Experiments into biology-technology interaction

In this paper a look is taken at how the use of implant and electrode technology can be employed to create biological brains for robots, to enable human enhancement and to diminish the effects of certain neural illnesses. In all cases the end result is to increase the range of abilities of the recipients. An indication is given of a number of areas in which such technology has already had a profound effect, a key element being the need for a clear interface linking a biological brain directly with computer technology. The emphasis is placed on practical scientific studies that have been and are being undertaken and reported on. The area of focus is the use of electrode technology, where either a connection is made directly with the cerebral cortex and/or nervous system or where implants into the human body are involved. The paper also considers robots that have biological brains in which human neurons can be employed as the sole thinking machine for a real world robot body.

The future of human-machine interaction: implant technology

In this paper a look is taken at how the use of implant and electrode technology can be employed to create biological brains for robots, to enable human enhancement and to diminish the effects of certain neural illnesses. In all cases the end result is to increase the range of abilities of the recipients. An indication is given of a number of areas in which such technology has already had a profound effect, a key element being the need for a clear interface linking a biological brain directly with computer technology. The emphasis is placed on practical scientific studies that have been and are being undertaken and reported on. The area of focus is the use of electrode technology, where either a connection is made directly with the cerebral cortex and/or nervous system or where implants into the human body are involved. The paper also considers robots that have biological brains in which human neurons can be employed as the sole thinking machine for a real world robot body.

Future issues with robots and cyborgs

In this article, four different practical experiments in robotics and human/machine merger are firstly described and then considered with regard to their ethical implications. Results from the experiments are discussed in terms of their meaning and application possibilities. The article is written from the perspective of scientific experimentation, opening up realistic possibilities to be faced in the future rather than giving conclusive comments on the technologies employed. Human implantation and the merger of biology and technology are key elements.

A novel human-machine interface using subdermal magnetic implants

This paper explores a novel tactile human-machine interface based on the controlled stimulation of mechanoreceptors by a subdermal magnetic implant manipulated through an external electromagnet. The selection of a suitable implant magnet and implant site is discussed and an external interface for manipulating the implant is described. The paper also reports on the basic properties of such an interface, including magnetic field strength sensitivity and frequency sensitivity obtained through experimentation on two participants. Finally, the paper presents two practical application scenarios for the interface.

Features for detection of Parkinson's disease tremor from local field potentials of the subthalamic nucleus

Deep Brain Stimulation (DBS) is a treatment routinely used to alleviate the symptoms of Parkinson's disease (PD). In this type of treatment, electrical pulses are applied through electrodes implanted into the basal ganglia of the patient. As the symptoms are not permanent in most patients, it is desirable to develop an on-demand stimulator, applying pulses only when onset of the symptoms is detected. This study evaluates a feature set created for the detection of tremor - a cardinal symptom of PD. The designed feature set was based on standard signal features and researched properties of the electrical signals recorded from subthalamic nucleus (STN) within the basal ganglia, which together included temporal, spectral, statistical, autocorrelation and fractal properties. The most characterized tremor related features were selected using statistical testing and backward algorithms then used for classification on unseen patient signals. The spectral features were among the most efficient at detecting tremor, notably spectral bands 3.5-5.5 Hz and 0-1 Hz proved to be highly significant. The classification results for determination of tremor achieved 94% sensitivity with specificity equaling one.

Cloud based global positioning system as a safety monitor for dementia patients

The aim of using GPS for Alzheimer's Patients is to give carers and families of those affected by Alzheimer's Disease, as well as all the other dementia related conditions, a service that can, via SMS text message, notify them should their loved one leave their home. Through a custom website, it enables the carer to remotely manage a contour boundary that is specifically assigned to the patient as well as the telephone numbers of the carers. The technique makes liberal use of such as Google Maps.

Hybrid Brains - Biology, Technology Merger

In this paper an attempt has been made to take a look at how the use of implant and electrode technology can now be employed to create biological brains for robots, to enable human enhancement and to diminish the effects of certain neural illnesses. In all cases the end result is to increase the range of abilities of the recipients. An indication is given of a number of areas in which such technology has already had a profound effect, a key element being the need for a clear interface linking the human brain directly with a computer. An overview of some of the latest developments in the field of Brain to Computer Interfacing is also given in order to assess advantages and disadvantages. The emphasis is clearly placed on practical studies that have been and are being undertaken and reported on, as opposed to those speculated, simulated or proposed as future projects. Related areas are discussed briefly only in the context of their contribution to the studies being undertaken. The area of focus is notably the use of invasive implant technology, where a connection is made directly with the cerebral cortex and/or nervous system. Tests and experimentation which do not involve human subjects are invariably carried out a priori to indicate the eventual possibilities before human subjects are themselves involved. Some of the more pertinent animal studies from this area are discussed including our own involving neural growth. The paper goes on to describe human experimentation, in which neural implants have linked the human nervous system bi-directionally with technology and the internet. A view is taken as to the prospects for the future for this implantable computing in terms of both therapy and enhancement.

Consumer robotic products: studying the factors that influence market adoption

The aim of this article is to identify the key factors that are associated with the adoption of a commercial robot in the home. This article is based on the development of the robot product Cybot by the University of Reading in conjunction with a publisher (Eaglemoss International Ltd.). The robots were distributed through a new part-work magazine series (Ultimate Real Robots) that had long-term customer usage and retention. A part-work is a serial publication that is issued periodically (e.g., every two weeks), usually in magazine format, and builds into a complete collection. This magazine focused on robotics and was accompanied by cover-mounted component parts that could be assembled, with instructions, by the user to build a working robot over the series. In total, the product contributed over half a million operational domestic robots to the world market, selling over 20 million robot part-work magazines across 18 countries, thereby providing a unique breadth of insight. Gaining a better understanding of the overall attitudes that customers of this product had toward robots in the home, their perception of what such devices could deliver and how they would wish to interact with them should provide results applicable to the domestic appliance, assistance/care, entertainment, and educational markets.

Upgrading humans via implants - why not?

In his forum paper, Prof. Kevin Warwick considers four different examples of how the use of implant technology is opening up the possibility of upgrading human abilities, particularly in terms of mental cognition. The main thrust is an overview of Prof. Warwick's own research, which led to him receiving a neural implant linking his nervous system bi-directionally with the internet. With this implant in place, neural signals were transmitted to various technological devices to directly control them, in some cases via the internet, and feedback to the brain was obtained from such stimuli as the fingertips of a robot hand, ultrasonic (extra-) sensory input and neural signals directly from another human's nervous system. A view is taken as to the prospects for the future, both in the short-term as a therapeutic device and in the long-term as a form of enhancement, including the realistic potential, in the near future, for thought communication – thereby opening up tremendous commercial potential. The therapy/enhancement dichotomy is considered here, as well as military and medical issues. Clearly though, an individual whose brain is part human/part machine can have abilities that far surpass those who remain with a human brain alone. Will such an individual exhibit different moral and ethical values to those of a human? If so, what effects might this have on society?

Spatio-temporal dependencies in functional connectivity in rodent cortical cultures

Models of functional connectivity in cortical cultures on multi-electrodes arrays may aid in understanding how cognitive pathways form and improve techniques that aim to interface with neuronal systems. To enable research on such models, this study uses both data- and model-driven approaches to determine what dependencies are present in and between functional connectivity networks derived from bursts of extracellularly recorded activity. Properties of excitation in bursts were analysed using correlative techniques to assess the degree of linear dependence and then two parallel techniques were used to assess functional connectivity. Three models presenting increasing levels of spatio-temporal dependency were used to capture the dynamics of individual functional connections and their consistencies were verified using surrogate data. By comparing network-wide properties between model generated networks and functional networks from data, complex interdependencies were revealed. This indicates the persistent co-activation of neuronal pathways in spontaneous bursts, as can be found in whole brain structures.

Parkinson’s Disease tremor classification – a comparison between Support Vector Machines and neural networks

Deep Brain Stimulation has been used in the study of and for treating Parkinson’s Disease (PD) tremor symptoms since the 1980s. In the research reported here we have carried out a comparative analysis to classify tremor onset based on intraoperative microelectrode recordings of a PD patient’s brain Local Field Potential (LFP) signals. In particular, we compared the performance of a Support Vector Machine (SVM) with two well known artificial neural network classifiers, namely a Multiple Layer Perceptron (MLP) and a Radial Basis Function Network (RBN). The results show that in this study, using specifically PD data, the SVM provided an overall better classification rate achieving an accuracy of 81% recognition.

A model-based PID controller for Hammerstein systems using B-spline neural networks

In this paper, a new model-based proportional–integral–derivative (PID) tuning and controller approach is introduced for Hammerstein systems that are identified on the basis of the observational input/output data. The nonlinear static function in the Hammerstein system is modelled using a B-spline neural network. The control signal is composed of a PID controller, together with a correction term. Both the parameters in the PID controller and the correction term are optimized on the basis of minimizing the multistep ahead prediction errors. In order to update the control signal, the multistep ahead predictions of the Hammerstein system based on B-spline neural networks and the associated Jacobian matrix are calculated using the de Boor algorithms, including both the functional and derivative recursions. Numerical examples are utilized to demonstrate the efficacy of the proposed approaches.

B-spline neural networks based PID controller for Hammerstein systems

A new PID tuning and controller approach is introduced for Hammerstein systems based on input/output data. A B-spline neural network is used to model the nonlinear static function in the Hammerstein system. The control signal is composed of a PID controller together with a correction term. In order to update the control signal, the multistep ahead predictions of the Hammerstein system based on the B-spline neural networks and the associated Jacobians matrix are calculated using the De Boor algorithms including both the functional and derivative recursions. A numerical example is utilized to demonstrate the efficacy of the proposed approaches.