Recurrence analysis in the detection of continuous task episodes for asynchronous BCI

• Published in: Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics pp. 1511 - 1517
• Main Authors: Ivette Ledesma-Ramirez, Claudia, Bojorges-Valdez, Erik, Yanez-Suarez, Oscar, Pina-Ramirez, Omar
• Format: Conference Proceeding
• Language: English
• Published: IEEE 11.10.2020
• Subjects: asynchronous BCI; Brain-computer interfaces; Computational efficiency; Electroencephalography; Feature extraction; mental calculation; Radio frequency; random forests; Real-time systems; Recurrence quantification analysis; Task analysis
• ISSN: 2577-1655
• DOI: 10.1109/SMC42975.2020.9282907

Asynchronous Brain Computer Interfaces (BCI) al-low system activation at free will. This scenario would be desirable for potential users such as patients with motor disabilities, however, there are still several limitations such as long calibration times and overall performance. Different approaches to improve these systems explore diverse linear electroencephalography (EEG) features in order to describe cognitive states. Alternatively, the present study proposes the use of recurrence quantification analysis (RQA), which provides indices about the dynamical behaviour of non-linear systems. We evaluated the accuracy of continuous detection of mental calculation and idle state using RQA features from EEG recordings. Among the different classification methods that were tested, random forest (RF) classifier resulted in the best performance. From 35 selected RQA features, 12 healthy subjects achieved mean accuracy of 0.924 ±0.048, whereas with 150 selected features mean accuracy was 0.977 ±0.020. These results suggest that RQA features are adequate for continuous task episode detection in asynchronous BCI. However, a restriction in the number of these features, conceived for improving computational costs and time, does impact detection performance.