Investigating the Brain Mechanisms of Externally Cued Sit-to-Stand Movement in Parkinson's Disease.

BACKGROUND: One of the more challenging daily-life actions for Parkinson's disease patients is starting to stand from a sitting position. Parkinson's disease patients are known to have difficulty with self-initiated movements and benefit from external cues. However, the brain processes underlying external cueing as an aid remain unknown. The advent of mobile electroencephalography (EEG) now enables the investigation of these processes in dynamic sit-to-stand movements. OBJECTIVE: To identify cortical correlates of the mechanisms underlying auditory cued sit-to-stand movement in Parkinson's disease. METHODS: Twenty-two Parkinson's disease patients and 24 healthy age-matched participants performed self-initiated and externally cued sit-to-stand movements while cortical activity was recorded through 32-channel mobile EEG. RESULTS: Overall impaired integration of sensory and motor information can be seen in the Parkinson's disease patients exhibiting less modulation in the θ band during movement compared to healthy age-matched controls. How Parkinson's disease patients use external cueing of sit-to-stand movements can be seen in larger high ß power over sensorimotor brain areas compared to healthy controls, signaling sensory integration supporting the maintenance of motor output. This appears to require changes in cognitive processing to update the motor plan, reflected in frontal θ power increases in Parkinson's disease patients when cued. CONCLUSION: These findings provide the first neural evidence for why and how cueing improves motor function in sit-to-stand movement in Parkinson's disease. The Parkinson's disease patients' neural correlates indicate that cueing induces greater activation of motor cortical areas supporting the maintenance of a more stable motor output, but involves the use of cognitive resources to update the motor plan. © 2024 International Parkinson and Movement Disorder Society.

Commit to your putting stroke: exploring the impact of quiet eye duration and neural activity on golf putting performance.

Introduction: There is a growing interest in characterizing the cognitive-motor processes that underlie superior performance in highly skilled athletes. The aim of this study was to explore neural markers of putting performance in highly skilled golfers by recording mobile EEG (electroencephalogram) during the pre-shot period. Methods: Twenty-eight right-handed participants (20 males) with a mean age of 24.2 years (± 6.4) and an average handicap of +1.7 (± 6.4) completed a testing session. Following the warm-up, participants completed 140 putts from a distance of 8ft (2.4m), with putts taken from 5 different positions. While putting, participants wore an eye tracker and a gel-based EEG system with 32 electrodes. Time and frequency domain features of the EEG signals were extracted to characterize Movement-Related Cortical Potentials (MRCP) and rhythmic modulations of neural activity in theta, alpha, sensorimotor and beta frequency bands associated with putting performance. Results: Eye-tracking data demonstrate that mean Quiet Eye durations are not a reliable marker of expertise as the same duration was found for both successful and unsuccessful putts. Following rigorous data processing data from 12 participants (8 males, mean age 21.6 years ± 5.4, average handicap +1.5 ± 4.4) were included in the EEG analysis. MRCP analysis revealed performance-based differences, with unsuccessful putts having a greater negative amplitude in comparison to successful putts. Time frequency analysis of the EEG data revealed that successful putts exhibit distinct neural activity profiles compared to unsuccessful ones. For successful putts, greater suppression of beta was present in the central region prior to the putt. By contrast, increased frontal theta power was present for unsuccessful putts immediately before the putt (consistent with hesitation and the need for motor plan adjustments prior to execution). Discussion: We propose that neural activity may provide plausible insights into the mechanisms behind why identical QE durations can lead to both success and failure. From an applied perspective, this study highlights the merits of a multi-measure approach to gain further insights into performance differences within highly skilled golfers. We discuss considerations for future research and solutions to address the challenges related to the complexities of collecting clean EEG signals within naturalistic sporting contexts.

A systematic review of mobile brain/body imaging studies using the P300 event-related potentials to investigate cognition beyond the laboratory.

The P300 ERP component, related to the onset of task-relevant or infrequent stimuli, has been widely used in the Mobile Brain/Body Imaging (MoBI) literature. This systematic review evaluates the quality and breadth of P300 MoBI studies, revealing a maturing field with well-designed research yet grappling with standardization and global representation challenges. While affirming the reliability of measuring P300 ERP components in mobile settings, the review identifies significant hurdles in standardizing data cleaning and processing techniques, impacting comparability and reproducibility. Geographical disparities emerge, with studies predominantly in the Global North and a dearth of research from the Global South, emphasizing the need for broader inclusivity to counter the WEIRD bias in psychology. Collaborative projects and mobile EEG systems showcase the feasibility of reaching diverse populations, which is essential to advance precision psychiatry and to integrate varied data streams. Methodologically, a trend toward ecological validity is noted, shifting from lab-based to real-world settings with portable EEG system advancements. Future hardware developments are expected to balance signal quality and sensor intrusiveness, enriching data collection in everyday contexts. Innovative methodologies reflect a move toward more natural experimental settings, prompting critical questions about the applicability of traditional ERP markers, such as the P300 outside structured paradigms. The review concludes by highlighting the crucial role of integrating mobile technologies, physiological sensors, and machine learning to advance cognitive neuroscience. It advocates for an operational definition of ecological validity to bridge the gap between controlled experiments and the complexity of embodied cognitive experiences, enhancing both theoretical understanding and practical application in study design.

Evaluation of a headphones-fitted EEG system for the recording of auditory evoked potentials and mental workload assessment.

Advancements in portable neuroimaging technologies open up new opportunities to gain insight into the neural dynamics and cognitive processes underlying day-to-day behaviors. In this study, we evaluated the relevance of a headphone- mounted electroencephalogram (EEG) system for monitoring mental workload. The participants (N = 12) were instructed to pay attention to auditory alarms presented sporadically while performing the Multi-Attribute Task Battery (MATB) whose difficulty was staged across three conditions to manipulate mental workload. The P300 Event-Related Potentials (ERP) elicited by the presentation of auditory alarms were used as probes of attentional resources available. The amplitude and latency of P300 ERPs were compared across experimental conditions. Our findings indicate that the P300 ERP component can be captured using a headphone-mounted EEG system. Moreover, neural responses to alarm could be used to classify mental workload with high accuracy (over 80%) at a single-trial level. Our analyses indicated that the signal-to-noise ratio acquired by the sponge-based sensors remained stable throughout the recordings. These results highlight the potential of portable neuroimaging technology for the development of neuroassistive applications while underscoring the current limitations and challenges associated with the integration of EEG sensors in everyday-life wearable technologies. Overall, our study contributes to the growing body of research exploring the feasibility and validity of wearable neuroimaging technologies for the study of human cognition and behavior in real-world settings.

Characterizing neurocognitive impairments in Parkinson's disease with mobile EEG when walking and stepping over obstacles.

The neural correlates that help us understand the challenges that Parkinson's patients face when negotiating their environment remain under-researched. This deficit in knowledge reflects the methodological constraints of traditional neuroimaging techniques, which include the need to remain still. As a result, much of our understanding of motor disorders is still based on animal models. Daily life challenges such as tripping and falling over obstacles represent one of the main causes of hospitalization for individuals with Parkinson's disease. Here, we report the neural correlates of naturalistic ambulatory obstacle avoidance in Parkinson's disease patients using mobile EEG. We examined 14 medicated patients with Parkinson's disease and 17 neurotypical control participants. Brain activity was recorded while participants walked freely, and while they walked and adjusted their gait to step over expected obstacles (preset adjustment) or unexpected obstacles (online adjustment) displayed on the floor. EEG analysis revealed attenuated cortical activity in Parkinson's patients compared to neurotypical participants in theta (4-7 Hz) and beta (13-35 Hz) frequency bands. The theta power increase when planning an online adjustment to step over unexpected obstacles was reduced in Parkinson's patients compared to neurotypical participants, indicating impaired proactive cognitive control of walking that updates the online action plan when unexpected changes occur in the environment. Impaired action planning processes were further evident in Parkinson's disease patients' diminished beta power suppression when preparing motor adaptation to step over obstacles, regardless of the expectation manipulation, compared to when walking freely. In addition, deficits in reactive control mechanisms in Parkinson's disease compared to neurotypical participants were evident from an attenuated beta rebound signal after crossing an obstacle. Reduced modulation in the theta frequency band in the resetting phase across conditions also suggests a deficit in the evaluation of action outcomes in Parkinson's disease. Taken together, the neural markers of cognitive control of walking observed in Parkinson's disease reveal a pervasive deficit of motor-cognitive control, involving impairments in the proactive and reactive strategies used to avoid obstacles while walking. As such, this study identified neural markers of the motor deficits in Parkinson's disease and revealed patients' difficulties in adapting movements both before and after avoiding obstacles in their path.

Burst c-VEP Based BCI: Optimizing stimulus design for enhanced classification with minimal calibration data and improved user experience.

The utilization of aperiodic flickering visual stimuli under the form of code-modulated Visual Evoked Potentials (c-VEP) represents a pivotal advancement in the field of reactive Brain-Computer Interface (rBCI). A major advantage of the c-VEP approach is that the training of the model is independent of the number and complexity of targets, which helps reduce calibration time. Nevertheless, the existing designs of c-VEP stimuli can be further improved in terms of visual user experience but also to achieve a higher signal-to-noise ratio, while shortening the selection time and calibration process. In this study, we introduce an innovative variant of code-VEP, referred to as "Burst c-VEP". This original approach involves the presentation of short bursts of aperiodic visual flashes at a deliberately slow rate, typically ranging from two to four flashes per second. The rationale behind this design is to leverage the sensitivity of the primary visual cortex to transient changes in low-level stimuli features to reliably elicit distinctive series of visual evoked potentials. In comparison to other types of faster-paced code sequences, burst c-VEP exhibit favorable properties to achieve high bitwise decoding performance using convolutional neural networks (CNN), which yields potential to attain faster selection time with the need for less calibration data. Furthermore, our investigation focuses on reducing the perceptual saliency of c-VEP through the attenuation of visual stimuli contrast and intensity to significantly improve users' visual comfort. The proposed solutions were tested through an offline 4-classes c-VEP protocol involving 12 participants. Following a factorial design, participants were instructed to focus on c-VEP targets whose pattern (burst and maximum-length sequences) and amplitude (100% or 40% amplitude depth modulations) were manipulated across experimental conditions. Firstly, the full amplitude burst c-VEP sequences exhibited higher accuracy, ranging from 90.5% (with 17.6s of calibration data) to 95.6% (with 52.8s of calibration data), compared to its m-sequence counterpart (71.4% to 85.0%). The mean selection time for both types of codes (1.5 s) compared favorably to reports from previous studies. Secondly, our findings revealed that lowering the intensity of the stimuli only slightly decreased the accuracy of the burst code sequences to 94.2% while leading to substantial improvements in terms of user experience. Taken together, these results demonstrate the high potential of the proposed burst codes to advance reactive BCI both in terms of performance and usability. The collected dataset, along with the proposed CNN architecture implementation, are shared through open-access repositories.

Capturing Cognitive Events Embedded in the Real World Using Mobile Electroencephalography and Eye-Tracking.

The study of cognitive processes underlying natural behaviors implies departing from computerized paradigms and artificial experimental probes. The present study aims to assess the feasibility of capturing neural markers (P300 ERPs) of cognitive processes evoked in response to the identification of task-relevant objects embedded in a real-world environment. To this end, EEG and eye-tracking data were recorded while participants attended stimuli presented on a tablet and while they searched for books in a library. Initial analyses of the library data revealed that P300-like features shifted in time. A Dynamic Time Warping analysis confirmed the presence of P300 ERP in the library condition. Library data were then lag-corrected based on cross-correlation coefficients. Together, these approaches uncovered P300 ERP responses in the library recordings. These findings highlight the relevance of scalable experimental designs, joint brain and body recordings, and template-matching analyses to capture cognitive events during natural behaviors.

Retrospective on the First Passive Brain-Computer Interface Competition on Cross-Session Workload Estimation.

As is the case in several research domains, data sharing is still scarce in the field of Brain-Computer Interfaces (BCI), and particularly in that of passive BCIs-i.e., systems that enable implicit interaction or task adaptation based on a user's mental state(s) estimated from brain measures. Moreover, research in this field is currently hindered by a major challenge, which is tackling brain signal variability such as cross-session variability. Hence, with a view to develop good research practices in this field and to enable the whole community to join forces in working on cross-session estimation, we created the first passive brain-computer interface competition on cross-session workload estimation. This competition was part of the 3rd International Neuroergonomics conference. The data were electroencephalographic recordings acquired from 15 volunteers (6 females; average 25 y.o.) who performed 3 sessions-separated by 7 days-of the Multi-Attribute Task Battery-II (MATB-II) with 3 levels of difficulty per session (pseudo-randomized order). The data -training and testing sets-were made publicly available on Zenodo along with Matlab and Python toy code (https://doi.org/10.5281/zenodo.5055046). To this day, the database was downloaded more than 900 times (unique downloads of all version on the 10th of December 2021: 911). Eleven teams from 3 continents (31 participants) submitted their work. The best achieving processing pipelines included a Riemannian geometry-based method. Although better than the adjusted chance level (38% with an α at 0.05 for a 3-class classification problem), the results still remained under 60% of accuracy. These results clearly underline the real challenge that is cross-session estimation. Moreover, they confirmed once more the robustness and effectiveness of Riemannian methods for BCI. On the contrary, chance level results were obtained by one third of the methods-4 teams- based on Deep Learning. These methods have not demonstrated superior results in this contest compared to traditional methods, which may be due to severe overfitting. Yet this competition is the first step toward a joint effort to tackle BCI variability and to promote good research practices including reproducibility.

Dual Passive Reactive Brain-Computer Interface: A Novel Approach to Human-Machine Symbiosis.

The present study proposes a novel concept of neuroadaptive technology, namely a dual passive-reactive Brain-Computer Interface (BCI), that enables bi-directional interaction between humans and machines. We have implemented such a system in a realistic flight simulator using the NextMind classification algorithms and framework to decode pilots' intention (reactive BCI) and to infer their level of attention (passive BCI). Twelve pilots used the reactive BCI to perform checklists along with an anti-collision radar monitoring task that was supervised by the passive BCI. The latter simulated an automatic avoidance maneuver when it detected that pilots missed an incoming collision. The reactive BCI reached 100% classification accuracy with a mean reaction time of 1.6 s when exclusively performing the checklist task. Accuracy was up to 98.5% with a mean reaction time of 2.5 s when pilots also had to fly the aircraft and monitor the anti-collision radar. The passive BCI achieved a F1-score of 0.94. This first demonstration shows the potential of a dual BCI to improve human-machine teaming which could be applied to a variety of applications.

Mobile EEG reveals functionally dissociable dynamic processes supporting real-world ambulatory obstacle avoidance: Evidence for early proactive control.

The ability to safely negotiate the world on foot takes humans years to develop, reflecting the extensive cognitive demands associated with real-time planning and control of walking. Despite the importance of walking, methodological limitations mean that surprisingly little is known about the neural and cognitive processes that support ambulatory motor control. Here, we report mobile EEG data recorded from 32 healthy young adults during real-world ambulatory obstacle avoidance. Participants walked along a path while stepping over expected and unexpected obstacles projected on the floor, allowing us to capture the dynamic oscillatory response to changes in environmental demands. Compared to obstacle-free walking, time-frequency analysis of the EEG data revealed clear neural markers of proactive and reactive forms of movement control (occurring before and after crossing an obstacle), visible as increases in frontal theta and centro-parietal beta power respectively. Critically, the temporal profile of changes in frontal theta allowed us to arbitrate between early selection and late adaptation mechanisms of proactive control. Our data show that motor plans are updated as soon as an upcoming obstacle appears, rather than when the obstacle is reached. In addition, regardless of whether motor plans required updating, a clear beta rebound was present after obstacles were crossed, reflecting the resetting of the motor system. Overall, mobile EEG recorded during real-world walking provides novel insight into the cognitive and neural basis of dynamic motor control in humans, suggesting new routes to the monitoring and rehabilitation of motor disorders such as dyspraxia and Parkinson's disease.

Mobile EEG identifies the re-allocation of attention during real-world activity.

The distribution of attention between competing processing demands can have dramatic real-world consequences, however little is known about how limited attentional resources are distributed during real-world behaviour. Here we employ mobile EEG to characterise the allocation of attention across multiple sensory-cognitive processing demands during naturalistic movement. We used a neural marker of attention, the Event-Related Potential (ERP) P300 effect, to show that attention to targets is reduced when human participants walk compared to when they stand still. In a second experiment, we show that this reduction in attention is not caused by the act of walking per se. A third experiment identified the independent processing demands driving reduced attention to target stimuli during motion. ERP data reveals that the reduction in attention seen during walking reflects the linear and additive sum of the processing demands produced by visual and inertial stimulation. The mobile cognition approach used here shows how limited resources are precisely re-allocated according to the sensory processing demands that occur during real-world behaviour.

Understanding Minds in Real-World Environments: Toward a Mobile Cognition Approach.

There is a growing body of evidence that important aspects of human cognition have been marginalized, or overlooked, by traditional cognitive science. In particular, the use of laboratory-based experiments in which stimuli are artificial, and response options are fixed, inevitably results in findings that are less ecologically valid in relation to real-world behavior. In the present review we highlight the opportunities provided by a range of new mobile technologies that allow traditionally lab-bound measurements to now be collected during natural interactions with the world. We begin by outlining the theoretical support that mobile approaches receive from the development of embodied accounts of cognition, and we review the widening evidence that illustrates the importance of examining cognitive processes in their context. As we acknowledge, in practice, the development of mobile approaches brings with it fresh challenges, and will undoubtedly require innovation in paradigm design and analysis. If successful, however, the mobile cognition approach will offer novel insights in a range of areas, including understanding the cognitive processes underlying navigation through space and the role of attention during natural behavior. We argue that the development of real-world mobile cognition offers both increased ecological validity, and the opportunity to examine the interactions between perception, cognition and action-rather than examining each in isolation.