Introduction
So far most BCI researchers have focused their attention on “synchronized” control applications. In synchronized applications, a user can initiate a command only during specific times specified by the system (see Figure 1(a)). In these systems, the users are required to generate an intentional control (IC) command during the periods allowed by the BCI system. In the example shown in Figure 1 (a), the user should generate one of IC1 or IC2 commands during the control period (the control period is shown as a ‘box’).
In a Self-paced BCI system (also called asynchronous BCI system), users can control object(s) at their own pace, i.e. whenever they wish (see Figure 1(b)) [MAS00]. This is in contrast to Synchronized BCI systems, where the users can control objects only in periods specified by the system. In the example shown in Figure 1 (b), the user is in the no control (NC) state at all times, except for those periods when he/she initiates an IC command. In the latter case, the system will be in an IC state. During NC periods, the user can be idle, thinking about a problem or performing any action other than attempting to control the device. This property of SBCI systems that allows them to support the presence of NC periods is called “NC support”. Whenever a BCI system involves control actions with periods of inaction, it needs to have NC support.
Figure 1 . Synchronized vs. self-paced control. (a) In a synchronized BCI system, control can be done only in certain intervals specified by the system. (b) In a self-paced BCI system, the control is done at the user’s own pace.
Synchronized BCI systems usually require the user to initiate an IC command during the control periods. In other words, during the control periods, the users are expected to be engaged with controlling the device. For this reason, they usually do not support the “NC” periods. In some cases, the output of the system might even become unstable if an IC command is not issued.
Although self-paced BCI systems give more freedom to the users, this freedom comes at a price. From the designer point of view, it is much harder to design a self-paced BCI system than a Synchronized BCI systems. This is because the control should not be able to identify Intentional Control commands issued by the user, but also not activated during the periods of No Control.
The performance of self-paced BCI systems depends on a) How well the system detects the control commands (which is called the true positive , TP, rate).b) How well the system performs in the situations that the subject does not intend to control (which is quantified using the false positive, FP, rate). The performance of the system during the so-called No Control (NC) periods is of utter importance. For example, the system proposed in [AND95] can be considered as a type of self-paced BCI system, as it should separate a specific mental task (mental arithmetic) from a baseline task. The average detection of the baseline task was, only 67% and it only improved when the output of classifier was averaged over a number of successive segments of data. This performance will be a huge drawback in the real-life simulations, as we are seeking FP «1% and not around 33%, as reported in [AND95].
Self-paced BCI Systems
The concept of self-paced control started in early 90’s with the development of the outlier processing method (OPM), which aimed at detecting movement-related potentials (MRPs) in the EEG signals [BIR93]. The results from this work were promising as true positive (TP) rates greater than 90% were achieved on a thumb movement task. However, its poor performance over NC epochs (FP rates ranging from 10% to 30%) restricted its use as a BCI system.
To overcome the vulnerabilities of OPM, another SBCI system called the low frequency- asynchronous switch design (LF-ASD) was later proposed in 2000 by Mason and Birch [MAS00] . Similar to OPM, LF-ASD is also designed to detect MRPs in the EEG signals. It uses features extracted from the 0.1- 4Hz band in six bipolar EEG channels recorded from F1- FC1, Fz- FCz, F2- FC2, FC1- C1, FCz-Cz and FC2- C2 on the scalp, sampled at 128 Hz. A detector that was a simplified version of the discrete wavelet transform was applied as the feature extractor and a 1-nearest neighbor (1-NN) classifier was used as the feature classifier. By analyzing the EEG signals of five individuals, the features related to MRP (or IC) periods showed a definite difference from those in NC periods [MAS00]. During the past few years, several changes have been applied to the structure of LF-ASD to improve its performance [BOR04,FAT06]. Despite these improvements, the performance of the LF-ASD is still not suitable for many practical applications. This is because since LF-ASD generates an output every 1/16th of a second, an average FP of 1% is ranslated into one false positive every 6.25 seconds, while the detection rate of IC commands is less than 50%. For most practical applications, generating such a high FP rate, may result in excessive user frustration.
Another SBCI design, which improves upon the feature extractor of LF-ASD, is proposed by Yom-tov et.al[YOM03]. The proposed method combines the LF-ASD feature extractor with a matched filter, resulting in a hybrid detector. This method also results in a high FP rate. For FP rates<2%, the TP rates are lower than 30%. This system generates an output every 1/25th of a second. An FP rate of 2% is translated into one false positive, every two seconds. As a result, the high amount of FPs limits the application of the proposed design.
While the above studies are based on features extracted from EEG signals, researchers from the University of Michigan have focused on extracting features from ECoG signals [GRA04, LEV99, LEV00]. To detect IC commands, their designs either use the cross-correlation with a template [LEV00] or the energy of wavelet packet transform [GRA04]. In these studies, a threshold-based classifier is used for classifying the features. While these systems usually achieve TP>50%, their performance on NC epochs is not very clear. First, none of these studies has determined the number of NC epochs. Moreover, to quantify the false positives, a new metric called the false discovery rate (FDR, i.e., the percentage of total activations of the switch that were false) was used [HUG99]. Since the number and the length of NC epochs is not determined in these studies, it is impossible to calculate the FP rate for these systems. In a recent study by this group, the reported FDR were in the range of 0% to 82% with 24 out of the 31 reported FDRs being higher than 10% [GRA04]. However, since the numbers of IC and NC epochs were not specifically determined, no comment can be made on the performance of these systems over NC data.
References
[AND95] C. W. Anderson, S. V. Devulapalli, and E. A. Stolz, “EEG signal classification with different signal representations,” Neural Networks for Signal Processing Piscataway, NJ: IEEE Press, 1995, vol. V, pp. 475-483.
[BIR93] G. E. Birch, P. D. Lawrence and R. D. Hare, "Single Trial Processing of Event Related Potentials Using Outlier Information", IEEE Trans. Biomed. Eng., vol. 40, no.1, pp. 59-73, 1993.
[BOR04] J. F. Borisoff, S. G. Mason, A. Bashashati and G. E. Birch, "Brain-computer interface design for asynchronous control applications: improvements to the LF-ASD asynchronous brain switch", IEEE Trans. Biomed. Eng., vol. 51, no.6, pp. 985-992, Jun.2004.
[FAT06] Fatourechi, M., Bashashati, A., Birch, G.E. and Ward, R.K. “Automatic User Customization for Improving the Performance of an Asynchronous Brain Interface System”, Journal of Medical & Biological Engineering and Computing, Vol.44, No.12, Dec 2006, pp.1093-1104.
[GRA04] B. Graimann, J. E. Huggins, S. P. Levine and G. Pfurtscheller, "Toward a direct brain interface based on human subdural recordings and wavelet-packet analysis", IEEE Trans. Biomed. Eng., vol. 51, no.6, pp. 954-962, Jun.2004.
[HUG99] J. E. Huggins, S. P. Levine, S. L. Bement, R. K. Kushwaha, L. A. Schuh, E. A. Passaro, M. M. Rohde, D. A. Ross, K. V. Elisevich and B. J. Smith, "Detection of Event-Related Potentials for Development of a Direct Brain Interface", J Clinical Neurophysiol, vol. 16, no.5, pp. 448-455, Sep.1999.
[LEV99] S. P. Levine, J. E. Huggins, S. L. Bement, R. K. Kushwaha, L. A. Schuh, E. A. Passaro, M. M. Rohde and D. A. Ross, "Identification of Electrocorticogram Patterns as the Basis for a Direct Brain Interface", J Clinical Neurophysiol, vol. 16, no.5, pp. 439-447, Sep.1999.
[LEV00] S. P. Levine, J. E. Huggins, S. L. Bement, R. K. Kushwaha, L. A. Schuh, M. M. Rohde, E. A. Passaro, D. A. Ross, K. V. Elisevich and B. J. Smith, "A Direct Brain Interface Based on Event-Related Potentials", IEEE Trans. Rehab. Eng., vol. 8, no.2, pp. 180-185, Jun.2000.
[MAS00] S. G. Mason and G. E. Birch, "A brain-controlled switch for asynchronous control applications", IEEE Trans. Biomed. Eng, vol. 47, no.10, pp. 1297-1307, Oct.2000.
[YOM03] E. Yom-Tov and G. F. Inbar, "Detection of Movement-Related Potentials from the Electro-Encephalogram for possible use in a Brain-Computer Interface", Medical and Biological Engineering and Computing, vol. 41, no.1, pp. 85-93, Jan.2003.
