Classification of motor imaginary EEG signals using machine learning

Brain Computer Interface (BCI) is a term that was first introduced by Jacques Vidal in the 1970s when he created a system that can determine the human eye gaze direction, making the system able to determine the direction a person want to go or move something to using scalp-recorded visual evoked potential (VEP) over the visual cortex. Ever since that time, many researchers where captivated by the huge potential and list of possibilities that can be achieved if simply a digital machine can interpret human thoughts. In this work, we explore electroencephalography (EEG) signal classification, specifically for motor imagery (MI) tasks. Classification of MI tasks can be carried out by using machine learning and deep learning models, yet there is a trade between accuracy and computation time that needs to be maintained. The objective is to create a machine learning model that can be optimized for real-time classification while having a relatively acceptable classification accuracy. The proposed model relies on common spatial patter (CSP) for feature extraction as well as linear discriminant analysis (LDA) for classification. With simple pre-processing stage and a proper selection of data for training the model proved to have a balanced accuracy of +80% while maintaining small run-time (milliseconds) that is opted for real-time classifications

Vision systems with the human in the loop

The emerging cognitive vision paradigm deals with vision systems that apply machine learning and automatic reasoning in order to learn from what they perceive. Cognitive vision systems can rate the relevance and consistency of newly acquired knowledge, they can adapt to their environment and thus will exhibit high robustness. This contribution presents vision systems that aim at flexibility and robustness. One is tailored for content-based image retrieval, the others are cognitive vision systems that constitute prototypes of visual active memories which evaluate, gather, and integrate contextual knowledge for visual analysis. All three systems are designed to interact with human users. After we will have discussed adaptive content-based image retrieval and object and action recognition in an office environment, the issue of assessing cognitive systems will be raised. Experiences from psychologically evaluated human-machine interactions will be reported and the promising potential of psychologically-based usability experiments will be stressed