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In this paper, an application of nonlinear principal component analysis for online P300 extraction and classification is proposed. In order to cover the nonlinearity between the variables, a five-layer neural network is applied for feature extraction. The experimental results in this work show that the implementation of the proposed method achieves a very significant statistical improvement in extracting and classifying P300 components. After a short time of practice, most participants could learn to extract and classify the P300 wave with greater than 80% accuracy.

Eye blinking known as ocular artifact cause changes to the electric fields over the scalp and as a result, EEG recordings are often significantly distorted and theirinterpretation problematic. In this paper, an algorithm using second order blind identification with robust orthogonalization (SOBI-RO) is used to remove the ocular artifact in amotor imagery experiment.Simulation results shows that the ocular artifacts are significantly removed and the sources of the brain activity are clearly identified. The identification performance using signal to distortion ratio value about 68.88% is achieved.

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In this era, face recognition technology is an important component that is widely used in various aspects of life, mostly for biometrics issues for personal identification. There are three main steps of a face recognition system:face detection, face embedding, and classification. Classification plays a vital role in making the system recognizes a face accurately. With the growing need for face recognition applications, the need for machine learning methods are required for accurate image classification is also increasing. One thing that can be done to increase the performance of the classifier is by tuning the hyperparameter. For this study, the evaluation performance of classification is conducted to obtain the best classifier among four different classifier algorithms (decision tree, SVM, random forest, and AdaBoost) for a specific dataset by tuning the hyperparameter. The best classifier is obtained by evaluating the performance of each classifier in terms of training time, accuracy, precision, recall, and F1-score. This study was using a dataset of 2267 facial data (128D vector space) derived from the face embedding process. The result showed that SVM is the best classifier with a training time of 0.5 s and the score for accuracy, precision, recall, and F1-score are about 98%.