comparative evaluation of state of the art algorithms for ssvep based bcis

• DOI: 10.5281/zenodo.581153
• Publication Date: 2016-01-01
• Authors: Elisavet Chatzilari; Katerina Adam; Ioannis Kompatsiaris; Kostantinos Georg...; Spiros Nikolopoulos; Vangelis P. Oikon...; Georgios Liaros
• Keywords: FOS: Computer and information sciences; Computer Vision and Pattern Recognition (cs.CV); Computer Science - Human-Computer Interaction; Computer Science - Computer Vision and Pattern Recognition; Machine Learning (stat.ML); 02 engineering and technology; Human-Computer Interaction (cs.HC); 03 medical and health sciences; 0302 clinical medicine; Statistics - Machine Learning; 0202 electrical engineering, electronic engineering, information engineering

Brain-computer interfaces (BCIs) have been gaining momentum in making human-computer interaction more natural, especially for people with neuro-muscular disabilities. Among the existing solutions the systems relying on electroencephalograms (EEG) occupy the most prominent place due to their non-invasiveness. However, the process of translating EEG signals into computer commands is far from trivial, since it requires the optimization of many different parameters that need to be tuned jointly. In this report, we focus on the category of EEG-based BCIs that rely on Steady-State-Visual-Evoked Potentials (SSVEPs) and perform a comparative evaluation of the most promising algorithms existing in the literature. More specifically, we define a set of algorithms for each of the various different parameters composing a BCI system (i.e. filtering, artifact removal, feature extraction, feature selection and classification) and study each parameter independently by keeping all other parameters fixed. The results obtained from this evaluation process are provided together with a dataset consisting of the 256-channel, EEG signals of 11 subjects, as well as a processing toolbox for reproducing the results and supporting further experimentation. In this way, we manage to make available for the community a state-of-the-art baseline for SSVEP-based BCIs that can be used as a basis for introducing novel methods and approaches.