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Abstract
The usability of EEG-based visual brain–computer interfaces (BCIs) based on event-related potentials (ERPs) benefits from reducing the calibration time before BCI operation. Linear decoding models, such as the spatiotemporal beamformer model, yield state-of-the-art accuracy. Although the training time of this model is generally low, it can require a substantial amount of training data to reach functional performance. Hence, BCI calibration sessions should be sufficiently long to provide enough training data. This work introduces two regularized estimators for the beamformer weights. The first estimator uses cross-validated L2-regularization. The second estimator exploits prior information about the structure of the EEG by assuming Kronecker–Toeplitz-structured covariance. The performances of these estimators are validated and compared with the original spatiotemporal beamformer and a Riemannian-geometry-based decoder using a BCI dataset with P300-paradigm recordings for 21 subjects. Our results show that the introduced estimators are well-conditioned in the presence of limited training data and improve ERP classification accuracy for unseen data. Additionally, we show that structured regularization results in lower training times and memory usage, and a more interpretable classification model.
Highlights
Brain–computer interfaces (BCIs) establish a direct communication pathway between the brain and an external device [1]
As a response to these issues, we introduce a regularization method that exploits prior knowledge about the spatiotemporal nature of the EEG signal to improve the accuracy for settings with low data availability and to speed up the classifier training time, thereby considerably reducing memory usage
Because the covariance might have changed for unseen data, the shrinkage estimate obtained with leave-one-out cross-validation (LOOCV) is probably still an underestimation of the optimal—but unknown—shrinkage coefficient that would yield the best classification accuracy for the unseen data
Summary
Brain–computer interfaces (BCIs) establish a direct communication pathway between the brain and an external device [1]. BCIs can be implemented in multiple ways, using non-invasive recording techniques such as electroencephalography (EEG) [4], magnetoencephalography (MEG) [5], functional nearinfrared spectroscopy (fNIRS) [6], and optically pumped magnetometers (OPM MEG) [7], or semi-invasive and invasive methods such as electrocorticography (ECoG) [8] or microelectrode arrays [9], which require surgery to implant a recording device. Among the non-invasive options, EEG is the most cost-effective and practical as it is not limited to the same controlled settings as MEG and OPM MEG. A popular class of BCI paradigms relies on the evocation of event-related potentials (ERPs) in the brain in response to visual, auditory, or tactile stimulation, given their low decoding cost and generally short calibration time before usage [11,12]. The P300 paradigm has been used extensively in BCI development and is easy to set up [13–16]
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