Artificial Intelligence in Brain-Machine Interface

Transcription

1 Artificial Intelligence in Brain-Machine Interface

2 2

3 Abstract This paper reviews research in the field of Brain-Machine Interfaces (BMIs) and the existing Artificial Intelligence (AI) in Neuroprosthetics to the purpose of examining the future for BMI in particular regard to AI. The conclusive remarks are that this research is expanding a lot at the moment and the potential for this field can result in rapid improvements in the lives of humans in need of prostheses. One of the main factors of this development is the implementation of AI in prostheses, something that has barely even started. 3

4 Contents 1 INTRODUCTION INSPIRATION AIM LAYOUT TYPES OF BMIS NEURON CULTURES NON-INVASIVE BMIS INVASIVE BMIS Experiments Cochlear Implants [13] Visual Aid A PERIPHERAL INVASIVE CLOSED-LOOP BMI [17] Procedure Result AI IN NEUROPROSTHETICS SOFTWARE IMPROVEMENTS THE INTELLIGENT HAND [20] The Intelligent Foot [22] SUMMARY DISCUSSION SUMMARY BIBLIOGRAPHY

5 1 Introduction In recent years a new field of research called Brain-Machine Interface has evolved rapidly. The aim of this research is to develop functional communication between the human nervous system and artificial computers or machines. It is probably considered science fiction by many but the research is growing and showing tangible results. Not-very-modest researchers envisage prostheses for amputees and quadriplegics controlled directly by the brain and cyborgs; artificially enhanced humans with built-in night-vision, artificial super-strong muscles etc. This brings hope to many people with impairments of all kinds of sorts. The most prevalent aid so far is the Cochlear implant, which have helped over a hundred thousand deaf people, giving them partial hearing. In this paper the cochlea implant will be reviewed, as well as a series of other BMI advancements. 1.1 Inspiration The inspiration for this paper was taken from an online article called Brain vs. Machine Control [1]. The article suggests that the field of BMI will spawn a new kind of interactivity between men and machine in the form of a cyborg. The cyborg-construction will inevitably result in direct neural connections between human brains and artificial intelligence (AI). In this case, it involves AI in the form of additional metal arms belonging to a science fiction character. How much science and how much fiction this idea contains remains to be seen. This particular article is of course but one among many sources, more or less fictional, containing this idea. 1.1 Aim The artificial extensions being researched in the field of BMI are called neuroprosthesis and this field of research is therefore also often called Neuroprosthetics. The aim of this paper is to summarize and analyze some important developments in BMI research and the existence of AI in neuroprosthesis to determine the likelihood and manifestation of the development in the above-mentioned article. 1.3 Layout Firstly, different types of BMIs will be dissected and explained. Then some different types of BMIs most important to the aim of this paper will be explained using a few examples. Next, AI in neuroprosthetics will be examined and lastly follows a summary and discussion. 5

6 2 Types of BMIs There are several different types of BMIs and neuroprostheses. The first type of BMI is neuron cultures. Neuron cultures are simply cultures of neurons grown in labs and connected to a computer. The purpose of neuron cultures is to examine biocompatibility, neuronal growth in interaction with artificial materials and artificial feedback and overall neuronal functionality. The second type of BMIs are non-invasive BMIs they utilize EEG recordings performed outside of brains, which has great benefits for people who suffer from spinal cord injuries or similar and cannot employ their peripheral nervous system beyond the brain. The third group of BMIs is invasive BMIs. They build on the knowledge of the neuron cultures, neuroscience and essentially a whole range of research fields to produce neuroprosthesis that can interact directly with the neural structures of humans and other creatures. 2.1 Neuron cultures Recently Dr Steve Potter at Georgia's Institute of Technology, Atlanta, and Guy Ben-Ary at the University of Western Australia, Perth on different sides of the Pacific together connected a culture of rat neurons resident in Atlanta to a robotic arm in Perth. Dr Potter grew 50,000 neurons from a rat in a petri dish and connected the culture to 64 electrodes. These electrodes picked up the electrical activity of the brain, translated them into a binary code in a computer, which sent the code over the Internet to the lab in Perth where it was transmitted to a robotic arm that responded to the input by moving its 3 colored markers across a canvas. The motion of the robotic arm was being registered by the computer and sent back to Atlanta and resulted in stimulation of the neuronal mass in the petri dish through the electrodes, creating a closed-loop entity that would, in theory, be able to respond to the environment. [2] At first, the drawings that the 50,000 neurons generated were chaotic, but progressed into more stable, although meaningless paintings, implying that a responsive neural network was at work, adapting to the outside stimulation it received. However, its communicative skills have not progressed beyond meaningless and is not likely to do so either. This, of course, in part due to the fact that the neurons do not have a goal with their lives; they have nothing meaningful to communicate. This is however a philosophical issue debating how a meaningful life arises and from what so that will be left for others to contemplate. The importance of this arts-project is that it proved that it was possible to create a closed-loop connection between a computer and a biological neural network using electrodes. Another similar experiment involving fish neurons has also been conducted recently. Scientists at Northwestern University, Chicago, US, connected a part of the brain of a fish a lamprey to a robot with motor functions and visual sensors. The part of the brain that was used was the part of the brain that keeps the fish in an upright position while swimming in the water, using the sunlight as guidance for its balance. It was expected that any light source would mimic the electrical patterns representing a sun and the part of the brain adapted to reacting to sunshine would respond to any input by correcting its position according to the stimulation and thus, by feeding the electrical patterns using a robot that sensed light, the brain-part was expected to react to the stimuli, as though it would have reacted to natural sunlight while swimming in the water. [3] 6

7 The result was as expected; the brain learned to interpret the signals from the robot sensors and learned which signals to return to the robot to move it in the direction of the light. This goes to show the no less than incredible ability of a brain to adapt to artificial sensory input and be able to adapt its own sensory output to machines. These abilities are very important for future creation and perfection of neuroprostheses, which will be discussed later in the paper. In another experiment giving support to the other findings, Thomas DeMarse at the University of Florida grew 25,000 neurons from a rat on a grid with 60 two-way electrodes. The neural network was hooked up to a computer program simulating a plane that could tilt to the left and to the right. These neurons, unlike the neurons in Atlanta, did seem to be goal-oriented, trying to keep the plane level and adapting to the different tilting stimulations and reacting to them by sending output to alter the angle of the wings. This again shows that neurons can learn, even when not assembled in an orderly fashion, as is the case in the brain grown according to the DNA of an animal. Important to remember here is that learning is enabled through the flexibility of the neural network to change and restructure itself; restructuring is learning. [4] 2.2 Non-invasive BMIs Other recent findings in BMI include experiments with live (whole) rats and monkeys, the perhaps most notable ones conducted by Miguel Nicolelis, neuroscientist at Duke University, Durham, North Carolina, and his co-workers. At first, they implanted electrodes into the brains of rats to record their neural activity [5]. The electrodes were picking up the electrical activity of networks of neurons, i.e. listening to several neurons processing together, instead of just one at a time and through these experiments important novel discoveries were made. The cortex operates using several parallel neural processes, which combined produce the output from the brain and Nicolelis et al. found that one needed not listen to all the parallel processes of the entire brain to retrieve vital information from it; instead registering electrical activity from only one or a few processes was sufficient to predict the output of the entire processing. This led to the continuation of experiments using implanted electrodes that were connected only to a few neural compositions, without having to search the entire brain for a specific process related to a specific output (or specific action). The implants that are used in these kinds of experiments consist of an array of electrodes that float around in the brain tissue registering electrical impulses without interfering or damaging the brain s neurons and for Nicolelis first tests an implant was inserted into a rat-brain to monitor as little as 46 neurons. Similar experiments [6-8], one involving an owl monkey pressing a lever was advanced to a new level, when a robotic arm was produced and connected to the monkey to reproduce the motion of the simulation in a physical artificial arm [9]. The effect was that the prosthetic limb mimicked the actual arm of the monkey to a high degree. When measuring the activity of 100 neurons the artificial arm was 70% accurate in its movement and when measuring the activity of 500 neurons the result was 95% accuracy. In the search for functional prostheses for disabled humans, the research has come quite far. So far the achievements, as have been presented in this paper, involve measuring brain activity more accurately, with less intrusion and with materials accepted by the brain, and knowledge of the required amount of neurons needed for more accurate prediction of the output of the motor cortex, useful algorithms for interpreting neural activity patterns and adequate physical prostheses, fine enough to replicate the motions researched upon in this experiment. 7

8 Ordinary prostheses works by myoelectrical readings, i.e. registering contractions of a functional muscle and conveying those signals to the operation of a prostheses. But the problem is that the muscle reading does not correspond to the prosthesis movement directly or even to the initially intended action of the muscle from the input, instead, the patient needs to learn to control the prosthesis by altering its way of maneuvering the muscle. Recently Todd Kuiken displayed how to better make of use the old nerve paths that are left in patients who have lost any of their limbs [10]. The nerve path to a hand that has been lost is still able to forward neural information and if it is moved to another muscle then thinking of moving the hand will move that muscle instead. Thus doing myoelectric readings of that muscle for a hand prosthesis will make the patient move the hand by actually thinking of moving the hand, making it all much more natural and a patient with 2 arm prostheses utilizing this technique is called the Bionic Man. 2.3 Invasive BMIs All neuroprostheses existing today involve transmitting sensory input data into the brain, and the most used of these is the cochlear implant, which will be granted an in-depth dissection in this chapter. But work has begun on similar devices meant to help the blind see again, using the same principal of sending sensory information directly into the synoptic (and auditory nerve respectively) bypassing the normal instruments of sight and hearing, eyes and ears. Lastly, some speculation about the possible prostheses that might arise in the future and the progress in closed-loop (motor cortex somatosensory cortex) prostheses will be discussed Experiments The experiment was to see if the rat could interact with an artifact, ultimately controlling it with thought alone. The artifact in question was a lever that, if pressed, would grant the rat a sip of water. A computer registered the neural activity of the 46 neurons when the lever was pressed and then the lever was disconnected and water was distributed based only on the input from the implant displaying the before-used neural pattern, which led to the rat learning that it did not have to move its arm to get water, it simply had to active the same process in these 46 neurons as it automatically did when moving its arm, but this time, without moving its arm, concentrating on only the prerequisite processing for moving the arm and not the whole process leading to the signaling of arm movement. Whether the neurons were disconnected from the rest of the arm moving process to be used only for the specific task of constructing the neural pattern as a way to get water or the neurons were still an integral part of the motion process and could be used in both ways was not revealed by this experiment but leads to intriguing questions about what would be required of the brain of a human controlling an artificial limb with thoughts alone. Quadriplegic humans have also been tested upon using the same techniques [11]. A part of the skull in a patient was removed, revealing an entry point to the motor cortex for insertion of an implant that would be able to register the neural activity of the patient s thoughts, concerning motor functions. An algorithm was again developed to interpret the signals deriving from an area of the brain that the patient had little use for, being almost completely paralyzed. His brain was not damaged, and the motor cortex was intact although fairly unused. However, with a computer interpreting voluntary activity of the motor cortex into cursor movement on a computer screen, the patient learned to control and use the cursor proving the feasibility of electrode mapping of the brain in humans as a means to control prostheses, taking the research one step closer to constructing functional neuroprostheses for people suffering from e.g. damage to the spinal cord. 8

9 The next principal matter in this field is to enable closed-loop feedback from the prostheses and tests aiming for this have been performed. In 2002 Chapin et al. implanted probes into rats. 2 probes were inserted in the somatosensory cortex of the brain where the input normally is received from stimuli of the left and right whisker respectively [12]. 1 probe was implanted in the medial forebrain bundle. The rats were trained to navigate using input through these 3 probes, where simulated touch on the left whisker represented a left turn, and simulated touch on the right whisker represented a right turn, and stimulation of the 3 rd probe indicated moving forward. This was learned through guiding the rats through a maze stimulating the rats using the wireless probes that received input from the scientists observing from afar, thus creating a remote control over the rats. After the rats had learned how to be guided through the maze, they were tested in other environments, successfully. This is proof of the basic feasibility of communicating with the brain through electrical stimulation Cochlear Implants [13] The cochlear implant, also referred to as the Bionic Ear, in 1978 became the first neural prosthesis ever to be implanted in a human. The purpose was to bypass the auditory system transmitting sound waves to the auditory nerve to help people with hearing disorders be able to hear speech again. The hearing disorder that can be helped through the use of a cochlear implant includes all forms of damage to the inner hair cells. Most people with hearing disorders suffer some kind of damage to the outer hair cells that are responsible for amplifying the sound waves making them easier to recognize and this condition is helped with a regular hearing aid that boosts the sounds going into the ear. The inner hair cells react by sending an electrical impulse via the auditory nerve when stimulated by a sound wave. Depending on the amplitude of a sound, it is passed on a certain length of the inner ear through the spiral-shaped cochlea and sound will hit separate hair cells, at different intervals and at a difference of depth into the cochlea making the sound distinguishable and interpretable. Damage to the inner hair cells is therefore more complex to circumvent and requires a device that can distinguish sounds and transfer that information as electrical impulses to the auditory nerve. 9

10 Graeme Clark, the pioneer and creator of cochlear implants, found that to mimic the cochlea it would be necessary to send impulses of different pitches to different parts of the auditory nerve, thus an electrode array with multiple channels was needed. The electrode array is inserted, as the picture shows, surrounding the cochlea, connecting its electrodes to different areas of the auditory nerve. The whole system works as follows: A sound is intercepted by a microphone which sends the audio information to a computational device processing the information in binary code, translating it to send it to the right part of the cochlea, with the right intervals and pitch of the stimuli, simulating normal sound processing to the best of its ability. Although the information is limited in resolution due to the tens of electrodes, compared to the tens of thousands of hair cells, the brain can extract enough useful information from the input to be able to interpret ordinary speech. Over the years Clark and others have improved the techniques surrounding the principle; matters worth recognition are: finding suitable materials to produce the artificial prosthesis in, materials that would not be rejected by the immune system of the body, as well as the discovery that, just like a fully hearing person can determine distance to the source of a sound using both ears, when operating 2 cochlear implants a person can receive enough auditory information to establish the source of the sound, and also progressively discovering better ways, better formulas for interpreting the input of the microphone into efficient binary coding Improving the Cochlear Implant One thing to notice is the adaptability of the brain, which can interpret the new signals almost as well as it could once interpret sound normally, and studies conducted by Clark et al. have shown that children born deaf in many cases develop the same language skills as children with normal hearing. Although the brain can perhaps do a lot better with more electrodes, that is a surgically and technically challenging task to approach and improvements to the implants are instead more likely with the improvement of the algorithms used to interpret the sound waves. A recent study addresses the problem of experiencing music with current cochlear implants. The main factor of this deficiency is said to be the lack of spectral information due to limitations in channels. Compared to a fully functional ear, a cochlear implant only provides one thousand of channel information or less. Sound is therefore reduced to a few amplitude channels, which works well when there is only one clear source of sound in hearing range. However, low-resolution amplitude is not sufficient in noisy space and not for music either. The answer is fine-tuned frequency information to complement amplitude. Using the Hilbert transform frequency, amplitude and time variables can be extracted and separated in the software of the prosthesis. This meant that information could be sent temporally much more accurate; highly resolved, down to milliseconds. [14] The result of the study was that subjects could hear normal speech in quiet surroundings just as good as with preexisting cochlear implants, barely better in noisy surroundings, but music recognition raised from 30% and 40% to >90% in noisy and quiet surroundings respectively [14]. This suggests that the potential for improvements in sound computing software is still great. The limitations of 24-channels implants can be circumvented using cleverer computing, although it is also obvious that finer implants would provide more potential for improved algorithms. 10

11 2.3.4 Visual Aid The Bionic Ear has been a success in aiding people with hearing disorders and currently research is being carried out investigating the possibility of creating a similar device for people with impaired vision [15-16]. Following the same principle, researchers hope to bypass damaged retinal cells, which hold the equivalent function for visual perception as does the inner hair cells of the cochlea hold for auditory perception. Scientists are testing a device connected to the retina via 16 electrodes, stimulation the optic nerve. The device consists of a range of photon receptors able to pick up light, transmitting the visual information to a decoder that determines the amount of stimulation the electrodes should send out. As this is all very fresh, the algorithmic developments in the cochlear implant has not been paralleled and early results are still far from satisfying; but initially it works by principal and software and hardware advances should follow making it possible for blind patients to see, just as it was made possible for deaf patients to hear. However, a photon receptor is fairly simple in its construction, like any solar cell, compared to a device for spectral analysis, which would be required if the patient was to be able to see colors. 2.4 A peripheral invasive closed-loop BMI [17] One of the more prominent advocates for cyborgs is Kevin Warwick. His scientific research along with his works of fiction and science fiction prophecies has resulted in much media attention. Warwick becoming a cyborg is an interesting landmark in BMI research, despite his many critics; possibly even thanks to his many critics, given the amount of media attention it brought to the field. In 2002, Warwick et al. conducted an experiment inserting an electrode array into his own body connecting to the median nerve of his lower left arm Procedure Each of the 100 electrodes in the array was 4 m in diameter at the platinum coated tip. The array was inserted into the median nerve of the arm and the wire bundle and electrical connector pad was fastened externally on the arm. The BMI fed signals to a multi degree of freedom hand prosthesis being controlled by voluntary opening involuntary closing (VOIC). This means that input tells it to open, but it closes on its own. The Figure 1. The microelectrode array hand is programmed to adjust the grip when closing using sensors to detect the force needed to grip objects, prevent them from slipping etc. All this is done by the prosthesis autonomously. The prosthesis sent tactile information back through the electrodes closing the loop. 11

12 The signals from the brain was filtered and amplified and decoded in software by a leaky integrator. One feed gives the signals frequency in 10-bit resolution and another records the temporal activity of the signals. The software is not described in any more detail in the paper Result Trials were conducted where the test subject learned to operate the hand prosthesis and although the feed-back sensory information from the prosthesis to the brain cannot be evaluated, control on the prosthetic hand was clearly discernible, as seen in Figure 2. Figure 2. Success rate for subject trying to control the grasp of the hand prosthetic Clearly, the subject has learned to control the prosthesis to a certain degree and with the help of the hand itself, it is a possible replacement for a natural hand given some time for training and sophisticated programming allowing the hand to correct for human error or failure in the neural communication. To quote the paper: it has been shown that it is possible to decode the neural activity into distinct control commands, such that, in co-operation with the on-board intelligence of a prosthetic hand, the hand flexion can be controlled. 12

13 3 AI in Neuroprosthetics 3.1 Software Improvements Most of the software in neuroprostheses is concerned with interpreting the brain signals into binary and electronic information. Several different methods of interpretation have been tested, like Artificial Neural Networks and fuzzy logic algorithms. In one experiment power spectral density (PSD) values where extracted from mental tasks using autoregressive methods and processed through a fuzzy neural network. The PSD values were used to train the device to produce letters. The information was converted into a tri-state morse code dot, dash and space which could then be translated into letters using morse alphabet; this to improve computational time. This experiment used EEG-signals and was able to conclude that this is a possible way for paralyzed humans to communicate given the low error counts and fast computation times. [18] Prediction algorithms are at the heart of all of the closed loop BMI studies. The two broad classes of prediction algorithms that are used are regression and classification. Regression algorithms attempt to map inputs to a continuous space of output variables Classification algorithms, on the other hand, map inputs onto a set of discrete classes usually with no implied ordering. [19] These two classes are illustrated in Figure 3. 13

14 Figure 3. Regression and classification approaches to BCI control of two-target and fourtarget applications. For the two-target application (targets are up and down triangles), both approaches must determine the parameters of a single function. In contrast, for the four-target application (targets are up and down closed and open triangles), the regression approach still needs only a single function while the classification approach needs three functions, one for each inter-target boundary. [20] Figure 3 illustrates the advantage of a regression approach in a BMI and for each new study there is almost as many new approaches and algorithms. There have not been many closedloop neuroprosthesis studies but here follows a brief review of a recent one. Four rats were surgically implanted with electrodes in the motor cortex. Using a support vector machine (SVM) to decode neural activity, indicative activity was more easily categorized and separated. The rats were trained to press levers for a food reward and received predominantly visual feedback. Once the rats were sufficiently trained the levers were no longer in use and the SVM would instead predict intended lever-pressing based on neural activity preceding the motion. The SVM was able to predict the motion significantly better than a comparative naïve Bayesian algorithm would. [19] 14

15 The success of the experiment led to the following statement: Our success with determining directional control signal has inspired us to look further into the idea of a supervisory closedloop system directing a vehicle. We have begun to expand the system into actual vehicle control and soon hope to allow for full velocity control using an asynchronous system interacting with a highly sophisticated vehicle capable of sensing its surrounding and interpreting the supervisory commands in light of such sensor readings [19]. In effect we would get rat-steered intelligent vehicles, research the likes of DARPA are likely to advance through funding. 3.2 The Intelligent Hand [20] Current hand prostheses usually employ myoelectric signals from arm muscles. They also receive feedback visually alone. Ideally a neuroprosthesis would be connected directly to the nerves and feeding them with electronic feedback for proprioception, touch, warmth etc. The prosthesis in this study has sensors for force, joint angle and slip, which is to a microprocessor in the hand. It is not sent back to the brain, however, but the microprocessor processes that information on its own, which is both the clever and easy solution, as well as the natural order of thinking: the conscious part of the brain usually makes only strategic decisions about the task, and the lower levels of the brain co-ordinate the joints in the fingers and arm to present the hand in the best way to perform the operation. Only difference is that here the lowerlevel brain functions are replaced by the artificial intelligence of the hand prosthesis and the only feed needed is from motor cortex to prosthesis. Considering that the processing power devoted to control the hand, as measured by the amount of motor cortex devoted to it, is as much as the legs and trunk combined [21] a lot of computational power has been rerouted from the brain to the prosthesis. The Oxford hand, as it is also known as, has been tested and evaluated showing good results. The hand takes care of grip strength in response to slip and quality of the object and also controls rotation at the joint while the human user of the prosthesis can focus on only the opening of the hand. This symbiosis is not complete though, as even smarter hands can be envisioned that would be able to handle many more hand functions. Personally I imagine someone juggling The Intelligent Foot [22] The PROPRIO FOOT is another more recent development. In October of 2006 NY Times presented the intelligent prosthesis that closely mimics the action of a human foot [23]. Similar to the intelligent hand, the intelligent foot is fitted with AI capable of controlling some lower-level functions of the prosthesis. The PROPRIO FOOT tracks its movement in space, remembers how the walk is comparing the walk to stored profiles to identify what type of gait is in action and what type of terrain is being trodden. When the foot is in walking mode, the toe lifts when the foot is lifted for more ground clearance, as would a natural foot do and similarly seats the toe faster when the foot comes down to the ground. The foot can adjust to slopes and stairs and even has a mode of operation for standing up from sitting. 15

16 4 Summary The research in the field of BMI is still scattered into many fractions directed at a whole range of approaches of the best way to assist humans in need of sophisticated prostheses. The following discussion aims at comparing some of the inconsistencies of approaches. 4.1 Discussion One matter of discussion is the need for AI in BMIs where the essential parts of the nervous system is intact and the prosthesis is only replacing the sensory neurons of the body. Prostheses simulating neurons would interact with the human brain without any need for additional computational power beyond that of the brain itself. The cochlear implant is a good example for this discussion. This device is currently employing a computational algorithm for extracting the most important input to forward to the brain out of all the auditory information it receives. There are a few restrictive variables, like the fact that the cochlear implant uses only 16 or 22 electrodes while the ear contains billions of nerve endings. Today this means that a cochlear implant is not able to replicate complete auditory function, but with only a few electrodes it is impressively efficient. This means that the possibility for more efficient algorithms in combination with improved technical devices or new technology such as nanotechnology could result in auditory prosthesis that would be superior to human ears, as long as the brain is ready to adapt. The need for AI in motor-controlled prostheses is already upon us. The intelligent foot described earlier is the first of its kind and will most likely be followed by many more similar prostheses. Right now it is impossible to obtain perfect interaction between a brain and a neuroprosthesis and therefore it is essential that the prosthesis can operate autonomously to an extent without input from the brain. This reduces the information flow needed in a closedloop device to the essential, basic commands and responses needed for walking, running etc. A lot of muscle movements are involuntarily following directly on triggering movements, such as lifting the toes while lifting the foot. If the foot automatically raises the toes when the foot it lifted, then the brain only has to signal for the lifting of the foot and the foot takes care of the rest. This reduction simplification is both a smart solution to the limitations of today s neuroprostheses as well as a potential for increasingly advanced foot functions that could go beyond human s foot functions. The research on peripherally connected neuroprostheses in contrast with BMIs connected to the brain itself generates the question of which is better. Two approaches are available. Either the prosthesis is connected to the stump of the amputee, or to the brain. If the device is not connected to the brain, then it can be myoelectrically controlled, or receiving input from cuff electrodes, but preferably it would be connected to the nerve endings at the end of the stump as this would be the most practical place for surgery and wouldn t involve any artificial devices on the body except for where the prosthesis follows. If instead connected to the brain, the prosthesis would still be physically connected to the stump, but the device would utilize a wireless connection to an implant connected directly to the motor cortex. There s a lot of scientific research demonstrating the capabilities of BMI connected to the cortex but a peripheral neuroprosthesis seems the more natural alternative, natural not only to the physiological functionality but also to the already existing brain and nervous system structure. 16

17 One last note in the discussion is that neuroprostheses have the potential of relieving some computational power from the brain to the prosthesis. A lot of brainpower and a lot of the physical brain are devoted to computing e.g. movement, which a sufficiently intelligent device could do autonomously instead. It also offers the possibility of prosthetic system capable of far more than what is naturally given to humans; superhuman strength and senses for example. The philosophical debate on the implications of this is left for others to indulge in. 4.2 Summary This paper concludes that there is a great deal of progress to be expected in the following years concerning BMIs and in particular the advancement of AI in neuroprosthesis. In the field of BMI new results mean better-suited biocompatible materials and more functional as well as user-friendlier hardware. In addition to this, better algorithms for signal decoding and processing is expected accompanied by improvements in closed-loop feeds, where the ultimate goal of totally replaceable neuroprostheses still lies far off. Lastly, developments in AI and compatibility between artificial and human intelligence in brain-machine interfacing will be a challenge for the coming of generations in neuroprosthetics. Although science is closing in, sci-fi is still ahead by a number of years. 17

18 5 Bibliography [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] Sit, Simonson, Oxenham, Faltys, Sarpeshkar. A Low-Power Asynchrounous Interleaved Sampling Algorithm for Cochlear Implants That Encodes Envelope and Phase Information. IEEE Transsactions on Biomedical Engineering, vol 54 no.1, January [15] [16] [17] Warwick, Kyberd, Goodhew, Hutt, Gasson. Invasive neural prosthesis for signal detection and nerve stimulation. International Journal of Adaptive Control and Signal Processing 2005; 19: [18] Palaniappan, Paramesran, Nishida, Saiwaki. A New Brain-Computer Interface Design Using Fuzzy ARTMAP. IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol 10 no.3 September [19] Olson, Si, Hu, He. Closed-Loop Cortical Control of Direction Using Support Vector Machines. IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol 13 no.1 March [20] McFarland, Wolpaw. Sensorimotor Rhythm-Based Brain-Computer Interface (BCI): Feature Selection by Regression Improves Performance. IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol 13 no.3 September [20] Kyberd. The intelligent hand. IEE Review September [21] Kyberd, Pons. A Comparison of the Oxford and Manus Intelligent Hand prostheses. Robotics and Automation vol 3 September [22] _FOOT_Sept2006.pdf [23] Additional resources: Lebedev, Nicolelis. Brain-machine interfaces: past, present and future. TRENDS in Neurosciences vol 29 no Spelman FA. The past, present, and future of cochlear prostheses. IEEE Engineering in Medicine and Biology Magazine 1999; 18: