Research about pattern recognition on electroencephalography (EEG) signal of finger motor imagery (MI) plays a critical role in Brain-Computer Interfaces (BCI) based hand prosthetics development. However, the previous research still used irrelevant channels to finger MI. This work proposed optimal EEG channels combination for five-finger MI. It is achieved by subject-dependence channel selection using One versus Rest Common Spatial Pattern (CSP-OVR) combined with sequential searching algorithms due to specific neural activation areas of MI. Optimal channels combinations are of great importance to reduce channels number. It supports the development of practical BCI-based hand prosthetics that can help hand handicapped to do daily activities easier. Experimental results show 4 out of 19 channels are relevant to five-finger MI with 0,6% accuracy degradation compared with EEG-MI pattern recognition using 19 channels. This result is better than the Principal Component Analysis (PCA) channel selection method that only selects 11 out of 19 channels with 1 % accuracy degradation.
As an application of EEG, Motor Imagery based Brain-Computer Interface (MI BCI) plays a significant role in assisting patients with disability to communicate with their environment. MI BCI could now be realized through various methods such as machine learning. Many attempts using different machine learning approaches as MI BCI applications have been done with every one of them yielding various results. While some attempts managed to achieve agreeable results, some still failed. This failure may be caused by the separation of the feature extraction and classification steps, as this may lead to the loss of information which in turn causes lower classification accuracy. This problem can be solved by integrating feature extraction and classification by harnessing a classification algorithm that processed the input data as a whole until it produces the prediction, hence the use of convolutional neural network (CNN) approach which is known for its versatility in processing and classifying data all in one go. In this study, the CNN exploration involved a task to classify 5 different classes of fingers’ imaginary movement (thumb, index, middle, ring, and pinky) based on the processed raw signal provided. The CNN performance was observed for both non-augmented and augmented data with the data augmentation techniques used include sliding window, noise addition, and the combination of those two methods. From these experiments, the results show that the CNN model managed to achieve an averaged accuracy of 47%, meanwhile with the help of augmentation techniques of sliding window, noise addition, and the combined methods, the model achieved even higher averaged accuracy of 57,1%, 47,2%, and 57,5% respectively.
This research aims to find out how the process of making hot asphalt mixtures (AC-WC) using grated mantup as a substitute for coarse aggression, and aims to find out how the influence of using grated mantras as hot asphalt mixture (AC-WC)This research method used is the exprimental trial and error method of Marshall testing methodology to analyze the properties of percent cavity in the mixture (VIM), percent cavity filled with asphalt (VFB), percent cavity between mineral aggregate (VMA), stability (Stability) , melt (Flow) and Marshall Quatient.The substitution of cricile variation is 0%, 25%, 50%, 100% of the coarse aggregate weight in this study indicating that the most ideal Marshall Properties value is calculated using the regression model equation with the highest index of determination where the index value of determination is obtained ( R2) = 1 for Marshall properties which is the highest is the substitution of gravel mantup 100% with Marshall parameters which includes: Stability 979.03 kg, VIM 97.71%, VMA 18.68%, VFWA 78.21%, Flow 3.13 mm , Marshall Question 316.46%. From these results the substitution of Krikil Mantup with a level of 100% meets the criteria in Indonesian national standards.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.