Recent advances in brain-computer interfaces
read more
Citations
Combining BCI with Virtual Reality: Towards New Applications and Improved BCI
Creating the feedback loop: closed-loop neurostimulation.
Study of Electroencephalographic Signal Processing and Classification Techniques towards the use of Brain-Computer Interfaces in Virtual Reality Applications
Single trial independent component analysis for P300 BCI system
EEG Signal Classification Using Power Spectral Features and linear Discriminant Analysis: A Brain Computer Interface Application
References
Hierarchical mixtures of experts and the EM algorithm
Motor imagery and direct brain-computer communication
Brain–machine interfaces: past, present and future
Direct cortical control of 3d neuroprosthetic devices
Control of a two-dimensional movement signal by a noninvasive brain-computer interface in humans
Related Papers (5)
Talking off the top of your head: toward a mental prosthesis utilizing event-related brain potentials
Frequently Asked Questions (19)
Q2. What are the main advantages of the SVM?
The main advantages of the SVM are that it allows to achieve very good classification accuracy and that nonlinear classification functions can be easily implemented by using kernels.
Q3. What is the common strategy used to separate the neurophysiologic signals from background activity?
A strategy that is often used to separate these signals from background activity and noise is lowpass or bandpass filtering, optionally followed by downsampling.
Q4. How many electrodes are used to record the EEG?
The EEG is recorded at 2048 Hz sampling rate from thirty-two electrodes placed at the standard positions of the 10-20 international system.
Q5. What are examples of ERPs that can be used in BCIs?
Other examples for ERPs that can be used in BCIs are steady-state visual evoked potentialss (SSVEPs) and motorrelated potentials (MRPs).
Q6. What is the assumption underlying the application of ICA to EEG signals?
The assumption underlying the application of ICA to EEG signals is that the signals measured on the scalp are a linear and instantaneous mixture of signals from independent sources in the cortex, deeper brain structures, and noise [18].
Q7. What is the drawback of training SVMs?
A drawback is however, that training SVMs is computationally complex because regularization constants and kernel parameters are typically estimated with a cross-validation procedure.
Q8. What are the main advantages of FDA?
The main advantages of FDA are that it is a computationally and conceptually simple method and that very good classification accuracy can be achieved.
Q9. What are the common time domain features used in BCIs?
Besides the use for the EEG signals P300, SCP, and MRP, time domain features are also used in BCI systems based on neuronal ensemble activity.
Q10. What is the definition of a Bayesian analysis?
Through a Bayesian analysis the degree of regularization can be estimated automatically and quickly from training data without the need for time consuming cross-validation.
Q11. What is the idea behind the use of a BCI?
The idea underlying research on neuromotor prostheses is to use a BCI for controlling movement of limbs and to restore motor function in tetraplegics or amputees.
Q12. What is the way to overcome these problems?
One approach to overcome these problems, is to develop machine learning algorithms, with which subjects can immediately start using a BCI, without training.
Q13. What is the common use of neuronal ensemble activity in BCIs?
Neuronal ensemble activity can thus be employed as neurophysiological signal in BCIs, in particular in BCIs using microelectrode arrays [12].
Q14. Why is the steering of a wheelchair a complex task?
Because steering a wheelchair is a complex task and becausewheelchair control has to be extremely reliable, the possible movements of the wheelchair are strongly constrained in current prototype systems.
Q15. What is the main disadvantage of the SVM?
A second issue is that the loss function used in the SVM is designed for problems in which only binary yes/no outputs are needed.
Q16. Why did the able-bodied subjects achieve better results than the disabled subjects?
While due to fatigue or concentration problems not all ablebodied subjects achieved 100% classification accuracy, the bitrates for the able-bodied subjects were in general higher than those of the disabled subjects.
Q17. What are the types of brain signals that are used in BCIs?
The types of signals resulting from concentration on mental tasks together with the corresponding BCI paradigms are described in subsections IIB, II-C, and II-D.ERPs are stereotyped, spatio-temporal patterns of brain activity, occurring time-locked to an event, for example after presentation of a stimulus, before execution of a movement, or after the detection of a novel stimulus.
Q18. What are the steps to develop asynchronous BCI systems?
algorithms that can detect if the user wants to communicate via the BCI or is engaged in other activity have to be developed.
Q19. How many disabled subjects were tested in their study?
For three of the other four disabled subjects tested in their study 100% classification accuracy was also achieved and the maximal bitrate varied between 9 and 19 bits/min.