of bioincompatibility and tissue scars will outweigh the benefit for most patients. Further, these approaches are not suitable for healthy subjects at all which renders them less interest-ing for non-therapeutical applications like gaminterest-ing or assistive devices for healthy subjects.
Input-Invasive BMI
Input-invasive BMIs transform external stimuli into bioelectric activity that is being pro-cessed by the brain. A very popular and well established input-invasive BMI is the cochlear implant. This device records external sound sources by small microphones similar to a hear-ing aid. The implant though is directly connected to the auditory nerve fibers and translates the recorded sound into bioelectric activity which in turn serves as input for the auditory nerve. The implant is not able to reconstruct the natural hearing since the number of elec-trodes used to connect to the nerve fibers does not even exceed 0.001% of the normal nerve connectivity. Yet, even with a low number of electrodes, a rudimentary hearing reconstruc-tion is possible.
Similar to methods that reconstruct basic hearing senses, visual prostheses are able to re-construct lost vision or amplify existing vision. These neural prostheses rely on externally worn cameras that transmit pictures to a translation device which transforms them into bio-electric signals suitable for neuronal processing. Several different types of visual prostheses exist, one of the most interesting from a technical point of view are visual cortical implants (e.g. [Coulombe et al., 2007]). This type is implanted directly above the visual cortex and com-municates via a wireless connection with the image acquisition hardware. The implanted part of the system consists of a central module which drives a multitude of micro-stimulators to properly induce sensing of bright spots in the subject’s field of view. One of the first to report a successful implantation of such an device was Dobelle in 1974. The patient was able to recognize simple patterns using this device.
A slightly different application emerged in the context of pain relief. In 1965, Melzack and colleagues proposed a new theory calledGate control theory[Melzack and Wall, 1965]. It was hypothized that nerves carrying electrically coded tactile and vibrational sensations termi-nate in the pain fibers which are located at the dorsal horn of the spinal chord. Thereby, two types of cells are involved in the processing. Thetransmission (T)andinhibitorycells. Signals from the pain and sensory fibers excite the T cells until they reach a critical level at which time pain sets in. Inhibitory cells on the other hand counteract to the T cells by dampening the T cell excitation, similar to closing the gate to pain (as formulated by Melzack and Wall). The theory concludes that sensory fiber activation excited the inhibitory cells while at the same time, pain fiber activation impedes inhibitory cells and thus leaves the gates to pain open.
Therefore, if sensory fiber activation is high compared to pain fiber activation, less pain will be sensed. Due to this finding a spinal chord stimulator (SCS) was built by [Shealy et al., 1967]
in order to aid in chronic pain therapy. An SCS uses electrodes implanted on top of the spinal chord and a pulse generator which drives the excitation signal. The sensory fiber excitation can be controlled on demand by the patient and activates the inhibitory cells as described
3.1 Invasive Methods
above. Recent research in this field has extended the applicability of this type if device also to the treatment of Parkinson’s disease [Fuentes et al., 2009].
Output-Invasive BMI
Compared to the former input invasive methods, the flow of information is reversed for output-invasive bramachine interfaces. Neuronal signals originating from the brain are being in-terpreted by a device that translates them into actions that would normally require muscle activity. One of the most widely used brain signals are neuroelectric potentials arising from motor cortical activity. These signals can be translated into commands to control either com-puter cursors or even limb prostheses. This field was pioneered by Chapin who succeeded to train rats to control a robotic arm along a 1D trajectory using only their brain signals. This was accomplished by implanting microwire electrodes into the rats primary motorcortex and the ventrolateral thalamus. The information contained in spike signals of the neuronal ensem-bles were mathematically transformed into neuronal population functions that could accu-rately predict the lever trajectory. Similar work was conducted by [Wessberg et al., 2000] who trained monkeys to precisely control a lever along a 1D and 3D trajectory. In this study, si-multaneously recorded cortical ensemble data of the monkey was used to train a linear model and an artificial neural network (ANN). The recording was realized with implanted microwire electrodes over multiple cortical sites but primarily over the motorcortex. After the implanta-tion of the electrodes, the monkeys were trained for 12 and 24 month to move a lever either to the left or to the right. Subsequently, a more complex task requiring three-dimensional tra-jectories was trained. This task involved controlling a robot arm to reach small pieces of food which were placed at one out of four random positions on a tray. Neuronal data was recorded simultaneously to the actual movement and served as input to either a linear model or an ANN. The hand trajectories predicted by the two methods revealed highly significant correla-tions to the real trajectories with average correlation coefficients of 0.71 for the linear model and 0.66 for the ANN. Even though the starting position and movement velocities were not constant for the individual trials, the predictions remained highly correlated to the real tra-jectories. This experiment showed that it is possible to decoded motorneuron data and trans-late it into a control signal that can be used to steer robotic devices. An extended and more complex variant of this experiment was conducted by [Carmena et al., 2003]. Two macaque monkeys were trained to perform a 3D movement and grasping task. The goal of the first task was to move a computer cursor to a given position in a 3D virtual environment. The second task consisted in not only moving to a specific location but also grasping a virtual disc located at a random position in the virtual environment. During the first phase of the experiment the monkeys performed motor actions using a pole which controlled the location of a computer cursor in a virtual 3D environment. The cursor served as visual feedback for their actions.
Grasping was performed by measuring grip force of the pole. During the training phase the position of the cursor was controlled by the position of the pole. After a few training trials the control signal for the cursor was switched to the brain signals of the monkey. While in this
brain-controlled mode, the monkeys started to perform the same motor actions as learned in the training session. After a few trials however they stopped performing an actual move-ment in some trials and controlled the cursor merely by thought. In response to this outcome, the researchers removed the pole object completely and continued the experiment. The elec-tromyographic recording of muscle activity (EMG) acquired at the wrist flexors/extensors and biceps did not reveal any sort of muscle activity, yet both monkeys were able to control the cursor by thought only without performing any motor actions.
Closed-Loop BMI
In the context of closed-loop control with BMI, Kevin Warwick has drawn much attention in the media. In 2002, Warwicket al. implanted a micro electrode array into his left arm which was connected to the median nerve [Warwick et al., 2005]. The array consisted of 100 electrodes with a 4µm diameter at the tip. Wire bundle and connector were attached ex-ternally on the surface of the arm. The signals transmitted through the wires were used to control a hand prosthesis by means of voluntary opening and involuntary closing (VOIC).
This means that only opening of the hand could be controlled by Warwick while closing was accomplished in an autonomous way. Pressure sensors in the prosthesis measured the grip force to prevent slipping and fed back tactile sensory information to the nerves and hence closing the loop. Control of the hand required a bit of training until Warwick was able to use it as intended. Warwick himself stated that coupled with theon-board intelligenceof the prosthetic hand, it is possible to decode the neural activity into distinct control commands.
The coupling of the on-board intelligence and brain controlled commands is a topic that has reemerged recently under the nameshared control(i.e. [Millán et al., 2009]).