An Original Algorithm for Classifying Premotor Potentials in Electroencephalogram Signal for Neurorehabilitation Using a Closed-Loop Brain–Computer Interface

Over the past decades, brain–computer interfaces (BCIs) have been rapidly evolving. A BCI is a system that records brain activity signals using electrophysiological methods and then processes these signals to generate control commands. The most challenging aspect of BCIs is the nonstationary nature of brain signals, which makes it difficult to achieve stable and accurate decoding. Therefore, developing robust methods for processing and classifying EEG signals to extract control commands is a critical research area. A related challenge is the low signal-to-noise ratio in EEG data, especially when target patterns are weak or the data is labeled inaccurately. This paper presents the results of an evaluation of an approach combining feature extraction and data augmentation techniques to address the aforementioned challenges applied to the classification of premotor potentials. The approach is based on the application of linear discriminant analysis (LDA) to sequentially extract informative components in the frequency and time domains For the first time, the applicability of this algorithm to EEG containing premotor patterns of real movements is demonstrated. Features of different nature (spectral power, Hjorth parameters, interchannel correlations) were tested and compared with each other and a traditional approach based on common spatial patterns and a linear classifier. It is shown that transformations in the frequency domain alone improve accuracy from 63.9 $$\%$$ in the traditional approach to 77.5 $$\%$$ on a dataset of 16 experiments on different subjects. With additional transformation in the time domain, accuracy increases to 98.8 $$\%$$ . On average, across different model configurations, a segment length of 500 ms is the most optimal. Two approaches were developed and tested to achieve algorithm universality across subjects: universal transformations in frequency domain trained on data from all subjects and without this step at all. It is shown that accuracies of up to 98.3 $$\%$$ can be achieved with such approaches. A discussion of optimal frequency bands, segment lengths, and features is provided. Thus, data from different subjects can be effectively classified by a common model, which is rare in global research and is usually accompanied by a number of assumptions, cumbersome models, and inferior accuracy. Thus, in addition to the achieved accuracy enhancement, the proposed algorithm exhibits robustness to transient noise and artifacts through signal segmentation into short epochs. It also effectively addresses the critical task of extracting informative signal components in scenarios with potentially imprecise expert annotations. Finally, it can be adapted to mitigate the need for subject-specific calibration. These attributes render the proposed algorithm suitable for real-time applications, including closed-loop BCIs for addressing the pressing challenge of neurorehabilitation.