Alternative communication systems for people with severe motor disabilities: a survey <s> Electroencephalography <s> Abstract This paper describes the development and testing of a system whereby one can communicate through a computer by using the P300 component of the event-related brain potential (ERP). Such a system may be used as a communication aid by individuals who cannot use any motor system for communication (e.g., ‘locked-in’ patients). The 26 letters of the alphabet, together with several other symbols and commands, are displayed on a computer screen which serves as the keyboard or prosthetic device. The subject focuses attention successively on the characters he wishes to communicate. The computer detects the chosen character on-line and in real time. This detection is achieved by repeatedly flashing rows and columns of the matrix. When the elements containing the chosen character are flashed, a P300 is elicited, and it is this P300 that is detected by the computer. We report an analysis of the operating characteristics of the system when used with normal volunteers, who took part in 2 experimental sessions. In the first session (the pilot study/training session) subjects attempted to spell a word and convey it to a voice synthesizer for production. In the second session (the analysis of the operating characteristics of the system) subjects were required simply to attend to individual letters of a word for a specific number of trials while data were recorded for off-line analysis. The analyses suggest that this communication channel can be operated accurately at the rate of 0.20 bits/sec. In other words, under the conditions we used, subjects can communicate 12.0 bits, or 2.3 characters, per min. <s> BIB001 </s> Alternative communication systems for people with severe motor disabilities: a survey <s> Electroencephalography <s> Brain-computer interfaces (BCIs) can provide communication and control to people who are totally paralyzed. BCIs can use noninvasive or invasive methods for recording the brain signals that convey the user's commands. Whereas noninvasive BCIs are already in use for simple applications, it has been widely assumed that only invasive BCIs, which use electrodes implanted in the brain, can provide multidimensional movement control of a robotic arm or a neuroprosthesis. We now show that a noninvasive BCI that uses scalp-recorded electroencephalographic activity and an adaptive algorithm can provide humans, including people with spinal cord injuries, with multidimensional point-to-point movement control that falls within the range of that reported with invasive methods in monkeys. In movement time, precision, and accuracy, the results are comparable to those with invasive BCIs. The adaptive algorithm used in this noninvasive BCI identifies and focuses on the electroencephalographic features that the person is best able to control and encourages further improvement in that control. The results suggest that people with severe motor disabilities could use brain signals to operate a robotic arm or a neuroprosthesis without needing to have electrodes implanted in their brains. <s> BIB002 </s> Alternative communication systems for people with severe motor disabilities: a survey <s> Electroencephalography <s> We show here that a brain-computer interface (BCI) using electrocorticographic activity (ECoG) and imagined or overt motor tasks enable humans to control a computer cursor in two dimensions. Over a brief training period of 12-36 min, each of five human subjects acquired substantial control of particular ECoG features recorded from several locations over the same hemisphere, and achieved average success rates of 53-73% in a two-dimensional four-target center-out task in which chance accuracy was 25%. Our results support the expectation that ECoG-based BCIs can combine high performance with technical and clinical practicality, and also indicate promising directions for further research. <s> BIB003
For people with severe motor disability such as locked-in syndrome, it often becomes impossible to communicate or control a muscular activity. However, these people generally keep cerebral and sensory functions intact. A solution planned to overcome this handicap is to use electroencephalography associated with the cerebral activity to control an interface. This type of interface using the cerebral waves is usually called brain computer interface (BCI). The approach used to build a BCI consists in measuring the cerebral activity through the EEG signal in order to determine the wishes of each subject. BCI applications can also be built with invasive technologies, e.g., electrocorticogram (ECoG), which involve implantation of electrodes in the cortex and provides better signal to noise-ratio BIB003 . Although invasive technologies are suppose to deliver higher-dimensional control, studies conducted by Wolpaw and McFarland BIB002 have shown that non-invasive EEEG-based BCI can give multidimensional movement control comparable to the control achieved by invasive BCIs. As invasive methods face technical difficulties and involve clinical risks, they should only be used in rare circumstances, when they are necessary to avoid artefacts (e.g. uncontrollable head control) or in cases where the invasive solution shows clearly superior performance than non-invasive methods. For those reasons, we focus on non-invasive BCI applications that use EEG. Of course, it is not possible to analyze complex thoughts but to detect for example variations of rhythms associated with sensorimotor activities. Thanks to the analysis of this type of EEG signals associated with an imagined motor activity (sensorimotor rhythms), it is possible to build interfaces in which the displacement of a cursor present at the screen (according to one or 2 dimensions) is controlled in a continuous way by cerebral waves  . Other EEG signals associated with visual or auditory stimulations allow the construction of brain computer interfaces; these signals are called the evoked potentials. The P300 waves are cognitive evoked potentials often used for BCI. For interfaces based on the treatment of P300, it is not allowed to provide a continuous control but to choose one item among several BIB001 . Contrary to the first case where the subject has to modulate EEG rhythms in a spontaneous way, in this last type of interface the evoked potentials are created by stimulations of the interface on the user, this strong dependence between stimulations and interface explains the term synchronised interfaces used. It is thus possible starting from the two examples of EEG signals presented to distinguish two types of BCI: • synchronous interfaces based on evoked potentials; • asynchronous interfaces based on EEG signals obtained in a spontaneous way by the subject, the sensorimotor rhythms being the most usually kind of EEG signals used in this type of interface. Each one of these two approaches present performances, advantages and disadvantages which will be presented in the following paragraphs.