An attempt to extend human sensory capabilities by means of implant technology

In this paper an attempt is described to increase the range of human sensory capabilities by means of implant technology. The key aim is to create an additional sense by feeding signals directly to the human brain, via the nervous system rather than via a presently operable human sense. Neural implant technology was used to directly interface a human nervous system with a computer in a one off trial. The output from active ultrasonic sensors was then employed to directly stimulate the human nervous system. An experimental laboratory set up was used as a test bed to assess the usefulness of this sensory addition.

Invasive neural prosthesis for neural signal detection and nerve stimulation

This paper specifically examines the implantation of a microelectrode array into the median nerve of the left arm of a healthy male volunteer. The objective was to establish a bi-directional link between the human nervous system and a computer, via a unique interface module. This is the first time that such a device has been used with a healthy human. The aim of the study was to assess the efficacy, compatibility, and long term operability of the neural implant in allowing the subject to perceive feedback stimulation and for neural activity to be detected and processed such that the subject could interact with remote technologies. A case study demonstrating real-time control of an instrumented prosthetic hand by means of the bi-directional link is given. The implantation did not result in infection, and scanning electron microscope images of the implant post extraction have not indicated significant rejection of the implant by the body. No perceivable loss of hand sensation or motion control was experienced by the subject while the implant was in place, and further testing of the subject following the removal of the implant has not indicated any measurable long term defects. The implant was extracted after 96 days. Copyright © 2004 John Wiley & Sons, Ltd.

Automatic artefact removal from event-related potentials via clustering

This paper outlines a method for automatic artefact removal from multichannel recordings of event-related potentials (ERPs). The proposed method is based on, firstly, separation of the ERP recordings into independent components using the method of temporal decorrelation source separation (TDSEP). Secondly, the novel lagged auto-mutual information clustering (LAMIC) algorithm is used to cluster the estimated components, together with ocular reference signals, into clusters corresponding to cerebral and non-cerebral activity. Thirdly, the components in the cluster which contains the ocular reference signals are discarded. The remaining components are then recombined to reconstruct the clean ERPs.

Practical interface experiments with implant technology

In this paper results are shown to indicate the efficacy of a direct connection between the human nervous system and a computer network. Experimental results obtained thus far from a study lasting for over 3 months are presented, with particular emphasis placed on the direct interaction between the human nervous system and a piece of wearable technology. An overview of the present state of neural implants is given, as well as a range of application areas considered thus far. A view is also taken as to what may be possible with implant technology as a general purpose human-computer interface for the future.

The application of implant technology for cybernetic systems

Objective: To assess the usefulness, compatibility, and long-term operability of a microelectrode array into the median nerve of the left arm of a healthy volunteer, including perception of feedback stimulation and operation of an instrumented prosthetic hand. Setting: The study was carried out from March 14 through June 18, 2002, in England and the United States. Results: The blindfolded subject received feedback information, obtained from force and slip sensors on the prosthetic hand, and subsequently used the implanted device to control the hand by applying an appropriate force to g rip an unseen object. Operability was also demonstrated remotely via the Internet, with the subject in New York, NY, and the prosthetic hand in Reading, England. Finally, the subject was able to control an electric wheelchair, via decoded signals from the implant device, to select the direction of travel by opening and closing his hand. The implantation did not result in infection or any perceivable loss of hand sensation or motion control. The implant was finally extracted because of mechanical fatigue of the percutaneous connection. Further testing after extraction has not indicated any measurable long-term defects in the subject. Conclusions: This implant may allow recipients to have abilities they would otherwise not possess. The response to stimulation improved considerably during the trial, suggesting that the subject learned to process the incoming information more effectively.

Issues impairing the success of neural implant technology

By monitoring signals from the central nervous system, humans can be provided with a novel extra channel of communication that can, for example, be used for the voluntary control of peripheral devices. Meanwhile, stimulation of neural tissue can bring about sensation such as touch, can facilitate feedback from external, potentially remote devices and even opens up the possibility of new sensory input for the individual to experience. The concept of successfully harnessing and stimulating nervous system activity is though something that can only be achieved through an appropriate interface. However, interfacing the nervous system by means of implant technology carries with it many problems and dangers. Further, results achieved may not be as expected or as they at first appear. This paper describes a comparative study investigating different implant types and procedures. It is aimed at highlighting potential problem areas and is intended to provide a useful reference explaining important tolerances and limits.

Modified level set method with Canny operator for image noise removal

The level set method is commonly used to address image noise removal. Existing studies concentrate mainly on determining the speed function of the evolution equation. Based on the idea of a Canny operator, this letter introduces a new method of controlling the level set evolution, in which the edge strength is taken into account in choosing curvature flows for the speed function and the normal to edge direction is used to orient the diffusion of the moving interface. The addition of an energy term to penalize the irregularity allows for better preservation of local edge information. In contrast with previous Canny-based level set methods that usually adopt a two-stage framework, the proposed algorithm can execute all the above operations in one process during noise removal.

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.

Influence of earthworm Eisenia fetida on removal efficiency of N and P in vertical flow constructed wetland.

This study investigates biomass, density, photosynthetic activity, and accumulation of nitrogen (N) and phosphorus (P) in three wetland plants (Canna indica, Typha augustifolia, and Phragmites austrail) in response to the introduction of the earthworm Eisenia fetida into a constructed wetland. The removal efficiency of N and P in constructed wetlands were also investigated. Results showed that the photosynthetic rate (P n), transpiration rate (T r), and stomatal conductance (S cond) of C. indica and P. austrail were (p < 0.05) significantly higher when earthworms were present. The addition of E. fetida increased the N uptake value by above-ground of C. indica, T. augustifolia, and P. australis by 185, 216, and 108 %, respectively; and its P uptake value increased by 300, 355, and 211 %, respectively. Earthworms could enhance photosynthetic activity, density, and biomass of wetland plants in constructed wetland, resulting in the higher N and P uptake. The addition of E. fetida into constructed wetland increased the removal efficiency of TN and TP by 10 and 7 %, respectively. The addition of earthworms into vertical flow constructed wetland increased the removal efficiency of TN and TP, which was related to higher photosynthetic activity and N and P uptake. The addition of earthworms into vertical flow constructed wetland and plant harvests could be the significantly sustainable N and P removal strategy

Automated artifact removal from the electroencephalogram; a comparative study

Contamination of the electroencephalogram (EEG) by artifacts greatly reduces the quality of the recorded signals. There is a need for automated artifact removal methods. However, such methods are rarely evaluated against one another via rigorous criteria, with results often presented based upon visual inspection alone. This work presents a comparative study of automatic methods for removing blink, electrocardiographic, and electromyographic artifacts from the EEG. Three methods are considered; wavelet, blind source separation (BSS), and multivariate singular spectrum analysis (MSSA)-based correction. These are applied to data sets containing mixtures of artifacts. Metrics are devised to measure the performance of each method. The BSS method is seen to be the best approach for artifacts of high signal to noise ratio (SNR). By contrast, MSSA performs well at low SNRs but at the expense of a large number of false positive corrections.