Japan Surgical Society:Tohoku Meeting

Feeding difficulties and malnutrition are common phenomena in amyotrophic lateral sclerosis patients, locked in patients and people with upper limb disability. Feeding is often time consuming, unpleasant, and may result in choking or asphyxiation. Nowadays, robotic aids are applied to assist these people for eating. However, assistive robots that require movements from the user are not suitable for people with critical disabilities, including sensory losses, and/or difficulty in basic physical mobility. In this regard, a robotic system that can be controlled merely by brain signals is quite a remarkable aid. Therefore, based on the requirements for real-time assistive robot a prototype of an EEG-based feeding robot is proposed. The proposed feeding system enables the target group to eat independently. Experimental results show that the developed system is able to perform the required tasks, in real-time, with tolerable errors of around 17% in average. This amount of error can be further supervised to be reduced or in some cases even eliminated

Motor imagery task classification using transformation based features

tThis paper proposes a feature extraction method named as LP QR, based on the decomposition of theLPC filter impulse response matrix of the signal of interest. This feature extraction method is inspired byLP SVD and is tested in the context of motor imagery electroencephalogram. The extracted features areclassified and benchmarked against extracted features of LP SVD method. The two applied methods arealso compared regarding the required execution time, which further highlights their respective meritsand demerits. This paper closely examines the contribution of EEG channels of these two informationextraction algorithms too. Consequently, a detailed analysis of the role of EEG channels concerning thenature of the extracted information is presented. This study is conducted on the BCI IIIa competitiondatabase of four motor imagery movements. The obtained results indicate that the proposed method isthe better choice if simplicity is demanded. The investigation into the role of EEG channels reveals thatlevel of contribution each channel can be quite dissimilar for different feature extraction algorithms

Testing and analysis of the proposed data driven method on the opportunity human activity dataset

This paper proposes a data-driven method for constructing materials to be used in a probabilistic knowledge base for human activity recognition. The utilized dataset, challenge subset of Opportunity, is a publicly available dataset. It consists of a set of daily activities, which has been manually labeled as modes of locomotion and gestures. We applied several methods to extract proper features from sensors on bodies of subjects, then, chosen features are fed into two different classifiers. Finally, predicted labels for modes of locomotion and hand gestures are calculated. To evaluate the method, the recognition rates are bench marked against the results of the competitors who have participated in Opportunity challenge as well as the baseline results provided by the Opportunity group. For modes of locomotion, our results surpass all of the available results and in some cases the recognition rate of our model is very close to the highest recognition rate. For gestures, regular or noisy data,in some cases our method is still higher than baseline or challenge participants but unlike locomotion, it is not capable to beat them all

Evaluation of time-domain features for motor imagery movements using FCM and SVM

Brain–Machine Interface is a direct communication pathway between brain and an external electronic device. BMIs aim to translate brain activities into control commands. To design a system that translates brain waves and its activities to desired commands, motor imagery tasks classification is the core part. Classification accuracy not only depends on how capable the classifier is but also it is about the input data. Feature extraction is to highlight the properties of signal that make it distinct from the signal of the other mental tasks. Performance of BMIs directly depends on the effectiveness of the feature extraction and classification algorithms. If a feature provides large interclass difference for different classes, the applied classifier exhibits a better performance. In order to attain less computational complexity, five timedomain procedure, namely: Mean Absolute Value, Maximum peak value, Simple Square Integral, Willison Amplitude, and Waveform Length are used for feature extraction of EEG signals. Two classifiers are applied to assess the performance of each feature-subject. SVM with polynomial kernel is one of the applied nonlinear classifier and supervised FCM is the other one. The performance of each feature for input data are evaluated with both classifiers and classification accuracy is the considered common comparison parameter

Robust classification of motor imagery EEG signals using statistical time–domain features

The tradeoff between computational complexity and speed, in addition to growing demands for real-time BMI (brain–machine interface) systems, expose the necessity of applying methods with least possible complexity. Willison amplitude (WAMP) and slope sign change (SSC) are two promising time– domain features only if the right threshold value is defined for them. To overcome the drawback of going through trial and error for the determination of a suitable threshold value, modified WAMP and modified SSC are proposed in this paper. Besides, a comprehensive assessment of statistical time–domain features in which their effectiveness is evaluated with a support vector machine (SVM) is presented. To ensure the accuracy of the results obtained by the SVM, the performance of each feature is reassessed with supervised fuzzy C-means. The general assessment shows that every subject had at least one of his performances near or greater than 80%. The obtained results prove that for BMI applications, in which a few errors can be tolerated, these combinations of feature–classifier are suitable. Moreover, features that could perform satisfactorily were selected for feature combination. Combinations of the selected features are evaluated with the SVM, and they could significantly improve the results, in some cases, up to full accuracy

Signal processing of EMG signal for continuous thumb-angle estimation

Human hand functions range from precise-minute handling to heavy and robust movements. Developing an artificial thumb which can mimic the actions of a real thumb precisely is a major achievement. Despite many efforts dedicated to this area of research, control of artificial thumb movements in resemblance to our natural movement, still poses asa challenge. Most of the development in this area is based on discontinuous thumb position control, which makes it possible to recreate several of the most important functions of the thumb but does not result in total imitation. The paper looks into the classification of Electromyogram (EMG) signals from thumb muscles for the prediction of thumb angle during flexion motion. For this purpose, an experimental setup is developed to measure the thumb angle throughout the range of flexion and simultaneously gather the EMG signals. Further various different features are extracted from these signals for classification and the most suitable feature set is determined and applied to different classifiers. A ‘piecewise-discretization’ approach is used for continuous angle prediction. The most determinant features are found to be the 2nd order Auto-regressive (AR) coefficients with Simple Square Integral (SSI) giving an accuracy of 85.41% in average while the best classification method is Support Vector Machine (SVM - with Puk kernel) with an average accuracy of 86.53

Evaluating the effectiveness of time-domain features for motor imagery movements using SVM

Motor imagery electroencephalogram signals are the only bio-signals that enable locked-in patients, who have lost control over every motor output, to communicate with and control their surroundings. Brain Machine Interface is collaboration between a human and machines, which translates brain waves to desired, understandable commands for a machine. Classification of motor imagery tasks for BMIs is the crucial part. Classification accuracy not only depends on how accurate and robust the classifier is; it is also about data. For well separated data, classifiers such as kernel SVM can handle classification and deliver acceptable results. If a feature provides large interclass difference for different classes, immunity to random noise and chaotic behavior of EEG signal is rationally conformed, which means the applied feature is suitable for classifying EEG signals. In this work, in order to have less computational complexity, time-domain algorithms are employed to motor imagery signals. Extracted features are: Mean Absolute Value, Maximum peak value, Simple Square Integral, Willison Amplitude, and Waveform Length. Support Vector Machine with polynomial kernel is applied for classification of four different classes of data. The obtained results show that these features have acceptable, distinct values for different these four motor imagery tasks. Maximum classification accuracy belongs to contribution of Willison amplitude as feature and SVM as classifier, with 95.1 percentages accuracy. Where, the lowest is the contribution of Waveform Length and SVM with 31.67 percentages classification accuracy

Signal processing of EMG signal for continuous thumbangle estimation

Human hand functions range from precise-minute handling to heavy and robust movements. Developing an artificial thumb which can mimic the actions of a real thumb precisely is a major achievement. Despite many efforts dedicated to this area of research, control of artificial thumb movements in resemblance to our natural movement, still poses as a challenge. Most of the development in this area is based on discontinuous thumb position control, which makes it possible to recreate several of the most important functions of the thumb but does not result in total imitation. The paper looks into the classification of Electromyogram (EMG) signals from thumb muscles for the prediction of thumb angle during flexion motion. For this purpose, an experimental setup is developed to measure the thumb angle throughout the range of flexion and simultaneously gather the EMG signals. Further various different features are extracted from these signals for classification and the most suitable feature set is determined and applied to different classifiers. A ‘piecewise-discretization’ approach is used for continuous angle prediction. The most determinant features are found to be the 2nd order Auto-regressive (AR) coefficients with Simple Square Integral (SSI) giving an accuracy of 85.41% in average while the best classification method is Support Vector Machine (SVM - with Puk kernel) with an average accuracy of 86.53%

A transform-based feature extraction approach for motor imagery tasks classification

In this paper, we present a new motor imagery classification method in the context of electroencephalography (EEG)-based brain-computer interface (BCI). This method uses a signal-dependent orthogonal transform, referred to as linear prediction singular value decomposition (LP-SVD), for feature extraction. The transform defines the mapping as the left singular vectors of the LP coefficient filter impulse response matrix. Using a logistic tree-based model classifier; the extracted features are classified into one of four motor imagery movements. The proposed approach was first benchmarked against two related state-of-the-art feature extraction approaches, namely, discrete cosine transform (DCT) and adaptive autoregressive (AAR)-based methods. By achieving an accuracy of 67.35%, the LP-SVD approach outperformed the other approaches by large margins (25% compared with DCT and 6 % compared with AAR-based methods). To further improve the discriminatory capability of the extracted features and reduce the computational complexity, we enlarged the extracted feature subset by incorporating two extra features, namely, Q- and the Hotelling's $T^{2}$ statistics of the transformed EEG and introduced a new EEG channel selection method. The performance of the EEG classification based on the expanded feature set and channel selection method was compared with that of a number of the state-of-the-art classification methods previously reported with the BCI IIIa competition data set. Our method came second with an average accuracy of 81.38%