Clinical Brain-Computer Interface Challenge 2020 (CBCIC at WCCI2020): Overview, methods and results

TL;DR

This paper presents the Clinical BCI Challenge 2020, providing a new dataset of stroke patients and evaluating algorithms for clinical BCI applications, emphasizing within- and cross-subject performance for real-world use.

Contribution

It introduces a novel open-access dataset of stroke patients and benchmarks algorithms in both within- and cross-subject categories for clinical BCI.

Findings

Winning algorithms achieved promising results in both categories.

Cross-subject algorithms showed potential for calibration-free BCI.

The dataset enables better testing of clinical BCI algorithms.

Abstract

In the field of brain-computer interface (BCI) research, the availability of high-quality open-access datasets is essential to benchmark the performance of emerging algorithms. The existing open-access datasets from past competitions mostly deal with healthy individuals' data, while the major application area of BCI is in the clinical domain. Thus the newly proposed algorithms to enhance the performance of BCI technology are very often tested against the healthy subjects' datasets only, which doesn't guarantee their success on patients' datasets which are more challenging due to the presence of more nonstationarity and altered neurodynamics. In order to partially mitigate this scarcity, Clinical BCI Challenge aimed to provide an open-access rich dataset of stroke patients recorded similar to a neurorehabilitation paradigm. Another key feature of this challenge is that unlike many competitions in the past, it was designed for algorithms in both with-in subject and cross-subject categories as a major thrust area of current BCI technology is to realize calibration-free BCI designs. In this paper, we have discussed the winning algorithms and their performances across both competition categories which may help develop advanced algorithms for reliable BCIs for real-world practical applications.