OVERVIEW
This chapter presents a non-exhaustive overview of neuroprosthetics that refers to devices or equipment that are connected to the Central Nervous System (CNS) or the Peripheral Nervous System (PNS). The use of neuroprosthetics has really developed into the 60s. However, recent technological developments have enabled the design of artificial systems that open up new prospects not only for neurorehabilitation, but also a new era of computing. Neuroprosthetics are also called neural prosthetics, Brain-Computer Interface (BCI), or Brain-Machine Interface (BMI). They can be classified into two main categories: input BCI or afferent neuroprosthetics that writes information into the brain, usually from an afferent or sensory input, and output BCI or efferent neuroprosthetics that can read information out of the brain. These two categories can be further broken down into several sub-categories that usually define the site of stimulation for input BCI and the type of response/command in the case of output BCI.
AFFERENT NEUROPROSTHETICS OR INPUT BCI
Writing information into of the brain
Sensory neuroprosthetics
Auditory prostheses/implants
Cochlear implants (CIs)
The most advanced neuroprosthetics are related to the auditory system and cochlear implants. The external part of a CI consists of 2 parts:
• A microphone that picks up surrounding sounds and a transmitter that carries the magnet that allows the transmission with the receiver.
• A housing including a speech processor that processes the sound information transmitted by the microphone.
The internal part consists of:
• A receiver placed beneath the skin and that comprises a receiving antenna and an array of 8 to 22 electrodes or channels, depending on the model. The electrod...
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...s not requested to perform a specific mental control. Rather, the user is performing a normal activity such as working on a decision making software and the BCI records and detects any features that could be useful such as stress, or error detection. In sum, a passive BCI records and processes the mental state activity of a user in a daily activity and detects any signal of fatigue, arousal, working memory load, decision making, or any emotional state. Such as BCI allow a better user performances while improving a human-computer interface. For instance, George et al. used a passive BCI to help with the guidance of a haptic device based on the user mental workload (George, 2012). Some neuromarketing companies such as Neurofocus provides companies with their consumers’ physiological responses via a BCI while purchasing or being exposed to new products (Pradeep, 2010).