High inter- and intra-individual variation is a prominent characteristic of electroencephalography (EEG) signals and a significant inhibitor to the practical implementation of brain–computer interfaces (BCIs) outside of research laboratories. However, a few methods exist to assess EEG signal variability. Here, a novel multi-class intra-trial trajectory (MITT) analysis to study EEG variability for mental imagery BCIs is presented. The methods yield insight into different aspects of signal variation, specifically (i) inter-individual, (ii) inter-task, (iii) inter-trial, and (iv) intra-trial. A novel representation of the time evolution of EEG signals was developed. Task trials were segmented into short temporal windows and represented in a feature space derived from unsupervised clustering of trial covariance matrices. Using this representation, temporal trajectories through the feature space were constructed. Two metrics were defined to assess user performance based on these trajectories: (1) InterTaskDiff, based on time-varying distances between the mean trajectories of different tasks, and (2) InterTrialVar, which measured the inter-trial variation of the temporal trajectories along the feature dimensions. Analysis of three-class BCI data from 14 adolescents revealed both metrics correlated significantly with classification results. Further analysis of intra-trial trajectories suggested the existence of characteristic task- and user-specific temporal dynamics. The participant-specific insights provided by MITT analysis could be used to overcome EEG-variability challenges impeding practical implementation of BCIs by elucidating avenues to improve user training feedback or selection of user-optimal classifiers and hyperparameters.

Brain-computer interfaces (BCIs) have many potential applications for individuals with physical disabilities; however, their usage is limited due to unreliable performance. While users can improve performance via training, the effectiveness of current training approaches may be limited by inaccurate performance assessments and confusing feedback. Herein, we render recently proposed user performance assessments for mental imagery electroencephalography (EEG)-BCIs conducive to online deployment. The approach uses$K$-means clustering to segment the EEG signal space into pattern states and then models transitions between these states using Markov chains. A metric, taskDistinct , uses the Markov chain steady-state distributions to measure user ability to produce task-specific EEG patterns. The objective of this work was twofold: first, to assess the sensitivity of the adjusted metrics to performance variations throughout a session; and second, to examine the influence of the number of pattern states in the models on this sensitivity. To meet these objectives, we performed pseudo-online analyses where the taskDistinct metric was computed with various numbers of pattern states throughout simulated data collection sessions. Analysis revealed significant positive correlations between the adapted taskDistinct metric and other performance metrics. Additionally, the metric sensitivity to performance changes was not significantly affected by the number of pattern states. The results indicate that the adapted Markov chain-based metrics could be used for assessing performance in online user training for mental imagery EEG-BCIs.

Brain-computer interfaces (BCIs) are positioned to help individuals with physical disabilities, yet training data for these systems are time-consuming and expensive to collect. BCIs could thus benefit from cross-user data sharing, but as neurophysiological signals used in BCIs are personal health information, the protection of user privacy and data sovereignty will be of the utmost importance for end-user BCI applications. Federated learning is a novel privacy-preserving machine learning technique that decentralizes training, leaving end-user data on the device/institution of origin. In this work, we explored the feasibility of federated learning for BCIs, profiling convergence and performance for two federated learning algorithms (FedAvg and FedDC) on both identically and independently distributed (IID) and non-IID partitions of data. Federated learning for a 4-class motor imagery BCI decoding task was found to be feasible, although came at a cost of reduced performance (longer convergence rate and reduced accuracy). The FedDC algorithm, which introduces drift correction for heterogeneous data, well out-performed the FedAvg algorithm. Larger datasets with more subjects and data per subject would be beneficial for further investigations, and future work should explore federated transfer learning techniques for combating inter-subject data heterogeneity and improving global model performance.

Brain-computer interfaces (BCIs) provide communicative alternatives to those without functional speech. Covert speech (CS)-based BCIs enable communication simply by thinking of words and thus have intuitive appeal. However, an elusive barrier to their clinical translation is the collection of voluminous examples of high-quality CS signals, as iteratively rehearsing words for long durations is mentally fatiguing. Research on CS and speech perception (SP) identifies common spatiotemporal patterns in their respective electroencephalographic (EEG) signals, pointing towards shared encoding mechanisms. The goal of this study was to investigate whether a model that leverages the signal similarities between SP and CS can differentiate speech-related EEG signals online. Ten participants completed a dyadic protocol where in each trial, they listened to a randomly selected word and then subsequently mentally rehearsed the word. In the offline sessions, eight words were presented to participants. For the subsequent online sessions, the two most distinct words (most separable in terms of their EEG signals) were chosen to form a ternary classification problem (two words and rest). The model comprised a functional mapping derived from SP and CS signals of the same speech token (features are extracted via a Riemannian approach). An average ternary online accuracy of 75.3% (60% chance level) was achieved across participants, with individual accuracies as high as 93%. Moreover, we observed that the signal-to-noise ratio (SNR) of CS signals was enhanced by perception-covert modeling according to the level of high-frequency ([Formula: see text]-band) correspondence between CS and SP. These findings may lead to less burdensome data collection for training speech BCIs, which could eventually enhance the rate at which the vocabulary can grow.

Brain-computer interfaces (BCIs) can provide a means of communication to individuals with severe motor disorders, such as those presenting as locked-in. Many BCI paradigms rely on motor neural pathways, which are often impaired in these individuals. However, recent findings suggest that visuospatial function may remain intact. This study aimed to determine whether visuospatial imagery, a previously unexplored task, could be used to signify intent in an online electroencephalography (EEG)-based BCI. Eighteen typically developed participants imagined checkerboard arrow stimuli in four quadrants of the visual field in 5-s trials, while signals were collected using 16 dry electrodes over the visual cortex. In online blocks, participants received graded visual feedback based on their performance. An initial BCI pipeline (visuospatial imagery classifier I) attained a mean accuracy of [Formula: see text]% classifying rest against visuospatial imagery in online trials. This BCI pipeline was further improved using restriction to alpha band features (visuospatial imagery classifier II), resulting in a mean pseudo-online accuracy of [Formula: see text]%. Accuracies exceeded the threshold for practical BCIs in 12 participants. This study supports the use of visuospatial imagery as a real-time, binary EEG-BCI control paradigm.

The majority of proposed NIRS-BCIs has considered binary classification. Studies considering high-order classification problems have yielded average accuracies that are less than favorable for practical communication. Consequently, there is a paucity of evidence supporting online classification of more than two mental states using NIRS. We developed an online ternary NIRS-BCI that supports the verbal fluency task (VFT), Stroop task and rest. The system utilized two sessions dedicated solely to classifier training. Additionally, samples were collected prior to each period of online classification to update the classifier. Using a continuous-wave spectrometer, measurements were collected from the prefrontal and parietal cortices while 11 able-bodied adult participants were cued to perform one of the two cognitive tasks or rests. Each task was used to indicate the desire to select a particular letter on a scanning interface, while rest avoided selection. Classification was performed using 25 iteration of bagging with a linear discriminant base classifier. Classifiers were trained on 10-dimensional feature sets. The BCI's classification decision was provided as feedback. An average online classification accuracy of [Formula: see text]% was achieved, representing an ITR of [Formula: see text] bits/min. The results demonstrate that online communication can be achieved with a ternary NIRS-BCI that supports VFT, Stroop task and rest. Our findings encourage continued efforts to enhance the ITR of NIRS-BCIs.

Brain-computer interfaces (BCIs) for communication can be nonintuitive, often requiring the performance of hand motor imagery or some other conversation-irrelevant task. In this paper, electroencephalography (EEG) was used to develop two intuitive online BCIs based solely on covert speech. The goal of the first BCI was to differentiate between 10[Formula: see text]s of mental repetitions of the word "no" and an equivalent duration of unconstrained rest. The second BCI was designed to discern between 10[Formula: see text]s each of covert repetition of the words "yes" and "no". Twelve participants used these two BCIs to answer yes or no questions. Each participant completed four sessions, comprising two offline training sessions and two online sessions, one for testing each of the BCIs. With a support vector machine and a combination of spectral and time-frequency features, an average accuracy of [Formula: see text] was reached across participants in the online classification of no versus rest, with 10 out of 12 participants surpassing the chance level (60.0% for [Formula: see text]). The online classification of yes versus no yielded an average accuracy of [Formula: see text], with eight participants exceeding the chance level. Task-specific changes in EEG beta and gamma power in language-related brain areas tended to provide discriminatory information. To our knowledge, this is the first report of online EEG classification of covert speech. Our findings support further study of covert speech as a BCI activation task, potentially leading to the development of more intuitive BCIs for communication.

Communication using brain-computer interfaces (BCIs) can be non-intuitive, often requiring the performance of a conversation-irrelevant task such as hand motor imagery. In this paper, the reliability of electroencephalography (EEG) signals in discriminating between different covert speech tasks is investigated. Twelve participants, across two sessions each, were asked to perform multiple iterations of three differing mental tasks for 10 s each: unconstrained rest or the mental repetition of the words “yes” or “no.” A multilayer perceptron (MLP) artificial neural network (ANN) was used to classify all three pairwise combinations of “yes,” “no,” and rest trials and also for ternary classification. An average accuracy of 75.7% ± 9.6 was reached in the classification of covert speech trials versus rest, with all participants exceeding chance level (57.8%). The classification of “yes” versus “no” yielded an average accuracy of 63.2 ± 6.4 with ten participants surpassing chance level (57.8%). Finally, the ternary classification yielded an average accuracy of 54.1% ± 9.7 with all participants exceeding chance level (39.1%). The proposed MLP network provided significantly higher accuracies compared to some of the most common classification techniques in BCI. To our knowledge, this is the first report of using ANN for the classification of EEG covert speech across multiple sessions. Our findings support further study of covert speech as a BCI activation task, potentially leading to the development of more intuitive BCIs for communication.

Most P300 event-related potential (ERP)-based brain-computer interface (BCI) studies focus on gaze shift-dependent BCIs, which cannot be used by people who have lost voluntary eye movement. However, the performance of visual saccade-independent P300 BCIs is generally poor. To improve saccade-independent BCI performance, we propose a bimodal P300 BCI approach that simultaneously employs auditory and tactile stimuli. The proposed P300 BCI is a vision-independent system because no visual interaction is required of the user. Specifically, we designed a direction-congruent bimodal paradigm by randomly and simultaneously presenting auditory and tactile stimuli from the same direction. Furthermore, the channels and number of trials were tailored to each user to improve online performance. With 12 participants, the average online information transfer rate (ITR) of the bimodal approach improved by 45.43% and 51.05% over that attained, respectively, with the auditory and tactile approaches individually. Importantly, the average online ITR of the bimodal approach, including the break time between selections, reached 10.77 bits/min. These findings suggest that the proposed bimodal system holds promise as a practical visual saccade-independent P300 BCI.

Dual-axis swallowing accelerometry has been proposed as a method for quantifying swallowing function. Acceleration signals in anterior-posterior and superior-inferior anatomical directions are processed and automatically segmented. However, the latter is often too liberal, admitting pre- and post-swallowing activity while also giving rise to non-swallow segments. These segmentation shortcomings adversely affect feature extraction and ultimately classification of swallowing function. In this paper, we propose a kernel density estimation-based algorithm to adaptively trim the swallow segments, and energy and noise-floor algorithms to reduce the number of false positive swallow segments. The balance between false positive reduction and loss of true positives can be adjusted according to algorithmic thresholds. Dramatic reductions (-85.4%) in false positives can be achieved with a moderate loss of true positives (-15.1%).

Current human-machine interfaces (HMIs) for users with severe disabilities often have difficulty distinguishing between intentional and inadvertent activations. Pre-movement neuro-cortical activity may aid in this elusive discrimination task but has not been exploited in HMIs. This work investigates the utility of the readiness potential (RP), a slow negative cortical potential preceding voluntary movement, for detecting the intention of self-initiated fine movements prior to their motoric realization. We recorded electroencephalography from the frontal, central, parietal and occipital lobes of 10 participants using a self-initiated switch activation protocol. Eye movement artifacts were removed by regression and the RP was detected on a single-trial basis, in a narrow frequency range (0.1-1 Hz). Common average reference was applied prior to windowed-averaging for feature extraction. Electrodes were selected according to a separability measure based on Fisher projection. Our findings demonstrate that feature fusion from an optimal number of electrodes achieves a statistically significant lower classification error than the best single classifier. Finally, voluntary fine movement intention was detected on a single-trial basis at above-chance levels approximately 396 ms before physical switch activation. These findings encourage the development of rapid-response, intention-aware HMIs for individuals with severe disabilities who struggle with executing voluntary fine motor movements.

We present a novel approach to estimating the mean square error (MSE) associated with any given threshold level in both hard and soft thresholding. The estimate is provided by using only the data that is being thresholded. This adaptive approach provides probabilistic confidence bounds on the MSE. The MSE bounds can be used to evaluate the denoising method. Our simulation results confirm that not only does the method provide an accurate estimate of the MSE for any given thresohlding method, but the proposed method can also search and find an optimum threshold for any noisy data with regard to MSE.

In this paper, the effects of auditory distractions on the performance of brain-computer interfaces (BCI) based on near-infrared spectroscopy (NIRS) are investigated. Experiments show that NIRS-BCI specificity decreases by an average 19% when operated in the presence of continuous background noise (relative to operation in silence) and by 13% when operated in the presence of startle noises. To improve BCI performance in noisy environments, a simple yet effective startle noise compensation strategy is proposed. Acoustic environmental conditions are tracked in realtime and false BCI activations that occur within seconds of detected startle noises are suppressed. Experiments show NIRS-BCI systems equipped with the proposed compensation system attaining performances in noisy conditions comparable to those attained in silent conditions.

The marginal maximum entropy criterion has been used to guide recursive partitioning of a continuous sample space. Although the criterion has been successfully applied in pattern discovery applications, its theoretical justification has not been clearly addressed. In the paper, it is shown that the basic marginal maximum entropy partitioning algorithm yields asymptotically consistent density estimates. This result supports the use of the marginal maximum entropy criterion in pattern discovery and implies that an optimal classifier can be constructed.

In this paper, a novel method of pattern discovery is proposed. It is based on the theoretical formulation of a contingency table of events. Using residual analysis and recursive partitioning, statistically significant events are identified in a data set. These events constitute the important information contained in the data set and are easily interpretable as simple rules, contour plots, or parallel axes plots. In addition, an informative probabilistic description of the data is automatically furnished by the discovery process. Following a theoretical formulation, experiments with real and simulated data will demonstrate the ability to discover subtle patterns amid noise, the invariance to changes of scale, cluster detection, and discovery of multidimensional patterns. It is shown that the pattern discovery method offers the advantages of easy interpretation, rapid training, and tolerance to noncentralized noise.