Abstract
AbstractTo enforce a widespread use of efficient and easy to use brain-computer interfaces (BCIs), the inter-subject robustness should be increased and the number of electrodes should be reduced. These two key issues are addressed in this contribution, proposing a novel method to identify subject-specific time-frequency characteristics with a minimal number of electrodes. In this method, two alternative criteria, time-frequency discrimination factor (TFDF) andFscore, are proposed to evaluate the discriminative power of time-frequency regions. Distinct from classical measures (e.g., Fisher criterion,r2coefficient), the TFDF is based on the neurophysiologic phenomena, on which the motor imagery BCI paradigm relies, rather than only from statistics.Fscore is based on the popularFisher’sdiscriminant and purely data driven; however, it differs from traditional measures since it provides a simple and effective measure for quantifying the discriminative power of a multi-dimensional feature vector. The proposed method is tested on BCI competition IV datasets IIa and IIb for discriminating right and left hand motor imagery. Compared to state-of-the-art methods, our method based on both criteria led to comparable or even better classification results, while using fewer electrodes (i.e., only two bipolar channels, C3 and C4). This work indicates that time-frequency optimization can not only improve the classification performance but also contribute to reducing the number of electrodes required in motor imagery BCIs.
Highlights
After several decades of development, current braincomputer interface (BCI) systems can be driven based on various types of brain signals obtained by techniques such as electroencephalography (EEG) [1], functional magnetic resonance imaging [2], nearinfrared spectroscopy (NIRS) [3], etc
These results show that (1) the estimated time-frequency region of interest (ROI) vary among different subjects, (2) even for the same subject, the estimated ROIs vary among different training sessions, and (3) the two criteria picked out different ROIs for the same training session
Even for the same subject, the timing and frequency of event-related desynchronization (ERD)/ERS may shift across sessions [12], which leads to the intra-subject difference in the estimation of ROIs between sessions
Summary
After several decades of development, current braincomputer interface (BCI) systems can be driven based on various types of brain signals obtained by techniques such as electroencephalography (EEG) [1], functional magnetic resonance imaging (fMRI) [2], nearinfrared spectroscopy (NIRS) [3], etc. One popular solution is to apply a data-driven spatial filtering technique, such as common spatial pattern (CSP) [6], on multi-channel (e.g. 64 or 128 channels) monopolar recording EEG data, which can improve the SNR of signal and extract discriminative features from the mixture of signals, especially for two-class discriminations [7]. Such a multi-channel setting inevitably reduces the portability and practicability of BCIs, which represents a main drawback for end users
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