Motor imagery (MI) becomes a powerful rehabilitation tool, particularly when combined with brain-computer interfaces (BCI). Therefore, methods to improve MI accuracy are a trending topic in the BCI field. Here, we examined the effects of transcranial direct current stimulation (tDCS) and MI priming on the MI signature in an EEG-based MI-BCI triple-blind study. Thirty healthy younger adults participated in this study and were designated to one of two priming groups: A bimanual tracking task and a MI task as primers for MI-BCI. During the performance of the primer task, participants received anodal and sham tDCS in two randomized sessions with a one-week wash-out period between sessions. Subsequently, participants performed an EEG-driven BCI-MI task. EEG time-frequency analyses revealed that desynchronization of the Beta Region precedes desynchronization in the Alpha Region, implying that the Beta frequency band might be best-suited to extract MI signatures as it could lead to faster MI-BCI. Contrary to our hypotheses, no effect of tDCS or priming task on EEG activity during the BCI-MI task was found. Future research should carefully consider the added value of tDCS and priming tasks BCI performance improvement. Electric field modeling studies and high-definition tDCS motor cortex stimulation might be promising avenues.
The classification performance of endogenous electroencephalogram (EEG) brain-computer interfaces (BCIs) can be improved by hybridizing the paradigm through the use of commands from multiple paradigms. Hybrid paradigms using motor imagery (MI) and speech imagery (SI) have shown promise, but there is a lack of research into: i) their effectiveness when compared to pure MI and SI for multiclass problems, and ii) automated command selection. This study investigates multiclass MI and SI hybrid paradigms and compares the results to those obtained using pure MI and SI. Performance was assessed using F1 score and accuracy. The performances of all possible hybrid paradigm designs were assessed. The analysis indicated that hybridization does not always guarantee improved performance when compared to the pure paradigms, and there is inter-subject variation in the best paradigm. This confirmed the need for automated subject-specific hybrid paradigm designs. An automated hybrid paradigm selection technique using successive halving (SH) for expedited computational times was developed and results were compared to those obtained using a standard grid search. The SH approach resulted in an improvement in F1 score of 21.09% and 36.86% compared to MI and SI and led to a reduction in computational times of 82.80% compared to grid search.
IntroductionRestorative Brain-Computer Interfaces (BCIs) provide an alternative non-muscular channel for stroke patients lacking volitional movement by enhancing sensorimotor rhythm modulation through motor imagery (MI). MI practice can be augmented with embodied feedback via virtual reality (VR). However, the clinical impact of embodied VR-BCI training remains under-explored.MethodsThis study examines the effects of embodied VR-BCI training on brain activity patterns (measured through EEG and fMRI) and clinical outcomes (assessed by the Fugl-Meyer Assessment, FMA) in four chronic stroke patients. Over a 3-week MI-BCI intervention, patients performed a bimanual rowing task (NeuRow) in VR. EEG data were used to extract Event-related desynchronization (ERD) and lateralization indices, while fMRI data focused on the primary motor (M1) and supplementary motor (SMA) regions of interest.ResultsResults indicated that all patients significantly induced ERD power, though the affected side exhibited reduced ERD compared to baseline during contralateral MI. Post-intervention, significant ERD differences from both hemispheres were observed, with decreased ERD correlating with no clinical improvement. Patients showing decreased ERD lateralization had no FMA score improvement. Activity within ipsilesional M1 and SMA correlated with FMA scores.ConclusionsThe findings suggest a relationship between brain activity and clinical outcomes, highlighting that increased ERD lateralization is associated with clinical improvement.
We address the recognized person-to-person Brain-Computer Interface (BCI) calibration problem and tackle session-dependency through the use of unsupervised canonical polyadic (CP) tensor decomposition. For a motor imagery task, the approach reveals universal structures within EEG data, common between subjects and prominent for a certain task. Further, we develop a novel similarity measure that includes weighting of the decomposition's factor matrices, and argue that it is more representative than what has previously been presented in literature. The proposed similarity measure shows potential in a BCI classification task, i.e. drowsiness during simulated driving (average Pearson correlation of 0.6).
Steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) enable people to control external devices by focusing on repetitive visual stimuli (RVS). However, RVS can be visually annoying and cause user discomfort. To address this issue, this study proposes subtle flickering RVS to enhance visual comfort with SSVEP-based BCIs. Previous research on mitigating this discomfort has explored various stimuli characteristics, such as frequencies, colors, and patterns, but some studies are contradicting and have their limitations. This study investigates whether modulation depth (MD) can reduce user discomfort when using LCD-displayed stimuli. Specifically, MDs ranging from 1 down to 0.0125 and their effect on SSVEP response and BCI performance have been computed. Results demonstrate that stimuli with a 0.5 MD achieve accuracy and information-transfer rate (ITR) performance of 80.06% and 41.6 bpm which are at par with a baseline BCI having stimuli with a MD of 1, having 79.98% accuracy and 41.08 bpm ITR. These findings demonstrate that a lower flicker annoyance may be obtained with stimuli having a MD of 0.5. By reducing user discomfort, the proposed subtle flickering RVS can improve user experience without significant degradation in performance, potentially increasing the adoption and usability of SSVEP-based BCIs.
Brain-computer interfaces based on electroencephalography (EEG) often exhibit unreliable performance in the classification of motor tasks. Recent research has shown that EEG source imaging (ESI) has the potential to outperform sensor domain approaches in various movement decoding tasks. However, ESI research to date has predominantly focused on the adult population, so its performance in youth with disabilities is unknown. In this study, we compared the offline classification performance of two ESI approaches (with and without modeling white matter conductivity anisotropy) to that of a sensor domain approach in the classification of left- versus right-hand movement execution and imagery tasks. Magnetic resonance images (MRI) were acquired from nine pediatric participants with brain lesions. Subsequently, cortical activity was recorded from 64 channels. MRI data were used to estimate participant-specific EEG sources. Various feature extraction and classification approaches were investigated in both sensor and source domains. Generally, ESI classification performance did not exceed chance levels and was statistically equivalent to sensor approaches except for isolated participants. However, ESI offered +9.61% improvement over the sensor domain (p = 0.031) in decoding motor execution in a participant with unilateral ventriculomegaly. Future research ought to delineate the specific task and participant characteristics which warrant the source domain approach.
Amyotrophic lateral sclerosis (ALS) severely impairs patients' ability to communicate, often leading to a decline in their quality of life within a few years of diagnosis. The P300 speller brain-computer interface (BCI) offers an alternative communication method by interpreting a subject's EEG response to flashing characters presented on a grid interface. This paper addresses the common speed limitations encountered in training efficient P300-based multi-subject classifiers by introducing innovative 'across-subject' classifiers. We leverage a combination of the second-generation Generative Pre-Trained Transformer (GPT2) and Dijkstra's algorithm to optimize stimuli and suggest word completion choices based on subjects' typing history. Additionally, we employ a multi-layered smoothing technique to accommodate out-of-vocabulary (OOV) words. Through extensive simulations employing random sampling of EEG data from multiple subjects, we demonstrate significant speed enhancements in typing passages containing rare and OOV words. These optimizations result in approximately $10\% $10% improvement in character-level typing speed and up to $40\% $40% improvement in multi-word prediction. We demonstrate that augmenting standard row/column highlighting techniques with layered word prediction yields close-to-optimal performance. Furthermore, we explore both 'within-subject' and 'across-subject' training techniques, showing that speed improvements are consistent across both approaches.
Brain-computer interfaces (BCI) enable movement-independent information transfer from humans to computers. Decoding imagined 3D objects from electroencephalography (EEG) may improve design ideation in engineering design or image reconstruction from EEG for application in brain-computer interfaces, neuro-prosthetics, and cognitive neuroscience research. Object-imagery decoding studies, to date, predominantly employ functional magnetic resonance imaging (fMRI) and do not provide real-time feedback. We present four linked studies in a study series to investigate: (1) whether five imagined 3D primitive objects (sphere, cone, pyramid, cylinder, and cube) could be decoded from EEG; and (2) the influence of real-time feedback on decoding accuracy. Studies 1 (N = 10) and 2 (N = 3) involved a single-session and a multi-session design, respectively, without real-time feedback. Studies 3 (N = 2) and 4 (N = 4) involved multiple sessions, without and with real-time feedback. The four studies involved 69 sessions in total of which 26 sessions were online with real-time feedback (15,480 trials for offline and at least 6,840 trials for online sessions in total). We demonstrate that decoding accuracy over multiple sessions improves significantly with biased feedback (p = 0.004), compared to performance without feedback. This is the first study to show the effect of real-time feedback on the performance of primitive object-imagery BCI.
