A Review and Classification of Widely used Offline Brain Datasets

Abstract

Abstract-Brain Computer Interfaces (BCI) are a natural extension to Human Computer Interaction (HCI) technologies. BCI is especially useful for people suffering from diseases, such as Amyotrophic Lateral Sclerosis (ALS) which cause motor disabilities in patients. To evaluate the effectiveness of BCI in different paradigms, the need of benchmark BCI datasets is increasing rapidly. Although, such datasets do exist, a comparative study of such datasets is not available to the best of our knowledge. In this paper, we provided a comprehensive overview of various BCI datasets. We briefly describe the characteristics of these datasets and devise a classification scheme for them. The comparative study provides feature extractors and classifiers used for each dataset. Moreover, potential use-cases for each dataset are also provided.

Confirmation of Personality Types Using Visual Evoked Potential with User Interface Design Stimulus

“…User interface development made use of EEG has been developed to validate design acceptance (Bang et al, 2011). P300 as brain wave reader devices are effective and efficient increasingly for use for HCI development because from brain waves, using that can know the user's reaction immediately for to a design of user interface (Zickler et al, 2013) and more specific for Brain-Computer Interaction (BCI) (Wasim et al, 2018), (Fira et al, 2018). Human personality is associated with visual design style; the suitability of the design with the visual design requirements to be examined by arranging the position of an object at the display (Ziemkiewicz et al, 2012).…”