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Motor imagery classification is known to be highly user dependent. Subspace alignment has been somewhat successful in allowing for unsupervised transfer from one training user to a new user. In this paper we develop a method to weight contributions from subspace alignment to multiple training users to give improved unsupervised transfer performance on the new test user. Ablation analyses show that both the subspace alignment and weighting are critical for improved performance. We also discuss how weighting uses the labels of the training users to better interpret subspace alignment. 

Brain-computer interface (BCI) systems read and infer the user brain activity directly from the brain providing a means of communication and rehabilitation for patients in need. However, brain signals are known to be non-stationary and existing systems are not reliable and robust enough to be taken outside of the laboratory. Often times long calibration and recalibration of the system is required which can be tiresome and frustrating to the user. In this study, we compare the method of common spatial patterns (CSP) with two of its variants, namely, the canonical correlation analysis approach to common spatial patterns (CCACSP) and the common spatio-spectral patterns (CSSP) in detecting the motor imagery signal when trained on calibration data with sham feedback and tested in online control. We show that the motor imagery performance is significantly better with CSSP and CCACSP compared to CSP and hence, able to provide a more reliable transfer of the classifier from calibration to online control. 

Brain-computer interface (BCI) systems are proposed as a means of communication for locked-in patients. One common BCI paradigm is motor imagery in which the user controls a BCI by imagining movements of different body parts. It is known that imagining different body parts results in event-related desynchronization (ERD) in various frequency bands. Existing methods such as common spatial patterns (CSP) and its refinement filterbank common spatial patterns (FB-CSP) aim at finding features that are informative for classification of the motor imagery class. Our proposed method is a temporally adaptive common spatial patterns implementation of the commonly used filter-bank common spatial patterns method using convolutional neural networks; hence it is called TA-CSPNN. With this method we aim to: (1) make the feature extraction and classification end-to-end, (2) base it on the way CSP/FBCSP extracts relevant features, and finally, (3) reduce the number of trainable parameters compared to existing deep learning methods to improve generalizability in noisy data such as EEG. More importantly, we show that this reduction in parameters does not affect performance and in fact the trained network generalizes better for data from some participants. We show our results on two datasets, one publicly available from BCI Competition IV, dataset 2a and another in-house motor imagery dataset.