Pseudo-online framework for BCI evaluation: a MOABB perspective using various MI and SSVEP datasets

• Published in: Journal of neural engineering Vol. 21; no. 1; pp. 16003 - 16023
• Main Authors: Carrara, Igor, Papadopoulo, Theodore
• Format: Journal Article
• Language: English
• Published: England IOP Publishing 01.02.2024
• Subjects: Algorithms; BCI-EEG; Brain-Computer Interfaces; deep learning; Electroencephalography - methods; Evoked Potentials; Humans; Imagery, Psychotherapy; machine learning; MOABB; pseudo online BCI; Riemann geometry; BCI-EEG; deep learning; Riemann geometry; machine learning; MOABB; pseudo online BCI
• ISSN: 1741-2560; 1741-2552; 1741-2552
• DOI: 10.1088/1741-2552/ad171a

Objective . BCI (Brain–Computer Interfaces) operate in three modes: online , offline , and pseudo-online . In online mode, real-time EEG data is constantly analyzed. In offline mode, the signal is acquired and processed afterwards. The pseudo-online mode processes collected data as if they were received in real-time. The main difference is that the offline mode often analyzes the whole data, while the online and pseudo-online modes only analyze data in short time windows. Offline processing tends to be more accurate, while online analysis is better for therapeutic applications. Pseudo-online implementation approximates online processing without real-time constraints. Many BCI studies being offline introduce biases compared to real-life scenarios, impacting classification algorithm performance. Approach . The objective of this research paper is therefore to extend the current MOABB framework, operating in offline mode, so as to allow a comparison of different algorithms in a pseudo-online setting with the use of a technology based on overlapping sliding windows. To do this will require the introduction of a idle state event in the dataset that takes into account all different possibilities that are not task thinking. To validate the performance of the algorithms we will use the normalized Matthews correlation coefficient and the information transfer rate. Main results . We analyzed the state-of-the-art algorithms of the last 15 years over several motor imagery and steady state visually evoked potential multi-subjects datasets, showing the differences between the two approaches from a statistical point of view. Significance . The ability to analyze the performance of different algorithms in offline and pseudo-online modes will allow the BCI community to obtain more accurate and comprehensive reports regarding the performance of classification algorithms.