Nowadays, there are millions of people around the world suffer from the disability caused by big stroke. In recent years we have seen a rising interest in brain computer interface (BCI) systems that help those patients to practice their normal lives. Therefore, this work presents a GUI application based on an offline BCI system to test their mental capacities. This application was designed based on three tests are vocabulary, arithmetic and Raven’s progressive matrices. The success of this system depends on the choice of the processing techniques. Therefore, Discrete Wavelet Transform (DWT) and Principal Components Analysis (PCA) were used to extract a set of statistical features from the recorded brain signals. These features were classified into four classes are head movement to up, down, right or left using three classifiers are Artificial Neural Network (ANN), Support Vector Machine (SVM) and Linear Discriminant Analysis (LDA). The performance of classifiers was measured using the most frequently statistical parameters: the sensitivity, specificity, precision, the total classification accuracy, and the area under receiver operating characteristics curve (AUC). It was concluded that when DWT was used as a feature extraction, ANN and SVM achieved the highest classification accuracy with a value of 95.24% but when using PCA, ANN achieved the highest classification accuracy with a value of 92.86%. On the other hand, LDA classifier was the worst among the three classifiers. |
