Ensemble Learning to EEG-Based Brain Computer Interfaces with Applications on P300-Spellers

• Published in: Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics pp. 631 - 638
• Main Authors: Barsim, Karim Said, Zheng, Wangbo, Yang, Bin
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.10.2018
• Subjects: Brain Computer Interfaces (BCI); Convolutional Neural Networks (CNN); Electroencephalography (EEG); Ensemble Learning; Interleaved Group Convolutions (IGC); Support Vector Machines (SVM)
• ISSN: 2577-1655
• DOI: 10.1109/SMC.2018.00116

Brain-Computer Interfaces (BCI) are systems in which the electrical activity of an animal brain becomes the main controller of an external electronic device capable of reading and processing electroencephalographic (EEG) signals. One early application of such systems is the attention-based spellers utilizing the P300 visually evoked potential in a framework known as the oddball paradigm. In this paper, we propose novel variants of machine learning model ensembles in addressing the task of P300 detection and attended target recognition in attention-based speller systems. Proposed ensembles adopted Bootstrap aggregation (Bagging) of calibrated Support Vector Machines (SVM) as well as data-driven learners. The latter is dominantly represented by Convolutional Neural Networks (CNN) with several variants, including what is referred to as Inception, Xception, and Interleaved Group Convolutions (IGC) modules. The proposed models are evaluated on a publicly available EEG dataset developed specifically for BCI applications and published in public contests, namely the 2nd dataset of the 3rd BCI competitions. The proposed models consistently outperform all previous works on the same dataset and show the highest 5- and 15-trial recognition rates of 76.5% and 98.5%, respectively, for both subjects in the dataset jointly. Additionally, we introduce in this work the first study on inter-subject evaluation under similar training protocols, reaching between 30%-40% recognition rates for either subject. We further investigate the effect of reducing the training data on the performance of the proposed models showing possibilities of reduced training time for a target recognition rate.