Journal Article

A probabilistic approach for calibration time reduction in hybrid EEG–fTCD brain–computer interfaces

Khalaf, Aya, Akcakaya, Murat

2020

Overview

Background Generally, brain–computer interfaces (BCIs) require calibration before usage to ensure efficient performance. Therefore, each BCI user has to attend a certain number of calibration sessions to be able to use the system. However, such calibration requirements may be difficult to fulfill especially for patients with disabilities. In this paper, we introduce a probabilistic transfer learning approach to reduce the calibration requirements of our EEG–fTCD hybrid BCI designed using motor imagery (MI) and flickering mental rotation (MR)/word generation (WG) paradigms. The proposed approach identifies the top similar datasets from previous BCI users to a small training dataset collected from a current BCI user and uses these datasets to augment the training data of the current BCI user. To achieve such an aim, EEG and fTCD feature vectors of each trial were projected into scalar scores using support vector machines. EEG and fTCD class conditional distributions were learnt separately using the scores of each class. Bhattacharyya distance was used to identify similarities between class conditional distributions obtained using training trials of the current BCI user and those obtained using trials of previous users. Results Experimental results showed that the performance obtained using the proposed transfer learning approach outperforms the performance obtained without transfer learning for both MI and flickering MR/WG paradigms. In particular, it was found that the calibration requirements can be reduced by at least 60.43% for the MI paradigm, while at most a reduction of 17.31% can be achieved for the MR/WG paradigm. Conclusions Data collected using the MI paradigm show better generalization across subjects.

• Publisher: BioMed Central,BioMed Central Ltd,Springer Nature B.V,BMC
• Subject: Accuracy / Biomaterials / Biomedical Engineering and Bioengineering / Biomedical Engineering/Biotechnology / Biotechnology / Brain / Brain-Computer Interfaces / Calibration / Clinical trials / Common spatial pattern / Datasets / Deep learning / Disabilities / EEG / Electrodes / Electroencephalography / Engineering / Euclidean space / Human-computer interaction / Humans / Hybrid brain–computer interfaces / Imagery / Interfaces / Learning / Machine Learning / Mental task performance / Performance evaluation / Probabilistic fusion / Probability / Random variables / Reduction / Signal Processing, Computer-Assisted / Support vector machines / Technology application / Template matching / Time Factors / Training / Transfer learning / Wavelet decomposition