A Review and Classification of Widely used Offline Brain Datasets

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.

@article{Wasim2018,
  title     = {A Review and Classification of Widely used Offline Brain Datasets},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {9},
  number    = {2},
  year      = {2018},
  publisher = {The Science and Information Organization},
  author    = {Muhammad Wasim and Muhammad Sajjad and Farheen Ramzan and Usman Ghani Khan and Waqar Mahmood},
  doi       = {10.14569/IJACSA.2018.090254},
  url       = {https://doi.org/10.14569/IJACSA.2018.090254}
}