Alternative communication systems for people with severe motor disabilities: a survey <s> EMG signal <s> 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's 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. <s> BIB001 </s> Alternative communication systems for people with severe motor disabilities: a survey <s> EMG signal <s> Electromyographic (EMG) signals are composed of the superposition of the activity of individual motor units. Techniques exist for the decomposition of an EMG signal into its constituent components. Following is a review and explanation of the techniques that have been used to decompose EMG signals. Before describing the decomposition techniques, the fundamental composition of EMG signals is explained and after, potential sources of information from and various uses of decomposed EMG signals are described. <s> BIB002 </s> Alternative communication systems for people with severe motor disabilities: a survey <s> EMG signal <s> We are developing electromyographic and electroencephalographic methods, which draw control signals for human-computer interfaces from the human nervous system. We have made progress in four areas: 1) real-time pattern recognition algorithms for decoding sequences of forearm muscle activity associated with control gestures; 2) signal-processing strategies for computer interfaces using electroencephalogram (EEG) signals; 3) a flexible computation framework for neuroelectric interface research; and d) noncontact sensors, which measure electromyogram or EEG signals without resistive contact to the body. <s> BIB003 </s> Alternative communication systems for people with severe motor disabilities: a survey <s> EMG signal <s> The objective of this paper is to develop a powered wheelchair controller based on EMG for users with high-level spinal cord injury. EMG is very naturally measured when the user indicating a certain direction and the force information which will be used for the speed of wheelchair is easily extracted from EMG. Furthermore, the emergency situation based on EMG will be checked relatively ease. We classified the pre-defined motions such as rest case, forward movement, left movement, and right movement by fuzzy min-max neural networks (FMMNN). This classification results and evaluation results with real users shows the feasibility of EMG as an input interface for powered wheelchair. <s> BIB004 </s> Alternative communication systems for people with severe motor disabilities: a survey <s> EMG signal <s> A classification algorithm is developed to translate facial movements into five cursor actions: i) left, ii) right, iii) up, iv) down, and v) left-click. The algorithm utilizes the unique spectral characteristics exhibited by electromyogram (EMG) signals obtained from different muscles in the face to assist in the classification process. A previous three-electrode, EMG-based system was utilized in to perform a similar translation of facial movements into cursor actions. This system also made use of spectral analysis to classify muscle activity. It was found that this system does not always discriminate between the EMG activity assigned to up and down cursor actions efficiently. To remedy this matter, a fourth electrode was added and a new classification algorithm was devised. This paper details the classification algorithm utilized with the four-electrode system. It also compares the effectiveness of the four-electrode system to that of the three-electrode system in classifying EMG activity into cursor actions. This was done through the use of Matlab simulations. It will be shown that the new four-electrode system produces significant improvements in classification performance <s> BIB005 </s> Alternative communication systems for people with severe motor disabilities: a survey <s> EMG signal <s> A Coupled Hidden Markov Model (CHMM) is proposed in this paper to perform multimodal speech recognition using myoeletric signals (MES) from the muscles of vocal articulation. MES signals are immune to noise, and words that are acoustically similar manifest distinctly in MES. Hence, they would effectively complement the acoustic data in a multimodal speech recognition system. Research in Audio-Visual Speech Recognition has shown that CHMMs model the asynchrony between different data streams effectively. Hence, we propose CHMM for multimodal speech recognition using audio and MES as the two data streams. Our experiments indicate that the multimodal CHMM system significantly outperforms the audio only system at different SNRs. We have also provided a comparison between different features for MES and have found that wavelet features provide the best results. <s> BIB006 </s> Alternative communication systems for people with severe motor disabilities: a survey <s> EMG signal <s> This paper outlines the development and initial testing of a new hybrid computer cursor control system based on Eye Gaze Tracking (EGT) and electromyogram (EMG) processing for hands-free control of the computer cursor. The ultimate goal of the system is to provide an efficient computer interaction mechanism for individuals with severe motor disabilities (or specialized operators whose hands are committed to other tasks, such as surgeons, pilots, etc.) The paper emphasizes the enhancements that have been made on different areas of the architecture, with respect to a previous prototype developed by our group, and demonstrates the performance improvement verified for some of the enhancements. <s> BIB007
When the brain commands a muscle to contract, signals are sent to motor neurons that in turn, control several fibres. As the membrane fibre is depolarized, an electrical potential is generated in the vicinity of the muscles fibres with duration of approximately 8 ms. The summation of the action potentials propagating trough the fibres yields the motor unit action potential (MUAP) of a motor unit (MU). To maintain the force exerted by the muscle, the MUAPs are fired repeatedly, with frequency from 7 to 20 Hz  , forming the sequence known as MUAPT (MUAP train). The electrode used to record muscle activity will register the electrical fields generated by all the motor units in the range. This ensemble of MUAPTs is the EMG signal. Recorded at the skin surface, the EMG signal may present 20-2000 μV peak-to-peak amplitude values [6] . There are two kinds of electrodes for EMG signal acquisition: intramuscular and surface electrodes. The former type is preferred for clinical applications, as in the diagnosis and evaluation of motor diseases BIB002 . Information from specific motor units or even fibres can be acquired with confidence; however it may produce infections and the mechanical action into the muscle may cause lesions. Consequently, the choice on using surface electrodes is appropriate for applications such as communication aid devices, as they will probably be used for several hours a day. The most common electrode type is the Ag/AgCl, usually 1-3 cm in diameter. Before the application of the electrode, the skin is cleansed with alcohol-wet swabs and a conductive gel is used to increase conductivity. One of the concerns is that conductivity decreases and thermal noise increases as the gel dries off. While the majority of studies referenced in this article reported the use of Ag/AgCl electrodes, some omitted the information and only one BIB003 reported the utilization of a noncontact electrode that could be used over clothes, however no technical details are provided. Electromyography is the process of recording and analyzing the EMG signal. One of its main applications is making a diagnosis and assessing the severity of the disability in case of neuromuscular disorders. Another application is the possibility of identifying the strategy used to control skeletal muscles during a movement, so it is later employed to operate prosthesis. Yet another approach consists in using the EMG signal as a source of information to control devices such as electrical wheelchairs BIB004 . The number of studies exploring the EMG signal potential for AAC is considerably lower when compared to the attention devoted to other possibilities such as EEG signals. The EMG signal, used as a channel for AAC, is usually acquired with sampling rates in the order of 1 kHz. This aspect is particularly important when spectral tools are employed, considering the Nyquist theorem and that most part of the signal energy is presented up to 500 Hz. Even though, in some studies, the sampling rate may be lower. The reason is not explicit, but could be connected to the necessity of the system to operate in real-time, which could be complicated by a large amount of data generated by higher sampling rates. The algorithms used to process the EMG are usually simple, especially when operating as a SBC sensor. The translation into a binary variable calls for simple strategiesrelated to signal amplitude -such as the variance of the root mean square (RMS) value of the signal  . The spectral domain analysis is also analysed. Signal features such as mean and median frequency (MNF and MDF) can be used to define when a muscle is active as their value shifts during contraction. The spectrum can be divided into sub bands before extracting the features BIB001  BIB005 BIB007 . In BIB007 BIB006 , as various muscles typically supply different MNFs, muscle activity can be correctly detected, in spite of interference due to the activity of others muscles.