Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology
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This paper investigates the applicability of generalized dynamic neural networks for the design of a two-valued anesthetic depth indicator during isoflurane/nitrous oxide anesthesia. The indicator construction is based on the processing of middle latency auditory evoked responses (MLAER) in combination with the observation of the patient's movement reaction to skin incision. The framework of generalized dynamic neural networks does not require any data preprocessing, visual data inspection or subjective feature extraction. ⋯ Using only evoked potentials before the pain stimulus, the patient's reaction could be predicted with a probability of 81.5%. The MLAER is closely associated to the patient's reaction to skin incision following noxious stimulation during 1 minimum alveolar anesthetic concentration isoflurane/nitrous oxide anesthesia. In combination with other parameters, MLAER could contribute to an objective and trustworthy movement prediction to noxious stimulation.
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For many years people have speculated that electroencephalographic activity or other electrophysiological measures of brain function might provide a new non-muscular channel for sending messages and commands to the external world - a brain-computer interface (BCI). Over the past 15 years, productive BCI research programs have arisen. Encouraged by new understanding of brain function, by the advent of powerful low-cost computer equipment, and by growing recognition of the needs and potentials of people with disabilities, these programs concentrate on developing new augmentative communication and control technology for those with severe neuromuscular disorders, such as amyotrophic lateral sclerosis, brainstem stroke, and spinal cord injury. ⋯ Future progress will depend on: recognition that BCI research and development is an interdisciplinary problem, involving neurobiology, psychology, engineering, mathematics, and computer science; identification of those signals, whether evoked potentials, spontaneous rhythms, or neuronal firing rates, that users are best able to control independent of activity in conventional motor output pathways; development of training methods for helping users to gain and maintain that control; delineation of the best algorithms for translating these signals into device commands; attention to the identification and elimination of artifacts such as electromyographic and electro-oculographic activity; adoption of precise and objective procedures for evaluating BCI performance; recognition of the need for long-term as well as short-term assessment of BCI performance; identification of appropriate BCI applications and appropriate matching of applications and users; and attention to factors that affect user acceptance of augmentative technology, including ease of use, cosmesis, and provision of those communication and control capacities that are most important to the user. Development of BCI technology will also benefit from greater emphasis on peer-reviewed research publications and avoidance of the hyperbolic and often misleading media attention that tends to generate unrealistic expectations in the public and skepticism in other researchers. With adequate recognition and effective engagement of all these issues, BCI systems could eventually provide an important new communication and control option for those with motor disabilities and might also give those without disabilities a supplementary control channel or a control channel useful in special circumstances.