Individually Adapted Imagery Improves Brain-Computer Interface Performance in End-Users with Disability

Abstract

Brain-computer interfaces (BCIs) translate oscillatory electroencephalogram (EEG) patterns into action. Different mental activities modulate spontaneous EEG rhythms in various ways. Non-stationarity and inherent variability of EEG signals, however, make reliable recognition of modulated EEG patterns challenging. Able-bodied individuals who use a BCI for the first time achieve - on average - binary classification performance of about 75%. Performance in users with central nervous system (CNS) tissue damage is typically lower. User training generally enhances reliability of EEG pattern generation and thus also robustness of pattern recognition. In this study, we investigated the impact of mental tasks on binary classification performance in BCI users with central nervous system (CNS) tissue damage such as persons with stroke or spinal cord injury (SCI). Motor imagery (MI), that is the kinesthetic imagination of movement (e.g. squeezing a rubber ball with the right hand), is the "gold standard" and mainly used to modulate EEG patterns. Based on our recent results in able-bodied users, we hypothesized that pair-wise combination of "brain-teaser" (e.g. mental subtraction and mental word association) and "dynamic imagery" (e.g. hand and feet MI) tasks significantly increases classification performance of induced EEG patterns in the selected end-user group. Within-day (How stable is the classification within a day?) and between-day (How well does a model trained on day one perform on unseen data of day two?) analysis of variability of mental task pair classification in nine individuals confirmed the hypothesis. We found that the use of the classical MI task pair hand vs. feed leads to significantly lower classification accuracy - in average up to 15% less - in most users with stroke or SCI. User-specific selection of task pairs was again essential to enhance performance. We expect that the gained evidence will significantly contribute to make imagery-based BCI technology become accessible to a larger population of users including individuals with special needs due to CNS damage.

Single-trial effective brain connectivity patterns enhance discriminability of mental imagery tasks

“…with findings of several previous studies [29,48,49]. However, as per our knowledge, this is the first study to compare the CI, MI, and the combined tasks pairs for connectivity features.…”

“…Details of the participants are summarized in Table A1 of Appendix A. For further exploration, readers are referred to the original study [29]. The mental tasks include five different classes: word generation imagery (class 1), mental subtraction imagery (class 2), spatial navigation imagery (class 3), right hand MI (class 4), and both feet MI (class 5).…”

MOABB: trustworthy algorithm benchmarking for BCIs

“…Figure 2 shows the difference between CSP and the channel log-variance and tangent space methods, as these are all wellknown approaches and have been compared against each other often in the past. Based on this meta-analysis, CSP reliably out-performs channel log-variances across datasets -however, there are datasets such as [16] and [26] in which the opposite trend is shown. Similarly, while the tangent space projection method normally out-performs CSP, that is also not true for half of the sampled datasets.…”

Heading for new shores! Overcoming pitfalls in BCI design

“…motor imagery or spatial navigation) significantly enhance BCI performance in healthy [92-93] as well as in users with disability [94-95]. In the latter case binary classification accuracy was up to 15% higher when compared to a classical motor imagery task combination [94]. These results again emphasize the need to consider the different components in the BCI feedback loop and their interplay.…”