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Brain-computer interfaces (BCIs) are promising tools for motor neurorehabilitation. Achieving a balance between classification accuracy and system responsiveness is crucial for real-time applications. This study aimed to assess how the duration of time windows affects performance, specifically classification accuracy and the false positive rate, to optimize the temporal parameters of MI-BCI systems. We investigated the impact of time window duration on classification accuracy and false positive rate, employing Linear Discriminant Analysis (LDA), Multilayer Perceptron (MLP), and Support Vector Machine (SVM) on data acquired from six post-stroke patients and on the external BCI IVa dataset. EEG signals were recorded and processed using the Common Spatial Patterns (CSP) algorithm for feature extraction. Our results indicate that longer time windows generally enhance classification accuracy and reduce false positives across all classifiers, with LDA performing the best. However, to maintain the real-time responsiveness, crucial for practical applications, a balance must be struck. The results suggest an optimal time window of 1-2 s, offering a trade-off between classification performance and excessive delay to guarantee the system responsiveness. These findings underscore the importance of temporal optimization in MI-BCI systems to improve usability in real rehabilitation scenarios.

Background and Objective: The input data distributions of EEG-based BCI systems can change during intra-session transitions due to nonstationarity caused by features covariate shifts, thus compromising BCI performance. We aimed to identify the most robust spatial filtering approach, among most used methods, testing them on calibration dataset, and test dataset recorded 30 min afterwards. In addition, we also investigated if their performance improved after application of Stationary Subspace Analysis (SSA)., Methods: We have recorded, in 17 healthy subjects, the calibration set at the beginning of the upper limb motor imagery BCI experiment and testing set separately 30 min afterwards. Both the calibration and test data were pre-processed and the BCI models were produced by using several spatial filtering approaches on the calibration set. Those models were subsequently evaluated on a test set. The differences between the accuracy estimated by cross-validation on the calibration dataset and the accuracy on the test dataset were investigated. The same procedure was performed with, and without SSA pre-processing step., Results: A significant reduction in accuracy on the test dataset was observed for CSP, SPoC and SpecRCSP approaches. For SLap and SpecCSP only a slight decreasing trend was observed, while FBCSP and FBCSPT largely maintained moderately high median accuracy >70%. In the case of application of SSA pre-processing, the differences between accuracy observed on calibration and test dataset were reduced. In addition, accuracy values both on calibration and test set were slightly higher in case of SSA pre-processing and also in this case FBCSP and FBCSPT presented slightly better performance compared to other methods., Conclusion: The intrinsic signal nonstationarity characteristics, caused by covariance shifts of power features, reduced the accuracy of BCI model, therefore, suggesting that this evaluation framework should be considered for testing and simulating real life performance. FBCSP and FBSCPT approaches showed to be more robust to feature covariance shift. SSA can improve the models performance and reduce accuracy decline from calibration to test set., Competing Interests: Declaration of Competing Interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2020. Published by Elsevier B.V.)

The study reports the performance of Parkinson's disease (PD) patients to operate Motor-Imagery based Brain-Computer Interface (MI-BCI) and compares three selected pre-processing and classification approaches. The experiment was conducted on 7 PD patients who performed a total of 14 MI-BCI sessions targeting lower extremities. EEG was recorded during the initial calibration phase of each session, and the specific BCI models were produced by using Spectrally weighted Common Spatial Patterns (SpecCSP), Source Power Comodulation (SPoC) and Filter-Bank Common Spatial Patterns (FBCSP) methods. The results showed that FBCSP outperformed SPoC in terms of accuracy, and both SPoC and SpecCSP in terms of the false-positive ratio. The study also demonstrates that PD patients were capable of operating MI-BCI, although with lower accuracy.

The premotor cortex is one of the fundamental structures composing the neural networks of the human brain. It is implicated in many behaviors and cognitive tasks, ranging from movement to attention and eye-related activity. Therefore, neural circuits that are related to premotor cortex have been studied to clarify their connectivity and/or role in different tasks. In the present work, we aimed to investigate the propagation of the neural activity evoked in the dorsal premotor cortex using transcranial magnetic stimulation/electroencephalography (TMS/EEG). Toward this end, interest was focused on the neural dynamics elicited in long-ranging temporal and spatial networks. Twelve healthy volunteers underwent a single-pulse TMS protocol in a resting condition with eyes closed, and the evoked activity, measured by EEG, was compared to a sham condition in a time window ranging from 45 ms to about 200 ms after TMS. Spatial and temporal investigations were carried out with sLORETA. TMS was found to induce propagation of neural activity mainly in the contralateral sensorimotor and frontal cortices, at about 130 ms after delivery of the stimulus. Different types of analyses showed propagated activity also in posterior, mainly visual, regions, in a time window between 70 and 130 ms. Finally, a likely "rebounding" activation of the sensorimotor and frontal regions, was observed in various time ranges. Taken together, the present findings further characterize the neural circuits that are driven by dorsal premotor cortex activation in healthy humans.

The present study investigates a Brain-Computer Interface (BCI) spelling procedure based on the P300 evoked potential. It uses a small matrix of words arranged in a tree-shaped organization ("multimenu"), and allows the user to build phrases one word at a time, instead of letter by letter. Experiments were performed in two sessions on a group of seven healthy volunteers. In the former, the "multimenu" was tested with a total of 60 choices: 30 "externally-imposed" selections and 30 "free-choice" selections. In the latter, 3 × 3 matrices were compared with 6 × 6 matrices. Each matrix was composed of letters or words, for a total of four matrices. Differences in classifier accuracy, bit rate and amplitude of the evoked P300 were evaluated. Average accuracy in all subjects was 87% with no differences between the selection methods. The 3 × 3 "multimenu" obtained the same level of classifier accuracy as the 6 × 6 matrices, even with a significantly lower amplitude of the P300. Bit rate was increased when using the 3 × 3 matrices compared to the 6 × 6 ones. The "multimenu" system was equally effective, but faster than conventional, letter-based matrices. By improving the speed of communication, this method can be of help to patients with severe difficulties in communication.

Humans perceive continuous speech through interruptions or brief noise bursts cancelling entire phonemes. This robust phenomenon has been classically associated with mechanisms of perceptual restoration. In parallel, recent experimental evidence suggests that the motor system may actively participate in speech perception, even contributing to phoneme discrimination. In the present study we intended to verify if the motor system has a specific role in speech perceptual restoration as well. To this aim we recorded tongue corticospinal excitability during phoneme expectation induced by contextual information. Results showed that phoneme expectation determines an involvement of the individual's motor system specifically implicated in the production of the attended phoneme, exactly as it happens during actual listening of that phoneme, suggesting the presence of a speech imagery-like process. Very interestingly, this motoric phoneme expectation is also modulated by subtle coarticulation cues of which the listener is not consciously aware. Present data indicate that the rehearsal of a specific phoneme requires the contribution of the motor system exactly as it happens during the rehearsal of actions executed by the limbs, and that this process is abolished when an incongruent phonemic cue is presented, as similarly occurs during observation of anomalous hand actions. We propose that altogether these effects indicate that during speech listening an attentional-like mechanism driven by the motor system, based on a feed-forward anticipatory mechanism constantly verifying incoming information, is working allowing perceptual restoration., (Copyright © 2011 Elsevier Ltd. All rights reserved.)

Involvement of the ipsilateral hemisphere during planning of reaching movements is still matter of debate. While it has been demonstrated that the contralateral hemisphere is dominant in visuo-motor integration, involvement of the ipsilateral hemisphere has also been proposed. Furthermore, a dominant role for left posterior parietal cortex has been shown in this process, independently of the hand and visual field involved. In this study, the possible involvement of ipsilateral parieto-occipital cortex in planning of reaching movements was investigated by transcranial magnetic stimulation (TMS). TMS was applied on four points of the parietal and occipital cortex at 50% (Time 1), 75% (Time 2) and 90% (Time 3) of reaction time from a go-signal to hand movement. The only effect observed was an increase in reaction time when a region around the parieto-occipital junction was stimulated at Time 2. These results provide further support to the hypothesis that, in the posterior parietal cortex, planning of reaching movements also relies on the ipsilateral hemisphere, in addition to the contralateral or dominant one.