## 2 Related Works  
To myo-electrically control a dexterous hand it is necessary to map EMG signals (corresponding to different muscle contractions) to the different existing degree of freedom using pattern recognition based algorithms. To this aim, researchers have been working on various aspects of EMG classification techniques such as electrode placement, selection of suitable feature extraction techniques, and selection of suitable classification algorithms to improve the classification accuracy and increase the number of controllable functions (motions). Electromyography are usually performed by placing several electrodes on the skin. Over the past decades, different electrode placement strategies have been investigated. Some researchers study the use of multichannel electrode arrays [20] or high-density EMG (large number of electrodes) strategy [21; 22], while others explore the precise anatomical positioning approach [18; 23]. In the pattern recognition based control approach, feature extraction and classification are the two important steps in achieving higher classification performance. The feature extraction process involves the transforming of raw EMG signals into feature vector that are assigned to represent different motions. Several features extraction methods have been suggested and these features can be sorted into time domain features [17], frequency domain features [18], and time-frequency domain features [24]. Time domain features are generally evaluated based on signal amplitude that varies with time. On the other hand, frequency domain features contain the power spectrum density of the signals and are computed by parametric methods or a periodogram. Time-frequency domain features contains the combination of temporal and frequency information. These features can characterize varying frequency information at different time locations, providing plentiful non-stationary information of the EMG signals. A list of specific features in each category can be found in [25]. Classification of arm/hand motions based on the extracted features can be performed by a large variety of methods such as linear discriminant analysis [26], support vector machines [18], or artificial neural networks [17]. Using these state-of-the-art approaches, many studies concentrate on the forearm and wrist movements such as flexion/extension, pronation/supination, and sometimes hand grasping/opening (all fingers simultaneously). However, since these systems can only produce a small number of motions, they have yet to fully mimic the human hand. With increasing number of multi-DOF prosthetic hands on the market, additional motions are needed to dexterously control these devices in daily activities. In one survey, ten participants with upper limb deficiency were asked what movements or features useful in a future prosthetic hand [27]. All participants wanted to point with the extended finger, 90% of them desired individual control of fingers, and 70% responded that wrist flexion and extension would be beneficial. Control of the fingers using EMG is extremely challenging compared to forearm/wrist movements. This is because the amplitude of EMG signal for finger movements are generally smaller than those of arm/wrist movements. Furthermore, since the muscles controlling the finger movements lie in the intermediate and deep layers of the forearm [28], attenuation of EMG signals caused by forearm tissues can be observed. Therefore, multiple electrodes are needed to provide enough information to distinguish the intended movement. In recent years, studies on finger movement control have started to appear in literature. However, the number of available literature is still relatively small compared to studies on classification of arm/wrist movements. Furthermore, arm/wrist movement and finger movements are rarely investigated together. However, both wrist and finger movements are needed in daily activities and thus, it is desirable to develop a system that can classify both wrist and finger movements.