A practical EMG-based human-computer interface for users with motor disabilities

In line with the mission of the Assistive Technology Act of 1998 (ATA), this study proposes an integrated assistive real-time system which affirms that technology is a valuable tool that can be used to improve the lives of people with disabilities . An assistive technology device is defined by the ATA as any item, piece of equipment, or product system, whether acquired commercially, modified, or customized, that is used to increase, maintain, or improve the functional capabilities of individuals with disabilities . The purpose of this study is to design and develop an alternate input device that can be used even by individuals with severe motor disabilities . This real-time system design utilizes electromyographic (EMG) biosignals from cranial muscles and electroencephalographic (EEG) biosignals from the cerebrum\u27s occipital lobe, which are transformed into controls for two-dimensional (2-D) cursor movement, the left-click (Enter) command, and an ON/OFF switch for the cursor-control functions . This HCI system classifies biosignals into mouse functions by applying amplitude thresholds and performing power spectral density (PSD) estimations on discrete windows of data. Spectral power summations are aggregated over several frequency bands between 8 and 500 Hz and then compared to produce the correct classification . The result is an affordable DSP-based system that, when combined with an on-screen keyboard, enables the user to fully operate a computer without using any extremities

Defining brain–machine interface applications by matching interface performance with device requirements

Interaction with machines is mediated by human-machine interfaces (HMIs). Brain-machine interfaces (BMIs) are a particular class of HMIs and have so far been studied as a communication means for people who have little or no voluntary control of muscle activity. In this context, low-performing interfaces can be considered as prosthetic applications. On the other hand, for able-bodied users, a BMI would only be practical if conceived as an augmenting interface. In this paper, a method is introduced for pointing out effective combinations of interfaces and devices for creating real-world applications. First, devices for domotics, rehabilitation and assistive robotics, and their requirements, in terms of throughput and latency, are described. Second, HMIs are classified and their performance described, still in terms of throughput and latency. Then device requirements are matched with performance of available interfaces. Simple rehabilitation and domotics devices can be easily controlled by means of BMI technology. Prosthetic hands and wheelchairs are suitable applications but do not attain optimal interactivity. Regarding humanoid robotics, the head and the trunk can be controlled by means of BMIs, while other parts require too much throughput. Robotic arms, which have been controlled by means of cortical invasive interfaces in animal studies, could be the next frontier for non-invasive BMIs. Combining smart controllers with BMIs could improve interactivity and boost BMI applications. © 2007 Elsevier B.V. All rights reserved

Research regarding electro-oculogram based Human Computer Interface (HCI)

In this article an electro-oculogram (EOG) based Human Computer Interface (HCI) will be presented, in order to control the mouse cursor on the screen of a computer or laptop. Electromyography (EMG) is the domain that employs the activation and deactivation (onset and cessation) of the muscles. EOG is the sub-domain of EMG field that focuses on the human eye’s movements. The EOG bio-signals can be recorded using Ag/AgCl electrodes coupled on the user’s skin and fed into a data acquisition device - an analog-to-digital converter (ADC) in order to be transmitted, filtered and processed on a computer or laptop. We acquired the EOG bio-signals with a 24-bit, 4 channel, 51200 samples/s per channel ADC, made by the National Instruments (N.I.), model NI-9234 industrial ADC, using only 3 recording channels and electrodes. After processing, the program running on the computer or laptop can be used to realize commands or control different applications according to the recorded bio-signals. In our case, this was done, using Artificial Neural Network (ANN) toolbox of MATLAB®. This HCI can be used by perfectly healthy or even by disabled people. In the case of disabled people, these systems can be used to control any electronic device connected to the computer or control the device itself. Applications of this type of HCIs can be Internet browsing, mail writing, word file editing, etc. This system is meant to offer a new way of computer control - other than the existing standard communication and/or control possibilities (like keyboard and/or mouse)

Neuro-electronic technology in medicine and beyond

This dissertation looks at the technology and social issues involved with interfacing electronics directly to the human nervous system, in particular the methods for both reading and stimulating nerves. The development and use of cochlea implants is discussed, and is compared with recent developments in artificial vision. The final sections consider a future for non-medicinal applications of neuro-electronic technology. Social attitudes towards use for both medicinal and non-medicinal purposes are discussed, and the viability of use in the latter case assessed

Craniux: A LabVIEW-Based Modular Software Framework for Brain-Machine Interface Research

This paper presents “Craniux,” an open-access, open-source software framework for brain-machine interface (BMI) research. Developed in LabVIEW, a high-level graphical programming environment, Craniux offers both out-of-the-box functionality and a modular BMI software framework that is easily extendable. Specifically, it allows researchers to take advantage of multiple features inherent to the LabVIEW environment for on-the-fly data visualization, parallel processing, multithreading, and data saving. This paper introduces the basic features and system architecture of Craniux and describes the validation of the system under real-time BMI operation using simulated and real electrocorticographic (ECoG) signals. Our results indicate that Craniux is able to operate consistently in real time, enabling a seamless work flow to achieve brain control of cursor movement. The Craniux software framework is made available to the scientific research community to provide a LabVIEW-based BMI software platform for future BMI research and development