analysis [22]–[24], but they have not been adapted to online processing for real-time fMRI....
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TL;DR: Using gradient‐echo echo‐planar MRI, a local signal increase is observed in the human brain during task activation, suggesting a local decrease in blood deoxyhemoglobin concentration and an increase in blood oxygenation.
Abstract: Using gradient-echo echo-planar MRI, a local signal increase of 4.3 +/- 0.3% is observed in the human brain during task activation, suggesting a local decrease in blood deoxyhemoglobin concentration and an increase in blood oxygenation. Images highlighting areas of signal enhancement temporally correlated to the task are created.
1,877 citations
"fMRI Brain-Computer Interfaces" refers background in this paper
...If two neighboring voxels differ in intrinsic brightness by 20%, then a motion of 10% of a voxel dimension can result in a 2% signal change—comparable to the BOLD signal change at 1.5 T subsequent to neural activation [18]....
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TL;DR: This work has shown that it is possible to accurately decode a person's conscious experience based only on non-invasive measurements of their brain activity, and can also be extended to other types of mental state, such as covert attitudes and lie detection.
Abstract: Recent advances in human neuroimaging have shown that it is possible to accurately decode a person's conscious experience based only on non-invasive measurements of their brain activity. Such 'brain reading' has mostly been studied in the domain of visual perception, where it helps reveal the way in which individual experiences are encoded in the human brain. The same approach can also be extended to other types of mental state, such as covert attitudes and lie detection. Such applications raise important ethical issues concerning the privacy of personal thought.
1,714 citations
"fMRI Brain-Computer Interfaces" refers background in this paper
...Univariate methods seek to find out how a particular perceptual or cognitive state is encoded by measuring brain activity from many thousands of locations repeatedly but then analyzing each location separately [27]....
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