Course Content

The aim is to recognize person identity based on brain activity, measured by EEG signals. Recently, classification from EEG data has attracted much attention with the rapid development of machine learning algorithms, and various real-world applications of brain–computer interface for normal people. Until now, researchers had little understanding of the details of relationship between different emotional states and various EEG features. With the help of EEG-based human identification, the computer can have a look inside user’s head to observe user's mental state. We systematically perform feature extraction, feature selection, feature smoothing and pattern classification methods in the process. The best features extracted are specified in detail and their effectiveness is proven by classification results. Human identification based on Face recognition is one of the latest technology being studied area in biometric as it has wide area of applications. But Face detection is one of the challenging problems in Image processing. The basic aim of face detection is determine if there is any face in an image & then locate position of a face in an image. Evidently face detection is the first step towards creating an automated system which may involve other face processing. The deep learning neural network needs to be created & trained with training set of faces & non-faces. All results are implemented in MATLAB 2013 environment. Database is collected for different persons from online EEG data base which is meant for research.

Abstract—Electroencephalography (EEG) is another mode for performing Person Identification (PI). Due to the nature of the EEG signals, EEG-based PI is typically done while the person is performing some kind of mental task, such as motor control. However, few works have considered EEG-based PI while the person is in different mental states (affective EEG). The aim of this paper is to improve the performance of affective EEGbased PI using a deep learning approach.

The proposed method is evaluated on the state-of-the-art affective dataset DEAP. The results indicate that CNN-GRU and CNN-LSTM can perform PI from different affective states and reach up to 99.90–100% mean Correct Recognition Rate (CRR), significantly outperforming a support vector machine (SVM) baseline system that uses power spectral density (PSD) features. Notably, the 100% mean CRR comes from only 40 subjects in DEAP dataset. To reduce the number of EEG electrodes from thirty-two to five for more practical applications, the frontal region gives the best results reaching up to 99.17% CRR (from CNN-GRU). Amongst the two deep learning models, we find CNN-GRU to slightly outperform CNN-LSTM, while having faster training time.

IN today's world of large and complex data-driven applications, research engineers are inspired to incorporate multiple layers of artificial neural networks or deep learning (DL) techniques into health informatic-related studies such as bioinformatics, medical imaging, pervasive sensing, medical informatics and public health [1]. Such studies also include those relating to frontier neural engineering research into brain activity using the non-invasive measurement technique called electroencephalography (EEG). The fundamental concept of EEG involves measuring electrical activity (variation of voltages) across the scalp. The EEG signal is one of the most complex in health data and can benefit from DL techniques in various applications such as insomnia diagnosis, seizure detection, sleep studies, emotion recognition, and Brain-Computer Interface (BCI) [2]–[7]. However, EEG-based Person Identification (PI) research using DL is scarcely found in literature. Thus, we are motivated to work in this direction.