Objective. This exploratory study compared two non-implantable communication brain-computer interfaces (cBCIs) to determine whether physiologic and self-report measures of mental fatigue, effort, and boredom were greater during calibration than during copy-spelling and whether there were differences between two common cBCI interfaces, rapid serial visual presentation (RSVP) and single-character presentation matrix (SCP-matrix).Approach. Twenty-three healthy adults successfully utilized both RSVP and SCP-matrix speller cBCIs in a single experimental session. Participants completed a calibration task and three online (closed-loop) copy-spelling tasks for each interface and provided self-report data on state mental fatigue, effort, and boredom. Physiological measures included EEG recordings alongside autonomic markers, including blood pressure, heart rate, respiration rate, and pulse rate variability.Main results. Participants reported significant increases in perceived mental fatigue, effort, boredom, and sleepiness during the session, with significant increases during calibration compared to copy-spelling. On average, users typed 1.5 more correct characters per copy-spelling phase using the SCP-matrix interface than when using RSVP. Results for autonomic and self-report metrics were consistent with fatigue being increased during calibration tasks relative to copy-spelling. EEG measures showed increased absolute and relative alpha activity and decreased relative theta activity during calibrations compared to copy-spelling, and increased absolute and relative alpha activity and decreased relative theta activity during RSVP, compared to matrix. P300 amplitude on average was greater during copy spelling tasks than during calibrations.Significance. Participants demonstrated increased fatigue while using non-implantable cBCIs. Evidence suggested that calibration tasks for both interfaces were more fatiguing, required more mental effort, and were less engaging than copy-spelling tasks. Increased user fatigue and perceived mental effort remain significant barriers to sustained use of non-implantable cBCI systems. Though limited, the current study enhances our understanding of user experience with cBCIs and emphasizes the need to design more engaging and concise calibration procedures.

A significant challenge in developing reliable Brain-Computer Interfaces (BCIs) is the presence of artifacts in the acquired brain signals. These artifacts may lead to erroneous interpretations, poor fitting of models, and subsequent reduced online performance. Furthermore, BCIs in a home or hospital setting are more susceptible to environmental noise. Artifact handling procedures aim to reduce signal interference by filtering, reconstructing, and/or eliminating unwanted signal contaminants. While straightforward conceptually and largely undisputed as essential, suitable artifact handling application in BCI systems remains unsettled and may reduce performance in some cases. A potential confound that remains unexplored in the majority of BCI studies using these procedures is the lack of parity with online usage (e.g., online parity). This manuscript compares classification performance between frequently used offline digital filtering, using the whole dataset, and an online digital filtering approach where the segmented data epochs that would be used during closed-loop control are filtered instead. In a sample of healthy adults (n = 30) enrolled in a BCI pilot study to integrate new communication interfaces, there were significant benefits to model performance when filtering with online parity. While online simulations indicated similar performance across conditions in this study, there appears to be no drawback to the approach with greater online parity.

Objective.The RSVP Keyboard is a non-implantable, event-related potential-based brain-computer interface (BCI) system designed to support communication access for people with severe speech and physical impairments. Here we introduce inquiry preview (IP), a new RSVP Keyboard interface incorporating switch input for users with some voluntary motor function, and describe its effects on typing performance and other outcomes.Approach.Four individuals with disabilities participated in the collaborative design of possible switch input applications for the RSVP Keyboard, leading to the development of IP and a method of fusing switch input with language model and electroencephalography (EEG) evidence for typing. Twenty-four participants without disabilities and one potential end user with incomplete locked-in syndrome took part in two experiments investigating the effects of IP and two modes of switch input on typing accuracy and speed during a copy-spelling task.Main results.For participants without disabilities, IP and switch input tended to worsen typing performance compared to the standard RSVP Keyboard condition, with more consistent effects across participants for speed than for accuracy. However, there was considerable variability, with some participants demonstrating improved typing performance and better user experience (UX) with IP and switch input. Typing performance for the potential end user was comparable to that of participants without disabilities. He typed most quickly and accurately with IP and switch input and gave favorable UX ratings to those conditions, but preferred standard RSVP Keyboard.Significance.IP is a novel multimodal interface for the RSVP Keyboard BCI, incorporating switch input as an additional control signal. Typing performance and UX and preference varied widely across participants, reinforcing the need for flexible, customizable BCI systems that can adapt to individual users. ClinicalTrials.gov Identifier: NCT04468919.

Brain responses to discrete stimuli are modulated when multiple stimuli are presented in sequence. These alterations are especially pronounced when the time course of an evoked response overlaps with responses to subsequent stimuli, such as in a rapid serial visual presentation (RSVP) paradigm used to control a brain–computer interface (BCI). The present study explored whether the measurement or classification of select brain responses during RSVP would improve through application of an established technique for dealing with overlapping stimulus presentations, known as irregular or “jittered” stimulus onset interval (SOI). EEG data were collected from 24 healthy adult participants across multiple rounds of RSVP calibration and copy phrase tasks with varying degrees of SOI jitter. Analyses measured three separate brain signals sensitive to attention: N200, P300, and occipitoparietal alpha attenuation. Presentation jitter visibly reduced intrusion of the SSVEP, but in general, it did not positively or negatively affect attention effects, classification, or system performance. Though it remains unclear whether stimulus overlap is detrimental to BCI performance overall, the present study demonstrates that single-trial classification approaches may be resilient to rhythmic intrusions like SSVEP that appear in the averaged EEG.

Objective: The nociceptive system has been implicated in acupuncture analgesia, although acupuncture's precise mechanism of action remains unknown. Electric pain-related evoked potentials (PREPs) have emerged as an effective and reliable electrophysiologic method for evaluation of the human nociceptive system by electric stimulation of nociceptive Aδ and C fibers. This pilot mechanistic study aims to assess the feasibility of using advanced PREP techniques together with electroacupuncture and to use PREPs to characterize acupuncture's effect on nociception. Methods: Seven healthy volunteers underwent a previously designed electroacupuncture protocol using acupoints in the legs bilaterally, which has been demonstrated to induce systemic analgesia. Advanced PREP techniques involving tripolar stimulating electrode, varying interstimulus interval, and incorporating a cognitive task during PREPs were used. PREPs were assessed before electroacupuncture, during electroacupuncture, and 30 min after electroacupuncture. Subjective pain perception in response to the PREP-related electric pain stimuli delivered to the nondominant hand was assessed on the visual analog scale (VAS) at baseline, during electroacupuncture, and 30 min postelectroacupuncture. Results: Reliable PREP N1, P1, and N2 waves were obtained from all subjects at the following average latencies: N1 = 131.5 msec, P1 = 189.4 msec, and N2 = 231.1 msec. Electroacupuncture caused a significant reduction in PREP N1P1 wave amplitudes from 25.6 to 15.4 μV (p = 0.006) and electric pain perception on the VAS-from 2.86 to 2.14 (p = 0.008), compared to baseline. These effects were sustained at 30 min postacupuncture with N1P1 wave amplitude 17.2 μV (p = 0.030) and VAS 2.28 (p = 0.030), compared to baseline. Conclusions: Electroacupuncture causes significant changes in objective nociception, measured by PREP N1P1 wave amplitudes, and in subjective nociception, measured by the VAS, and these effects are sustained for 30 min after electroacupuncture. Planned future studies will involve chronic pain populations and will aim to assess acupuncture's longer term analgesic effects.

This pilot mechanistic study aims to assess the feasibility of using advanced electric pain-related evoked potential (PREP) techniques together with electroacupuncture, and the utility of using PREPs to capture and quantify acupuncture's nociceptive effects.

This study evaluated the feasibility of using occipitoparietal alpha activity to drive target/non-target classification in a brain-computer interface (BCI) for communication. EEG data were collected from 12 participants who completed BCI Rapid Serial Visual Presentation (RSVP) calibrations at two different presentation rates: 1 and 4 Hz. Attention-related changes in posterior alpha activity were compared to two event-related potentials (ERPs): N200 and P300. Machine learning approaches evaluated target/non-target classification accuracy using alpha activity. Results indicated significant alpha attenuation following target letters at both 1 and 4 Hz presentation rates, though this effect was significantly reduced in the 4 Hz condition. Target-related alpha attenuation was not correlated with coincident N200 or P300 target effects. Classification using posterior alpha activity was above chance and benefitted from individualized tuning procedures. These findings suggest that target-related posterior alpha attenuation is detectable in a BCI RSVP calibration and that this signal could be leveraged in machine learning algorithms used for RSVP or comparable attention-based BCI paradigms.

Brain computer interfaces systems are controlled by users through neurophysiological input for a variety of applications including communication, environmental control, motor rehabilitation, and cognitive training. Although individuals with severe speech and physical impairment are the primary users of this technology, BCIs have emerged as a potential tool for broader populations, especially with regards to delivering cognitive training or interventions with neurofeedback. The goal of this study was to investigate the feasibility of using a BCI system with neurofeedback as an intervention for people with mild Alzheimer's disease. The study focused on visual attention and language since ad is often associated with functional impairments in language and reading. The study enrolled five adults with mild ad in a nine to thirteen week BCI EEG based neurofeedback intervention to improve attention and reading skills. Two participants completed intervention entirely. The remaining three participants could not complete the intervention phase because of restrictions related to covid. Pre and post assessment measures were used to assess reliability of outcome measures and generalization of treatment to functional reading, processing speed, attention, and working memory skills. Participants demonstrated steady improvement in most cognitive measures across experimental phases, although there was not a significant effect of NFB on most measures of attention. One subject demonstrated significantly significant improvement in letter cancellation during NFB. All participants with mild AD learned to operate a BCI system with training. Results have broad implications for the design and use of bci systems for participants with cognitive impairment. Preliminary evidence justifies implementing NFB-based cognitive measures in AD.

There are high technological and software demands associated with conducting brain-computer interface (BCI) research. In order to accelerate the development and accessibility of BCI, it is worthwhile to focus on open-source and desired tooling. Python, a prominent computer language, has emerged as a language of choice for many research and engineering purposes. In this manuscript, we present BciPy, an open-source, Python-based software for conducting BCI research. It was developed with a focus on restoring communication using event-related potential (ERP) spelling interfaces, however, it may be used for other non-spelling and non-ERP BCI paradigms. Major modules in this system include support for data acquisition, data queries, stimuli presentation, signal processing, signal viewing and modeling, language modeling, task building, and a simple Graphical User Interface (GUI).

Background: Traumatic brain injury (TBI) is a leading cause of morbidity and mortality globally. Currently, the association between hyperoxia and outcomes in patients with TBI remains debatable. We assessed the effect of hyperoxia on the neurological outcomes and survival of critically ill patients with moderate-severe TBI. Methods: This was a retrospective cohort study of all adults with moderatesevere TBI admitted to the ICU between 1st January 2016 and 31st December 2019 who required invasive mechanical ventilation. We noted ABGs performed with the first 3 hours of intubation, then 6-12 hours and 24-48 hours. The patients were divided into two categories: normoxia (PaO2 60-99 mmHg) and hyeroxia (PaO2 >100 mmHg). Multivariable logistic regression was performed to assess predictors of hospital mortality and good neurologic outcome (Glasgow outcome score [GOS] ≥ 4). In a second analysis the patients were divided into survivors and non-survivors. Results: The study included 308 patients: 23.4% (n=72) in normoxia group and 76.6% (n=236) in hyeroxia group. Hyperoxia was not associated with increased hospital (43% vs. 18%, p=0.20) mortality. Further, the hospital discharge GCS (10 ± 5 vs. 11 ± 4, p=0.10) and GOS (3 ± 1 vs. 3 ± 1, p=0.35) were similar. In multivariable logistic regression analysis, hyperoxia was not associated with increased mortality (adjusted odds ratio [aOR] 0.99, 95% CI 0.99-1.00, p=0.11). PaO2 within different ranges was also not associated with mortality: 100-200 mmHg: aOR 0.60, 95% CI 0.29-1.52; 201-300 mmHg: aOR 0.66, 95% CI 0.29-1.52; 301-400 mmHg: aOR 0.80, 95% CI 0.31-2.09; and >400 mmHg: aOR 0.39, 95% CI 0.14-1.08; reference: PaO2 60-99 mmHg. The Kaplan-Meier survival curve for normoxia verses hyperoxia showed no significant difference for all-cause mortality. In the survivors verse nonsurvivors analysis, the PaO2 were (median, IQT) 199 mmHg (111-329) and 165 mmHg (84-252), respectively. Conclusion: Hyperoxia (PaO2 >100 mmHg) was not associated with increased mortality or poor neurological outcomes (determined by GOS) in moderatesevere TBI patients.

Objective: Pain habituation is a central pain-inhibiting mechanism that is less effective or absent in chronic low back pain patients (cLBP). Brain structures associated with habituation are also implicated in interoceptive awareness. Primary aims are to: (1) compare habituation between healthy controls and cLBP patients and (2) explore the relationship between habituation and interoceptive awareness. Methods: Participants completed self-report measures of clinical pain (PROMIS) and interoceptive awareness (Multidimensional Assessment of Interoceptive Awareness;MAIA-2) and underwent a pain paradigm with recorded EEG activity. Three blocks of 20 stimuli were applied using tri-polar electrodes with a fixed inter-block interval of five minutes. Latencies of pain-related evoked potentials (PREP) components and baseline-to-peak ERP amplitudes were analyzed. Early-potentials (N1) arise from somatosensory cortices and are sensitive to intensity of stimulation. Late latency potentials (P2) arise from the insula and ACC and reflect threat and hedonic appraisal of pain. Habituation was measured by calculating percentage change in P2 amplitude between Blocks 1 and 3. Negative percentage values indicated habituation. Due to COVID, research was halted, therefore, preliminary data analyses using nonparametric tests were conducted. Results: Healthy controls (n=4) demonstrated habituation, evidenced by reduced P2 amplitude from block 1 (Mdn=228.51) to block 3 (Mdn=216.30;p=0.06). In cLBP patients (n=4), pain habituation was absent evidenced by an increase in P2 amplitude from block 1 (Mdn=221.67) to block 3 (Mdn=234.86). Median differences were found in P2 habituation between healthy controls (Mdn=-3.12) and cLBP patients (Mdn=5.80;p=.02). Habituation was positively associated with disability (r=0.86) and negatively associated with several subscales on the MAIA-2. Conclusion: PREP's may be a potential biomarker for pain habituation. Testing in the larger planned sample will provide insight into the relationship between pain habituation and interoceptive awareness in cLBP.

Access to communication is critical for individuals with late-stage amyotrophic lateral sclerosis (ALS) and minimal volitional movement, but they sometimes present with concomitant visual or ocular motility impairments that affect their performance with eye tracking or visual brain-computer interface (BCI) systems. In this study, we explored the use of modified eye tracking and steady state visual evoked potential (SSVEP) BCI, in combination with the Shuffle Speller typing interface, for this population. Two participants with late-stage ALS, visual impairments, and minimal volitional movement completed a single-case experimental research design comparing copy-spelling performance with three different typing systems: (1) commercially available eye tracking communication software, (2) Shuffle Speller with modified eye tracking, and (3) Shuffle Speller with SSVEP BCI. Participant 1 was unable to type any correct characters with the commercial system, but achieved accuracies of up to 50% with Shuffle Speller eye tracking and 89% with Shuffle Speller BCI. Participant 2 also had higher maximum accuracies with Shuffle Speller, typing with up to 63% accuracy with eye tracking and 100% accuracy with BCI. However, participants' typing accuracy for both Shuffle Speller conditions was highly variable, particularly in the BCI condition. Both the Shuffle Speller interface and SSVEP BCI input show promise for improving typing performance for people with late-stage ALS. Further development of innovative BCI systems for this population is needed.

PURPOSE:This study used a multiple crossover ABAB single case design to examine intracranial EEG data during a breath awareness meditation and an active control task. RESULTS:Visual analyses suggest that a brief breath awareness mediation was consistently associated with increased alpha power when compared to the active control. Less consistent effects were found with theta, beta, and high gamma activity. Nonparametric tests provided additional support for this finding. CONCLUSIONS:Acquiring intracranial EEG patterns during a meditative state may provide more insight into the physiology of meditation with less contamination of high-frequency muscle activity. While access to intracranial EEG during meditation is rarely available, single case design studies are considered adaptations of interrupted time-series designs and can provide an experimental evaluation of intervention effects.

Across- and within-recording variabilities in electroencephalographic (EEG) activity is a major limitation in EEG-based brain-computer interfaces (BCIs). Specifically, gradual changes in fatigue and vigilance levels during long EEG recording durations and BCI system usage bring along significant fluctuations in BCI performances even when these systems are calibrated daily. We address this in an experimental offline study from EEG-based BCI speller usage data acquired for one hour duration. As the main part of our methodological approach, we propose the concept of adversarial invariant feature learning for BCIs as a regularization approach on recently expanding EEG deep learning architectures, to learn nuisance-invariant discriminative features. We empirically demonstrate the feasibility of adversarial feature learning on eliminating drowsiness effects from event related EEG activity features, by using temporal recording block ordering as the source of drowsiness variability.

Individuals with severe speech and physical impairments may have concomitant visual acuity impairments (VAI) or ocular motility impairments (OMI) impacting visual BCI use. We report on use of the Shuffle Speller typing interface for SSVEP BCI copy-spelling with simulated VAI, simulated OMI, and unimpaired vision. To mitigate the effects of visual impairments, we introduce a method that adaptively selects user-specific trial lengths to maximize expected information transfer rate (ITR), which is shown to closely approximate correct letter selection rate. All participants could type under the unimpaired and simulated VAI conditions, without significant differences in typing accuracy or speed. Most participants (31 of 37) could not type under the simulated OMI condition; some achieved high accuracy with slower typing speeds. Reported workload and discomfort were low, and satisfaction high, under the unimpaired and simulated VAI conditions. Implications and future directions for exploration of visual impairment in BCI use are discussed.

Negative affect (NA) has been related to lower working memory performance across all ages, including in older adults where it has been suggested as a marker for later cognitive impairments. However, NA-related decreases in working memory performance have not been shown in a full range of working memory paradigms or fully explored in the context of electrophysiological measures of working memory. We examined the impact of NA and related markers on an electroencephalography (EEG) marker of visual working memory (VWM) capacity, referred to as the contralateral delay activity (CDA). This study analyzed data collected from 48 cognitively intact, mildly stressed older adults (50-74 years old) who completed a VWM change-detection task to elicit the CDA, as well as self-rated measures of affect, stress, neuroticism and depression. Regression analyses revealed significant CDA amplitude effects with NA across task conditions. These results indicate a reduction in a physiological measure of VWM capacity in high-NA participants. These results are of interest as NA has been associated with a greater risk for worse cognitive function, detrimental health outcomes and reduced quality of life in older adults. This research adds to our understanding of how NA impacts older adults and gives a potential biomarker for successful intervention outcomes.

Brain computer interfaces (BCI) generally require the user to maintain an attentive state. Potential end-users with severe speech and physical impairments may have limited communication abilities to report their current state, thus an automatic calculation of state may improve performance. It's not yet known if an effective automatic calculation of drowsiness can be detected reliably in end-user populations or healthy controls. In this study, we examined data from 20 healthy participants (33 ± 13 years of age) to understand how self-rated measures (sleepiness and boredom) and automatically calculated drowsiness measures are associated with underlying classifier performance (area under the curve, AUC) during 5 successive 11-min calibration sessions. The BCI system used in this experiment is the RSVPKeyboard™, which relies on P300 responses to target letters (Orhan et al., 2012). EEG was obtained using a DSI-24 system (Wearable Sensing), recorded at 300 Hz and software filtered to reduce presence of artifacts. Automatically calculated measures based on the literature (Oken et al., 2006) utilized eye-blink rate (blinks/time) and theta [4–7 Hz], alpha [8–12 Hz], and median power frequencies (MPF). The frequency measures were calculated from a stimulus presentation sequence of approximately 10 letters lasting 4 s fed into MATLAB's power spectral density function and averaged. P300 amplitude was calculated with EEGLAB v. 14.1 using a peak to trough (peak minus trough) method; the highest positive potential in the 350–600 ms range of the Cz channel as the peak and the negative-most peak preceding it as the trough. Self-report of sleepiness was assessed using the Karolinska Sleepiness Scale (KSS) (Gillberg et al., 1994). Boredom was assessed using a 6-item boredom scale (Markey et al., 2014). Using a Wilcoxon-Signed Rank Test to compare scores from the first and last calibration sessions, the following observations were made: (1) There were significant differences in AUC (Z = 3.32, p < .001; mean from .877 to .794), (2) self-report measures of boredom (Z = −3.349, p < .001; mean from 13.3 to 23.65) and sleepiness (Z = −3.91, p < .0001; mean from 3.55 to 7.1), (3) alpha power (Z = −3.18, p = .0015; mean from 7.19E−6 to 9.6E−6), (4) eye blink rate (Z = 2.42, p = .15; mean from 4.35 to 2.67), and (5) P300 amplitude (Z = 3.44, p < .001; mean from 9.8E−6 to 6.37E−6) between the first and last calibration sessions. There were no significant differences in MPF (mean from 8.8 to 9.62) or theta (mean from 1.27E−5 to 1.36E−5). There were significant declines in performance using a BCI system over the course of this experiment. These results suggest the automatically calculated measures may be able to capture user state and relationships to changes in performance. While the self-report measure findings are of interest, it may be difficult to continually solicit questionnaire feedback and automated measures could be further leveraged in classification models.

The effect of fatigue and drowsiness on brain-computer interface (BCI) performance was evaluated. 20 healthy participants performed a standardized 11-minute calibration of a Rapid Serial Visual Presentation BCI system five times over two hours. For each calibration, BCI performance was evaluated using area under the receiver operating characteristic curve (AUC). Self-rated measures were obtained following each calibration including the Karolinska Sleepiness Scale and a standardized boredom scale. Physiological measures were obtained during each calibration including P300 amplitude, theta power, alpha power, median power frequency and eye-blink rate. There was a significant decrease in AUC over the five sessions. This was paralleled by increases in self-rated sleepiness and boredom and decreases in P300 amplitude. Alpha power, median power frequency, and eye-blink rate also increased but more modestly. AUC changes were only partly explained by changes in P300 amplitude. There was a decrease in BCI performance over time that related to increases in sleepiness and boredom. This worsened performance was only partly explained by decreases in P300 amplitude. Thus, drowsiness and boredom have a negative impact on BCI performance. Increased BCI performance may be possible by developing physiological measures to provide feedback to the user or to adapt the classifier to state.

Despite the interest in mindfulness over the past 20 years, studies have only recently begun to examine mindfulness in older adults. The primary aim of this study was to evaluate pretreatment to post-treatment change in negative affect variability (NAV) following a mindfulness training among 134 mildly stressed, middle-aged to older adults. The secondary aim was to assess if the effects of mindfulness training on NAV would be partially explained by pretreatment to post-treatment reductions in perceived stress, a trend that would be congruent with several stress models. In this randomized control trial, participants were assigned to either a 6-week mindfulness meditation training programme or to a wait list control. Ecological momentary assessment, a data capturing technique that queries about present moment experiences in real time, captured NAV. Mixed-model ANOVAs and a path analysis were conducted. Participants in the mindfulness meditation training significantly reduced NAV when compared with wait list control participants. Further, there was a significant indirect group effect on reductions in NAV through change in perceived stress. Few studies have tested mechanisms of action, which connect changes that occur during mindfulness training with psychological outcomes in older adults. Understanding the mechanisms by which mindfulness enhances well-being may optimize interventions.