Introduction
Many physiological disorders such as Amyotrophic Lateral Sclerosis (ALS) or injuries such as high-level spinal cord injury can disrupt the communication path between the brain and the body. People with severe motor disabilities may lose all voluntary muscle control, including eye movements. These people are forced to accept a reduced quality of life, resulting in dependence on caretakers and escalating social costs [VAU03]. Most of the existing assistive technology devices for these patients are not possible because these devices are dependant on motor activities from specific parts of the body. Alternative control paradigms for these individuals are thus desirable.
Over the last two decades, brain-computer interface (BCI) has emerged as a new frontier in assistive technology (AT) since it could provide an alternative communication channel between a user’s brain and the outside world [WOL02]. Other terms that are also used in the literature for referring to a BCI system include: brain interface (BI), direct brain interface (DBI), and brain machine interface (BMI).
A BCI system allows individuals with motor disabilities to control objects in their environments (such as a light switch in their room or television, wheelchairs, neural prosthesis and computers) using their brain signals only.This could be accomplished by measuring specific features of the user’s brain activity that relate to his/her intent to perform the control. This specific type of brain activity is termed a “neurological phenomenon”. As an example, when a particular movement such as right index finger flexion is performed, specific neurological phenomena that correspond to that movement are generated. The corresponding neurological phenomena are then translated into signals that are eventually used to control devices [MAS07].
Figure 1 shows a traditional BCI system in which a person controls a device in an operating environment (e.g., a powered wheelchair in a house) through a series of functional components (revised from [FAT06]). In this context, the user’s brain activity is used to generate IC commands that operate the BCI system. The user monitors the state of the device to determine the result of his/her control efforts.
Figure 1 . The functional components of a BCI system
The building components of a BCI system (shown in Figure 1) have the following tasks: the electrodes placed on the head of the user record the brain signal (e.g., electroencephalography (EEG) signals from the scalp, electrocorticography (ECoG) signals from the brain or neuronal activity recorded using microelectrodes implanted in the brain). The ‘artifact processor’ block deals with artifacts in the EEG signals after the signals have been amplified. This block can either remove artifacts from the EEG signals or can simply mark some EEG epochs as artifact-contaminated. The ‘feature generator’ block transforms the resultant signals into feature values that relate to the underlying neurological phenomena employed by the user for control. For example, if the user is using the power of his/her Mu (8-12Hz) rhythm for the purpose of control, the feature generator could continually generate features relating to the power-spectral estimates of the user’s Mu rhythms. The feature generator generally consists of three components: the ‘signal enhancement’, the ‘feature extraction’, and the ‘feature selection’ components, as shown in Figure 1.
In some BCI designs, ‘signal enhancement’ or some of form of ‘pre-processing’ is performed to increase the signal-to-noise ratio of the brain signal(s) prior to extracting the features. To reduce the dimensionality of the problem, it is desired to reduce the number of features and/or the number of EEG channels. ‘Feature selection’ could be performed after or at the feature extraction stage to reduce the number of features and/or EEG channels used. Ideally, the features that are meaningful or useful in the classification stage are identified and chosen, while others are omitted.
The ‘feature translator’ block translates the features into logical control signals, e.g., 0 and 1 where 0 denotes NC and 1 denotes IC. The translation algorithm uses linear classification methods (e.g., linear discriminant analysis) or nonlinear ones (e.g., neural networks). As shown in Figure 1 , a feature translator may consist of two components: ‘feature classification’ and ‘post-processing’. The main aim of the feature classification component is to classify the features into logical control signals. Post-processing methods such as a moving average may be used after feature classification to reduce the number of activations of the system.
The control interface translates the logical control signals from the feature translator into semantic control signals that are appropriate for the particular type of device used. Finally, the device controller translates the semantic control signals into physical control signals that are used by the device. For more detail refer to [MAS07].
The rest of this article explores some FAQs involving the BCI systems. Please go to the main page for more discussion on the signal processing aspects of BCI systems.
Frequently Asked Questions (FAQs)
1) Who is the target of BCI Systems?
Many people think that BCI systems can be used by anyone. This is simply not true (at least at his stage). The potential users of BCI systems include [WOL06]:
- Individuals who are truly unlocked.
- Individuals who have a very limited capacity for control, e.g., useful eye movement.
- Individuals who retain substantial neuromuscular control.
Currently, the second class is the main target of BCI communication and applications. This is because BCI systems are designed for individuals with motor disabilities to communicate with the outside world. The number of control options that BCI systems currently provide is also very limited at the time being. So only an individual who really needs to use a BCI system (and does not have any other useful communication channel) may be willing to use a BCI system in the long run.
2) Can we use executed movements instead of attempted movements in our BCI studies?
It has been stated that the specific cortical pattern associated with the variation of the parameters of motor control during motor imagery and motor execution are the same [ROM00].
References
[FAT06] Fatourechi, M., Bashashati, A., Ward, R. K., and Birch, G. E., “EOG and EMG Artifacts in Brain Interface Systems: a Survey”, Clinical Neurophysiology, Vol.118, No.3, Mar 2007 pp.480-494 (Invited Paper)
[MAS07] S. G. Mason, A. Bashashati, M. Fatourechi, K. F. Navarro and G. E. Birch, "A comprehensive survey of brain interface technology designs", Ann. Biomed. Eng., vol. 35, no.2, pp. 137-169, Feb.2007.
[ROM00] Romero, D. H., et. al, “Event-related potentials as a function of movement parameter variations during motor imagery and isometric action”, Behavioral Brain Research, Vol.117, No.1-2, Dec. 2000, pp.83-96
[VAU03] T. Vaughan, W. J. Heetderks, L. J. Trejo, W. Z. Rymer, M. Wienrich, M. M. Moore, A. Kubler, B. H. Dobkin, N. Birbaumer, E. Donchin, E. W. Wolpaw and J. W. R, "Brain-computer interface technology: a review of the second international meeting", IEEE Trans. Neural Syst. Rehab. Eng., vol. 11, no.2, pp. 94-109, 2003.
[WOL02] J. R. Wolpaw, N. Birbaumer, D. J. McFarland, G. Pfurtscheller and T. M. Vaughan, "Brain-computer interfaces for communication and control", Clin. Neurophysiol., vol. 113, no.6, pp. 767-791, Jun.2002.
[WOL06] Wolpaw, J. R., et.al, “BCI MEeting 2005- Workshop on Signals and recording methods”, IEEE Trans. Neural Systems and Rehab. Eng. Vol. 14, No.2, June 2006, pp. 138.141
