Abstract
A potential limitation of motor imagery (MI) based brain-computer interface (BCI) (MI-BCI) is that it usually requires a relatively long time to record sufficient electroencephalogram (EEG) data for robust feature extraction and classification. Moreover, due to the non-stationarities in EEG signals, the offline training model has poor adaptability and classification ability in cross-session or sample-wise online testing. Methods: To address the problems, we propose a model updating scheme with adaptive and fast operation. Based on the Common Spatial Pattern (CSP), we propose an online and fast generalized eigendecomposition method by Recursive Least Squares updates of the CSP filter coefficients (RLS-CSP), which allows incremental training for CSP spatial filters. Additionally, we present an Incremental Self-training Classification algorithm based on Density Clustering (ISCDC) to select high-confidence samples to update spatial filters and classifier, and classify at the same time. Results: We conducted extensive experiments to validate the efficiency of the proposed adaptive CSP and classifier on the BCI III_IVa and BCI III_V data sets. Experimental results demonstrate that RLS-CSP outperforms significantly in a small sample setting (SSS), and ISCDC has great adaptability in cross-session and non-stationary EEG signals. The results indicate that our proposed methods are feasible to improve the real-time performance of online BCI system.
Highlights
Brain-Computer Interface (BCI) is a human-computer interaction technology
The Common Spatial Pattern (CSP), is a widely used a time-spatial feature extraction method in motor imagery (MI)-BCI system, which is effective to extract the frequency band variances as features [6][7]by conducting spatial filtering on multichannel simultaneously [8]. small sample setting: CSP is highly dependent on sample-based covariance [2], and is very sensitive to noise
One of the ways to keep CSP adaptive is to update the initial spatial filters with high-confidence testing samples[23,24,25].To evaluate the confidence, this paper proposes an Incremental Self-training Classification method based on Density Clustering (ISCDC) by combining Density Peaks Clustering (DPC) and Density-Based Spatial Clustering of Applications with Noise (DBSCAN)
Summary
Brain-Computer Interface (BCI) is a human-computer interaction technology. It does not rely on the peripheral nerve and muscle system and aims to provide a bridge between human brain and external devices [1][2]. It has demonstrated broad application prospects in the rehabilitation of disabled people and auxiliary control of healthy people [3]. Effective characterization of ERD/ERS phenomenon is of vital importance to a MI-BCI system. The Common Spatial Pattern (CSP), is a widely used a time-spatial feature extraction method in MI-BCI system, which is effective to extract the frequency band variances as features [6][7]by conducting spatial filtering on multichannel simultaneously [8]. small sample setting: CSP is highly dependent on sample-based covariance [2], and is very sensitive to noise
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