RUI : Control of a Robotic Manipulator via a Brain-Computer Interface Project Description 1 Problem Definition

Nearly two million people in the United States and far more worldwide suffer from neuromuscular disorders that impair muscle control such as amyotrophic lateral sclerosis (ALS), brainstem stroke, cerebral palsy, and spinal cord injury. The most severely affected patients lose all voluntary muscle control, including eye movements and respiration, and become completely “locked in” to their bodies, unable to communicate in any way. Conventional assistive devices require some level of voluntary muscle control, and thus are not appropriate for people in this condition. Brain signals such as electroencephalographic activity (EEG) and electrocorticographic activity (ECoG), among others, can provide alternate, non-muscular channels for communication and control. A braincomputer interface (BCI) exploits these brain signals for communication directly from brain to an output device, independent of peripheral nerves and muscles. The goal of BCI research is to develop new assistive communication and control technology that will allow individuals with severe neuromuscular disabilities to express their wishes to caregivers, interact with their environment, or to operate a neuroprosthesis or personal computer, etc. Modern life-support technology can significantly extend the life spans of these individuals, potentially prolonging the personal, social, and economic burdens. The hope is that BCI technology will greatly improve the quality of life for these individuals and relieve some of the associated burdens for caregiving. Excellent reviews of BCI research are provided in [57] and [61]. Figure 1 shows a basic block diagram of a BCI system. The user encodes device commands via brain signals and the BCI decodes the signals and transfers them to the output device. Recent studies have shown that non-muscular communication is possible via a BCI. Currently, electrophysiological signals such as EEG, ECoG, and single unit recordings appear to be the most promising and practical signals for a BCI because they offer high temporal resolution and do not require bulky and expensive recording equipment. An illustration of the relative electrode sizes and placements is provided in Figure 2. Because EEG recording is non-invasive, it has been studied extensively and its capabilities for a BCI are well-established. However, compared to invasive methods (ECoG and single unit recordings), EEG has inferior spatial and temporal resolution and can be corrupted by muscular (electromyographic (EMG)) artifacts, which impose limits on the communication capacity. Single unit recordings offer superior spatial and temporal resolution. However, since the electrodes penetrate the brain, tissue damage occurs and the recordings can suffer from signal degradation due to encapsulation. These obstacles currently prohibit their clinical use in humans. An intermediate BCI methodology, using electrocorticographic activity (ECoG) recorded from the cortical surface, could be a powerful and practical alternative to these extremes. ECoG has higher spatial resolution than EEG (i.e., millimeters vs. centimeters [25] [28][37][38], broader bandwidth (i.e., 0-200 Hz vs. 0-40 Hz), higher amplitude (i.e., 50-100 μV maximum vs. 10-20 μV), and far less vulnerability to artifacts such as EMG [25][53]. At the same time, because ECoG is recorded by subdural electrode arrays and thus does not require electrodes that penetrate into cortex, it entails less clinical risk and is likely