Sep 26, 2010
Change in brain activity through virtual reality-based brain-machine communication in a chronic tetraplegic subject with muscular dystrophy
Change in brain activity through virtual reality-based brain-machine communication in a chronic tetraplegic subject with muscular dystrophy.
BMC Neurosci. 2010 Sep 16;11(1):117
Authors: Hashimoto Y, Ushiba J, Kimura A, Liu M, Tomita Y
ABSTRACT: BACKGROUND: For severely paralyzed people, a brain-computer interface (BCI) provides a way of re-establishing communication. Although subjects with muscular dystrophy (MD) appear to be potential BCI users, the actual long-term effects of BCI use on brain activities in MD subjects have yet to be clarified. To investigate these effects, we followed BCI use by a chronic tetraplegic subject with MD over 5 months. The topographic changes in an electroencephalogram (EEG) after long-term use of the virtual reality (VR)-based BCI were also assessed. Our originally developed BCI system was used to classify an EEG recorded over the sensorimotor cortex in real time and estimate the user's motor intention (MI) in 3 different limb movements: feet, left hand, and right hand. An avatar in the internet-based VR was controlled in accordance with the results of the EEG classification by the BCI. The subject was trained to control his avatar via the BCI by strolling in the VR for 1 hour a day and then continued the same training twice a month at his home. RESULTS: After the training, the error rate of the EEG classification decreased from 40% to 28%. The subject successfully walked around in the VR using only his MI and chatted with other users through a voice-chat function embedded in the internet-based VR. With this improvement in BCI control, event-related desynchronization (ERD) following MI was significantly enhanced (p < 0.01) for feet MI (from -29% to -55%), left-hand MI (from -23% to -42%), and right-hand MI (from -22% to -51%). CONCLUSIONS: These results show that our subject with severe MD was able to learn to control his EEG signal and communicate with other users through use of VR navigation and suggest that an internet-based VR has the potential to provide paralyzed people with the opportunity for easy communication.
Dec 13, 2009
USA Today reports about a new device that uses brain waves to allow players to manipulate a sphere within a clear 10-inch-tall training tower, analogous to Yoda and Luke Skywalker's abilities in the Star Wars films. The Force Trainer is expected to be priced at $90 to $100.
Image is from USA Today article
Sep 21, 2009
Neurofeedback-based motor imagery training for brain-computer interface (BCI).
J Neurosci Methods. 2009 Apr 30;179(1):150-6
Authors: Hwang HJ, Kwon K, Im CH
In the present study, we propose a neurofeedback-based motor imagery training system for EEG-based brain-computer interface (BCI). The proposed system can help individuals get the feel of motor imagery by presenting them with real-time brain activation maps on their cortex. Ten healthy participants took part in our experiment, half of whom were trained by the suggested training system and the others did not use any training. All participants in the trained group succeeded in performing motor imagery after a series of trials to activate their motor cortex without any physical movements of their limbs. To confirm the effect of the suggested system, we recorded EEG signals for the trained group around sensorimotor cortex while they were imagining either left or right hand movements according to our experimental design, before and after the motor imagery training. For the control group, we also recorded EEG signals twice without any training sessions. The participants' intentions were then classified using a time-frequency analysis technique, and the results of the trained group showed significant differences in the sensorimotor rhythms between the signals recorded before and after training. Classification accuracy was also enhanced considerably in all participants after motor imagery training, compared to the accuracy before training. On the other hand, the analysis results for the control EEG data set did not show consistent increment in both the number of meaningful time-frequency combinations and the classification accuracy, demonstrating that the suggested system can be used as a tool for training motor imagery tasks in BCI applications. Further, we expect that the motor imagery training system will be useful not only for BCI applications, but for functional brain mapping studies that utilize motor imagery tasks as well.
Neurofeedback and brain-computer interface clinical applications.
Int Rev Neurobiol. 2009;86:107-17
Authors: Birbaumer N, Ramos Murguialday A, Weber C, Montoya P
Most of the research devoted to BMI development consists of methodological studies comparing different online mathematical algorithms, ranging from simple linear discriminant analysis (LDA) (Dornhege et al., 2007) to nonlinear artificial neural networks (ANNs) or support vector machine (SVM) classification. Single cell spiking for the reconstruction of hand movements requires different statistical solutions than electroencephalography (EEG)-rhythm classification for communication. In general, the algorithm for BMI applications is computationally simple and differences in classification accuracy between algorithms used for a particular purpose are small. Only a very limited number of clinical studies with neurological patients are available, most of them single case studies. The clinical target populations for BMI-treatment consist primarily of patients with amyotrophic lateral sclerosis (ALS) and severe CNS damage including spinal cord injuries and stroke resulting in substantial deficits in communication and motor function. However, an extensive body of literature started in the 1970s using neurofeedback training. Such training implemented to control various EEG-measures provided solid evidence of positive effects in patients with otherwise pharmacologically intractable epilepsy, attention deficit disorder, and hyperactivity ADHD. More recently, the successful introduction and testing of real-time fMRI and a NIRS-BMI opened an exciting field of interest in patients with psychopathological conditions.
Dec 01, 2008
Brain-machine interface via real-time fMRI: Preliminary study on thought-controlled robotic arm.
Neurosci Lett. 2008 Nov 18;
Authors: Lee JH, Ryu J, Jolesz FA, Cho ZH, Yoo SS
Real-time functional MRI (rtfMRI) has been used as a basis for brain-computer interface (BCI) due to its ability to characterize region-specific brain activity in real-time. As an extension of BCI, we present an rtfMRI-based brain-machine interface (BMI) whereby 2-dimensional movement of a robotic arm was controlled by the regulation (and concurrent detection) of regional cortical activations in the primary motor areas. To do so, the subjects were engaged in the right- and/or left-hand motor imagery tasks. The blood oxygenation level dependent (BOLD) signal originating from the corresponding hand motor areas was then translated into horizontal or vertical robotic arm movement. The movement was broadcasted visually back to the subject as a feedback. We demonstrated that real-time control of the robotic arm only through the subjects' thought processes was possible using the rtfMRI-based BMI trials.
Jul 29, 2008
Auditory and Spatial Navigation Imagery in Brain-Computer Interface using Optimized Wavelets.
J Neurosci Methods. 2008 Jul 6;
Authors: Cabrera AF, Dremstrup K
Features extracted with optimized wavelets were compared with standard methods for a Brain-Computer Interface driven by non-motor imagery tasks. Two non-motor imagery tasks were used, Auditory Imagery of a familiar tune and Spatial Navigation Imagery through a familiar environment. The aims of this study were to evaluate which method extracts features that could be best differentiated and determine which channels are best suited for classification. EEG activity from 18 electrodes over the temporal and parietal lobes of nineteen healthy subjects was recorded. The features used were autoregressive and reflection coefficients extracted using autoregressive modeling with several model orders and marginals of the wavelet spaces generated by the Discrete Wavelet Transform (DWT). An optimization algorithm with 4 and 6 taps filters and mother wavelets from the Daubechies family were used. The classification was performed for each single channel and for all possible combination of two channels using a Bayesian Classifier. The best classification results were found using the marginals of the Optimized DWT spaces for filters with 6 taps in a 2 channels classification basis. Classification using 2 channels was found to be significantly better than using 1 channel (p<<0.01). The marginals of the optimized DWT using 6 taps filters showed to be significantly better than the marginals of the Daubechies family and autoregressive coefficients. The influence of the combination of number of channels and feature extraction method over the classification results was not significant (p=0.97).
Jan 05, 2008
Towards an independent brain-computer interface using steady state visual evoked potentials.
Clin Neurophysiol. 2008 Feb;119(2):399-408
Authors: Allison BZ, McFarland DJ, Schalk G, Zheng SD, Jackson MM, Wolpaw JR
OBJECTIVE: Brain-computer interface (BCI) systems using steady state visual evoked potentials (SSVEPs) have allowed healthy subjects to communicate. However, these systems may not work in severely disabled users because they may depend on gaze shifting. This study evaluates the hypothesis that overlapping stimuli can evoke changes in SSVEP activity sufficient to control a BCI. This would provide evidence that SSVEP BCIs could be used without shifting gaze. METHODS: Subjects viewed a display containing two images that each oscillated at a different frequency. Different conditions used overlapping or non-overlapping images to explore dependence on gaze function. Subjects were asked to direct attention to one or the other of these images during each of 12 one-minute runs. RESULTS: Half of the subjects produced differences in SSVEP activity elicited by overlapping stimuli that could support BCI control. In all remaining users, differences did exist at corresponding frequencies but were not strong enough to allow effective control. CONCLUSIONS: The data demonstrate that SSVEP differences sufficient for BCI control may be elicited by selective attention to one of two overlapping stimuli. Thus, some SSVEP-based BCI approaches may not depend on gaze control. The nature and extent of any BCI's dependence on muscle activity is a function of many factors, including the display, task, environment, and user. SIGNIFICANCE: SSVEP BCIs might function in severely disabled users unable to reliably control gaze. Further research with these users is necessary to explore the optimal parameters of such a system and validate online performance in a home environment.