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What Limits the Performance of Current Invasive Brain Machine Interfaces?

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Review

What Limits the Performance of Current Invasive Brain Machine Interfaces?

Gytis Baranauskas. Front Syst Neurosci.

Abstract

The concept of a brain-machine interface (BMI) or a computer-brain interface is simple: BMI creates a communication pathway for a direct control by brain of an external device. In reality BMIs are very complex devices and only recently the increase in computing power of microprocessors enabled a boom in BMI research that continues almost unabated to this date, the high point being the insertion of electrode arrays into the brains of 5 human patients in a clinical trial run by Cyberkinetics with few other clinical tests still in progress. Meanwhile several EEG-based BMI devices (non-invasive BMIs) were launched commercially. Modern electronics and dry electrode technology made possible to drive the cost of some of these devices below few hundred dollars. However, the initial excitement of the direct control by brain waves of a computer or other equipment is dampened by large efforts required for learning, high error rates and slow response speed. All these problems are directly related to low information transfer rates typical for such EEG-based BMIs. In invasive BMIs employing multiple electrodes inserted into the brain one may expect much higher information transfer rates than in EEG-based BMIs because, in theory, each electrode provides an independent information channel. However, although invasive BMIs require more expensive equipment and have ethical problems related to the need to insert electrodes in the live brain, such financial and ethical costs are often not offset by a dramatic improvement in the information transfer rate. Thus the main topic of this review is why in invasive BMIs an apparently much larger information content obtained with multiple extracellular electrodes does not translate into much higher rates of information transfer? This paper explores possible answers to this question by concluding that more research on what movement parameters are encoded by neurons in motor cortex is needed before we can enjoy the next generation BMIs.

Keywords: brain computer interface; brain machine interface; extracellular recordings; information; multichannel recordings; throughput.

Figures

Figure 1
Figure 1
The number of papers published each year with terms “Brain Machine Interface,” PubMed search data.
Figure 2
Figure 2
Information transfer rates or throughput estimated for center-out reaching task (blue and black circles) and for “GO” cue task (red circle) in BMI papers. Most data were taken from supplemental material of Gilja et al. (2012) while the “GO” cue data are for Santhanam et al. (2006) study. The line represents a linear fit for all blue data points; there was no significant increase in information transfer rates in this time period.

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