Brain-Computer Interfaces (BCIs) offer direct communication between the brain and external devices, holding immense potential across various applications. This study focuses on Motor Imagery-based BCIs (MI-BCI), decod- ing neural patterns associated with mentally rehearsed motor actions. Despite their promise, BCIs face challenges in real-world applications, primarily in reliability and complexity. While classification accuracy is a standard metric for BCI perfor- mance, the literature often overlooks real-time responsiveness. Many studies report classification outcomes offline, disregarding the prompt translation of EEG signals into actions. The acceptable delay from EEG signal to action should not exceed 1 s; however, numerous studies employ time-windows exceeding 4 s, affecting user control perception. This article aims to compare the trade-off between time- window length and classification accuracy in MI-BCI, using three linear classifiers (LDA, MLP, SVM). Participants include stroke patients and subjects from the BCI IVa dataset. Results demonstrate time-frequency plots indicating MI-related EEG changes, revealing a trade-off between accuracy and responsiveness. Our find- ings underscores the importance of addressing real-time responsiveness in BCI evaluations, proposing a balance for practical system utility. In conclusion, this study enhances our understanding of the delicate balance needed for optimal real- world application of MI-BCIs, emphasizing the trade-off between accuracy and responsiveness.
