A New Method to Generate Artificial Frames Using the Empirical Mode Decomposition for an EEG-Based Motor Imagery BCI

• Published in: Frontiers in neuroscience Vol. 12; p. 308
• Main Authors: Dinarès-Ferran, Josep, Ortner, Rupert, Guger, Christoph, Solé-Casals, Jordi
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Research Foundation 11.05.2018; Frontiers Media S.A
• Subjects: artificial frames; Biology; brain-computer interface; Calibration; Consciousness; EEG; Electroencephalography; empirical mode decomposition; Engineering; Interfaces; Medicine; Mental task performance; motor imagery; Neurology; Neuroscience; Patients; Rehabilitation; Signal processing; Stroke; Spain; motor imagery; artificial frames; empirical mode decomposition; EEG; brain-computer interface
• ISSN: 1662-453X; 1662-4548; 1662-453X
• DOI: 10.3389/fnins.2018.00308

EEG-based Brain-Computer Interfaces (BCIs) are becoming a new tool for neurorehabilitation. BCIs are used to help stroke patients to improve the functional capability of the impaired limbs, and to communicate and assess the level of consciousness in Disorder of Consciousness (DoC) patients. BCIs based on a motor imagery paradigm typically require a training period to adapt the system to each user's brain, and the BCI then creates and uses a classifier created with the acquired EEG. The quality of this classifier relies on amount of data used for training. More data can improve the classifier, but also increases the training time, which can be especially problematic for some patients. Training time might be reduced by creating new artificial frames by applying Empirical Mode Decomposition (EMD) on the EEG frames and mixing their Intrinsic Mode Function (IMFs). The purpose of this study is to explore the use of artificial EEG frames as replacements for some real ones by comparing classifiers trained with some artificial frames to classifiers trained with only real data. Results showed that, in some subjects, it is possible to replace up to 50% of frames with artificial data, which reduces training time from 720 to 360 s. In the remaining subjects, at least 12.5% of the real EEG frames could be replaced, reducing the training time by 90 s. Moreover, the method can be used to replace EEG frames that contain artifact, which reduces the impact of rejecting data with artifact. The method was also tested on an out of sample scenario with the best subjects from a public database, who yielded very good results using a frame collection with 87.5% artificial frames. These initial results with healthy users need to be further explored with patients' data, along with research into alternative IMF mixing strategies and using other BCI paradigms.