Silent speech interfaces

In this paper we review classification algorithms used to design brain-computer interface (BCI) systems based on electroencephalography (EEG). We briefly present the commonly employed algorithms and describe their critical properties. Based on the literature, we compare them in terms of performance and provide guidelines to choose the suitable classification algorithm(s) for a specific BCI.

We investigated the potential of the Utah Intracortical Electrode Array (UIEA) to provide signals for a brain-computer interface (BCI). The UIEA records from small populations of neurons which have an average signal-to-noise ratio (SNR) of 6:1. We provide specific examples that show the activities of these populations of neurons contain sufficient information to perform control tasks. Results from a simple stimulus detection task using these signals as inputs confirm that the number of neurons present in a recording is significant in determining task performance. Increasing the number of units in a recording decreases the sensitivity of the response to the stimulus; decreasing the number of units in the recording, however, increases the variability of the response to the stimulus. We conclude that recordings from small populations of neurons, not single units, provide a reliable source of sufficiently stimulus selective signals which should be suitable for a BCI. In addition, the potential for simultaneous and proportional control of a large number of external devices may be realized through the ability of an array of microelectrodes such as the UIEA to record both spatial and temporal patterns of neuronal activation.

Patients with severe paralysis of limbs, face and vocal apparatus may be intelligent and aware and yet, tragically, unable to communicate. We describe a communication link for such a 'locked-in' patient with amyotrophic lateral sclerosis. We recorded action potentials in her brain over several months by means of an electrode that induces growth of myelinated fibers into its recording tip. She was able to control the neural signals in an on/off fashion. This result is an important step towards providing such patients with direct control of their environment by interfacing with a computer. Additionally, it indicates that restoration of paralyzed muscles may be possible by using the signals to control muscle stimulators.