Accurate binary classification of electroencephalography (EEG) signals is a challenging task for the development of motor imagery (MI) brain-computer interface (BCI) systems. In this study, two sliding window techniques are proposed to enhance the binary classification of MI. The first one calculates the longest consecutive repetition (LCR) of the sequence of prediction of all the sliding windows and is named SW-LCR. The second calculates the mode of the sequence of prediction of all the sliding windows and is named SW-Mode. Common spatial pattern (CSP) is used for extracting features with linear discriminant analysis (LDA) used for classification of each time window. Both the SW-LCR and SW-Mode are applied on publicly available BCI Competition IV-2a data set of healthy individuals and on a stroke patients' data set. Compared with the existing state of the art, the SW-LCR performed better in the case of healthy individuals and SW-Mode performed better on stroke patients' data set for left- versus right-hand MI with lower standard deviation. For both the data sets, the classification accuracy (CA) was approximately 80% and kappa (κ) was 0.6. The results show that the sliding window-based prediction of MI using SW-LCR and SW-Mode is robust against intertrial and intersession inconsistencies in the time of activation within a trial and thus can lead to a reliable performance in a neurorehabilitative BCI setting.

Brain-computer interface (BCI) performance is often impacted due to the inherent non-stationarity in the recorded EEG signals coupled with a high variability across subjects. This study proposes a novel method using Logistic Regression with Tangent Space-based Transfer Learning (LR-TSTL) for motor imagery (MI)-based BCI classification problems. The single-trial covariance matrix (CM) features computed from the EEG signals are transformed into a Riemannian geometry frame and tangent space features are computed by considering the lower triangular matrix. These are then further classified using the logistic regression model to improve classification accuracy. The performance of LR-TSTL is tested on healthy subjects’ dataset as well as on stroke patients’ dataset. As compared to existing within-subject learning approaches the proposed method gave an equivalent or better performance in terms of average classification accuracy (78.95±11.68%), while applied as leave-one-out cross-subject learning for healthy subjects. Interestingly, for the patient dataset LR-TSTL significantly (p<0.05) outperformed the current benchmark performance by achieving an average classification accuracy of 81.75±6.88%. The results show that the proposed method for cross-subject learning has the potential to realize the next generation of calibration-free BCI technologies with enhanced practical usability especially in the case of neurorehabilitative BCI designs for stroke patients.B.

Objective. Magnetoencephalography (MEG) based brain–computer interface (BCI) involves a large number of sensors allowing better spatiotemporal resolution for assessing brain activity patterns. There have been many efforts to develop BCI using MEG with high accuracy, though an increase in the number of channels (NoC) means an increase in computational complexity. However, not all sensors necessarily contribute significantly to an increase in classification accuracy (CA) and specifically in the case of MEG-based BCI no channel selection methodology has been performed. Therefore, this study investigates the effect of channel selection on the performance of MEG-based BCI. Approach. MEG data were recorded for two sessions from 15 healthy participants performing motor imagery, cognitive imagery and a mixed imagery task pair using a unique paradigm. Performance of four state-of-the-art channel selection methods (i.e. Class-Correlation, ReliefF, Random Forest, and Infinite Latent Feature Selection were applied across six binary tasks in three different frequency bands) were evaluated in this study on two state-of-the-art features, i.e. bandpower and common spatial pattern (CSP). Main results. All four methods provided a statistically significant increase in CA compared to a baseline method using all gradiometer sensors, i.e. 204 channels with band-power features from alpha (8–12 Hz), beta (13–30 Hz), or broadband (α + β) (8–30 Hz). It is also observed that the alpha frequency band performed better than the beta and broadband frequency bands. The performance of the beta band gave the lowest CA compared with the other two bands. Channel selection improved accuracy irrespective of feature types. Moreover, all the methods reduced the NoC significantly, from 204 to a range of 1–25, using bandpower as a feature and from 15 to 105 for CSP. The optimal channel number also varied not only in each session but also for each participant. Reducing the NoC will help to decrease the computational cost and maintain numerical stability in cases of low trial numbers. Significance. The study showed significant improvement in performance of MEG-BCI with channel selection irrespective of feature type and hence can be successfully applied for BCI applications.

Imagination of movement can be used as a control method for a brain-computer interface (BCI) allowing communication for the physically impaired. Visual feedback within such a closed loop system excludes those with visual problems and hence there is a need for alternative sensory feedback pathways. In the context of substituting the visual channel for the auditory channel, this study aims to add to the limited evidence that it is possible to substitute visual feedback for its auditory equivalent and assess the impact this has on BCI performance. Secondly, the study aims to determine for the first time if the type of auditory feedback method influences motor imagery performance significantly. Auditory feedback is presented using a stepped approach of single (mono), double (stereo), and multiple (vector base amplitude panning as an audio game) loudspeaker arrangements. Visual feedback involves a ball-basket paradigm and a spaceship game. Each session consists of either auditory or visual feedback only with runs of each type of feedback presentation method applied in each session. Results from seven subjects across five sessions of each feedback type (visual, auditory) (10 sessions in total) show that auditory feedback is a suitable substitute for the visual equivalent and that there are no statistical differences in the type of auditory feedback presented across five sessions.