Real-Time EMG Based Pattern Recognition Control for Hand Prostheses: A Review on Existing Methods, Challenges and Future Implementation <s> Future Prospects-Implementation of Real-Time EMG-PR Control <s> The electroencephalogram (EEG) is modified by motor imagery and can be used by patients with severe motor impairments (e.g., late stage of amyotrophic lateral sclerosis) to communicate with their environment. Such a direct connection between the brain and the computer is known as an EEG-based brain-computer interface (BCI). This paper describes a new type of BCI system that uses rapid prototyping to enable a fast transition of various types of parameter estimation and classification algorithms to real-time implementation and testing. Rapid prototyping is possible by using Matlab, Simulink, and the Real-Time Workshop. It is shown how to automate real-time experiments and perform the interplay between on-line experiments and offline analysis. The system is able to process multiple EEG channels on-line and operates under Windows 95 in real-time on a standard PC without an additional DSP board. The BCI can be controlled over the Internet, LAN or modem. This BCI was tested on 3 subjects whose task it was to imagine either left or right hand movement. A classification accuracy between 70% and 95% could be achieved with two EEG channels after some sessions with feedback using an adaptive autoregressive model and linear discriminant analysis. <s> BIB001 </s> Real-Time EMG Based Pattern Recognition Control for Hand Prostheses: A Review on Existing Methods, Challenges and Future Implementation <s> Future Prospects-Implementation of Real-Time EMG-PR Control <s> Ninety-nine healthy people participated in a brain-computer interface (BCI) field study conducted at an exposition held in Graz, Austria. Each subject spent 20-30 min on a two-session BCI investigation. The first session consisted of 40 trials conducted without feedback. Then, a subject-specific classifier was set up to provide the subject with feedback, and the second session - 40 trials in which the subject had to control a horizontal bar on a computer screen - was conducted. Subjects were instructed to imagine a right-hand movement or a foot movement after a cue stimulus depending on the direction of an arrow. Bipolar electrodes were mounted over the right-hand representation area and over the foot representation area. Classification results achieved with 1) an adaptive autoregressive model (39 subjects) and 2) band power estimation (60 subjects) are presented. Roughly 93% of the subjects were able to achieve classification accuracy above 60% after two sessions of training. <s> BIB002 </s> Real-Time EMG Based Pattern Recognition Control for Hand Prostheses: A Review on Existing Methods, Challenges and Future Implementation <s> Future Prospects-Implementation of Real-Time EMG-PR Control <s> Feedback plays an important role when learning to use a brain computer interface (BCI), particularly in the case of synchronous feedback that relies on the interaction subject. In this preliminary study, we investigate the role of combined auditory-visual feedback during synchronous μ rhythm-based BCI sessions to help the subject to remain focused on the selected imaginary task. This new combined feedback, now integrated within the general purpose BCI2000 software, has been tested on eight untrained and three trained subjects during a monodimensional left-right control task. In order to reduce the setup burden and maximize subject comfort, an electroencephalographic device suitable for dry electrodes that required no skin preparation was used. Quality and index of improvement was evaluated based on a personal self-assessment questionnaire from each subject and quantitative data based on subject performance. Results for this preliminary study show that the combined feedback was well tolerated by the subjects and improved performance in 75% of the naive subjects compared with visual feedback alone. <s> BIB003 </s> Real-Time EMG Based Pattern Recognition Control for Hand Prostheses: A Review on Existing Methods, Challenges and Future Implementation <s> Future Prospects-Implementation of Real-Time EMG-PR Control <s> Brain computer interface (BCI) is an assistive technology, which decodes neurophysiological signals generated by the human brain and translates them into control signals to control external devices, e.g., wheelchairs. One problem challenging noninvasive BCI technologies is the limited control dimensions from decoding movements of, mainly, large body parts, e.g., upper and lower limbs. It has been reported that complicated dexterous functions, i.e., finger movements, can be decoded in electrocorticography (ECoG) signals, while it remains unclear whether noninvasive electroencephalography (EEG) signals also have sufficient information to decode the same type of movements. Phenomena of broadband power increase and low-frequency-band power decrease were observed in EEG in the present study, when EEG power spectra were decomposed by a principal component analysis (PCA). These movement-related spectral structures and their changes caused by finger movements in EEG are consistent with observations in previous ECoG study, as well as the results from ECoG data in the present study. The average decoding accuracy of 77.11% over all subjects was obtained in classifying each pair of fingers from one hand using movement-related spectral changes as features to be decoded using a support vector machine (SVM) classifier. The average decoding accuracy in three epilepsy patients using ECoG data was 91.28% with the similarly obtained features and same classifier. Both decoding accuracies of EEG and ECoG are significantly higher than the empirical guessing level (51.26%) in all subjects (p<0.05). The present study suggests the similar movement-related spectral changes in EEG as in ECoG, and demonstrates the feasibility of discriminating finger movements from one hand using EEG. These findings are promising to facilitate the development of BCIs with rich control signals using noninvasive technologies. <s> BIB004 </s> Real-Time EMG Based Pattern Recognition Control for Hand Prostheses: A Review on Existing Methods, Challenges and Future Implementation <s> Future Prospects-Implementation of Real-Time EMG-PR Control <s> This paper compiles and analyzes some of the most current works related to upper limb prosthesis with emphasis on man-machine interfaces. A brief introduction of the basic subjects is given to explain what a prosthesis is, what types of prostheses exist, what they serve for, how they communicate with the user (control and feedback), and what technologies are involved. The method used in this review is also discussed, as well as the cataloging process and analysis of articles for the composition of this review. Each article is analyzed individually and its results are presented in a succinct way, in order to facilitate future research and serve as a source for professionals related to the area of prosthesis, such as doctors, engineers, researchers, and anyone interested in this subject. Finally, the needs and difficulties of the current prostheses, as well as the negative and positive points in the results are analyzed, and the progress achieved so far is discussed. <s> BIB005
• Electroencephalogram (EEG), and ECog (Electrocortocogram) measures brain signals, and they could be used to supersede EMG for prostheses control. ECog electrodes are invasive as they are placed directly inside the head, whereas EEG electrodes are non-invasive, as they are positioned on the scalp area BIB005 , where information regarding the targeted body movements are measurable [101] . EEG and ECog have currently found an application as a brain-machine interface BIB001 , and in theory, can control the movement of the prosthesis similar to the EMG. In other words, while EMG measures the electric current from muscles and provides the control signal according to the action intended by subject  , brain-machine interface decodes the electrical signal generated from the brain and converts them to the control signal for the control of prostheses BIB004 without using a muscle as an intermediate BIB005 . Unfortunately, due to the invasiveness (ECog) and the problem associated with electrodes montage stability (EEG), generalised poor signal-to-noise ratio (SNR) and the poor spatial resolution of the signals, not to mention the discomfort related to the need of having multiple devices over the subject's body (i.e., head and limbs), we believe that, at present, these devices may be better suited for patients with spinal cord injury where voluntary EMG signals may be not available. It is necessary to mention that another issue often related to the use of brain signals to drive external devices is the need of extensive training BIB003 BIB002 and poor performances of the brain to computer interface.