Biology subjects

Ladouce, S.

Publications and source records attributed to Ladouce, S..

7 recordsLinked to original sources

bioRxiv2024

EEG-based Assessment of Long-Term Vigilance and Lapses of Attention using a User-Centered Frequency-Tagging Approach

ObjectiveSustaining vigilance over extended periods is crucial for many critical operations but remains challenging due to the cognitive resources required. Fatigue and other factors contribute to fluctuations in vigilance, causing attentional focus to drift from task-relevant information. Such lapses of attention, common in prolonged tasks, lead to decreased performance and missed critical information, with potentially serious consequences. Identifying physiological markers that predict inattention is key to developing preventive strategies. ApproachPrevious research has established electroencephalography (EEG) responses to periodic visual stimuli, known as steady-state visual evoked potentials (SSVEP), as sensitive markers of attention. In this study, we evaluated a minimally intrusive SSVEP-based approach for tracking vigilance in healthy participants (N = 16) during two sessions of a 45-minute sustained visual attention task (Mackworths clock task). A 14 Hz frequency-tagging flicker was either superimposed on the task or absent. Main resultsResults revealed that SSVEP responses were lower prior to lapses of attention, while other spectral EEG markers, such as frontal theta and parietal alpha activity, did not reliably distinguish between detected and missed attention probes. Importantly, the flicker did not affect task performance or participant experience. SignificanceThis non-intrusive frequency-tagging method provides a continuous measure of vigilance, effectively detecting attention lapses in prolonged tasks. It holds promise for integration into passive brain-computer interfaces, offering a practical solution for real-time vigilance monitoring in high-stakes settings like air traffic control or driving. Highlights- Fluctuations in the SSVEP response to a continuous frequency-tagging flicker presented throughout a 45-minutes long vigilance task was used to characterize lapses of attention - The frequency-tagging flicker superimposed to the task was designed to be minimally intrusive thanks to its low luminance and low contrast - Neither the user experience nor the task performance were altered by the presence of the frequency-tagging flicker - The SSVEP response outperformed measures of individual alpha peak and theta band activity in the distinction of attentional lapses (missed events) from successful target event identification - The present findings have implications for the design of Human-Computer Interfaces aimed at monitoring (in)attention during the performance of monotonous routines (e.g., surveillance, driving,...)

neuroscience↗

bioRxiv2024

Leveraging Grating-Based Flickers: A Leap Toward Practical, Visually Comfortable, and High-Performance Dry EEG Code-VEP BCI

AbstractO_ST_ABSPurposeC_ST_ABSReactive Brain-Computer Interfaces (rBCIs) typically rely on repetitive visual stimuli, which can strain the eyes and cause attentional distraction. To address these challenges, we propose a novel approach rooted in visual neuroscience to design visual Stimuli for Augmented Response (StAR). The StAR stimuli consist of small randomly-oriented Gabor or Ricker patches that optimize foveal neural response while reducing peripheral distraction. MethodsIn a factorial design study, 24 participants equipped with an 8-dry electrodes EEG system focused on series of target flickers presented under three formats: traditional Plain flickers, Gabor-based, or Ricker-based flickers. These flickers were part of a five-classes Code Visually Evoked Potentials (c-VEP) paradigm featuring low frequency, short, and aperiodic visual flashes. ResultsSubjective ratings revealed that Gabor and Ricker gratings were visually comfortable and nearly invisible in peripheral vision compared to plain flickers. Moreover, Gabor and Ricker-based textures achieved higher accuracy (93.6% and 96.3%, respectively) with only 88 seconds of calibration data, compared to plain flickers (65.6%). A follow-up online implementation of this experiment was conducted to validate our findings within the frame of naturalistic operations. During this trial, remarkable accuracies of 97.5% in a cued task and 94.3% in an asynchronous digicode task were achieved, with a mean decoding time as low as 1.68 seconds. ConclusionThis work demonstrates the potential to expand BCI applications beyond the lab by integrating visually unobtrusive systems with gel-free, low density EEG technology, thereby making BCIs more accessible and efficient. The datasets, algorithms, and BCI implementations are shared through open-access repositories.

neuroscience↗

bioRxiv2024

Frequency-tagging of spatial attention using periliminal flickers

Steady-State Visually Evoked Potentials (SSVEP) manifest as a sustained rhythmic activity that can be observed in surface electroencephalography (EEG) in response to periodic visual stimuli, commonly referred to as flickers. SSVEPs are widely used in fundamental cognitive neuroscience paradigms and Brain-Computer Interfaces (BCI) due to their robust and rapid onset. However, they have drawbacks related to the intrusive saliency of flickering visual stimuli, which may induce eye strain, cognitive fatigue, and biases in visual exploration. Previous findings highlighted the potential of altering features of flicker stimuli to improve user experience. In this study, we propose to reduce the amplitude modulation depth of flickering stimuli down to the individuals perceptual visibility threshold (periliminal) and below (subliminal). The stimulus amplitude modulation depth represents the contrast difference between the two alternating states of a flicker. A simple visual attention task where participants responded to the presentation of spatially-cued target stimuli (left and right) was used to assess the validity of such periliminal and subliminal frequency-tagging probes to capture spatial attention. The left and right sides of the screen, where target stimuli were presented, were covered by large flickers (13 and 15 Hz respectively). The amplitude modulation depth of these flickers was manipulated across three conditions: control, periliminal, and subliminal. The latter two levels of flickers amplitude modulation depth were defined through a perceptual visibility threshold protocol on a single-subject basis. Subjective feedback indicated that the use of periliminal and subliminal flickers substantially improved user experience. The present study demonstrates that periliminal and subliminal flickers evoked SSVEP responses that can be used to derive spatial attention in frequency-tagging paradigms. The single-trial classification of attended space (left versus right) based on SSVEP response reached an average accuracy of 81.1% for the periliminal and 58% for the subliminal conditions. These findings reveal the promises held by the application of inconspicuous flickers to both cognitive neuroscience research and BCI development. HighlightsO_LIFrequency-tagging of spatial attention can be achieved through the presentation of flickering visual stimuli (flickers) whose contrast is reduced down to the individuals perceptual visibility threshold revealing the potential of periliminal flickers as reliable frequency-tagging probes of spatial attention C_LIO_LIBelow this perceptual visibility threshold, the signal-to-noise ratio of SSVEP responses was not sufficient to reliably distinguish the field upon which participants directed their attention C_LIO_LIThe subliminal and periliminal flickers ameliorated the overall user experience and represent effective solutions to reduce bottom-up distraction, eye strain, and fatigue related to the presentation of flickering stimulation C_LIO_LIThe present findings have implications for the design of minimally intrusive frequency-tagging probes used within the frame of both fundamental cognitive neuroscience research and Brain Computer Interface C_LI

neuroscience↗

bioRxiv2023

Burst c-VEP Based BCI: Optimizing Stimulus Design for Enhanced Classification with Minimal Calibration Data and Improved User Experience

The Steady State Visual Evoked Potential (SSVEP) is a widely used technique in Brain-Computer Interface (BCI) research due to its high information transfer rate. However, this method has some limitations, including lengthy calibration time and visual fatigue. Recent studies have explored the use of code-modulated Visual Evoked Potentials (c-VEP) with aperiodic flickering visual stimuli as an alternative approach to address these issues. One advantage of c-VEP 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 can be further improved to achieve a higher signal-to-noise ratio, and shorten the selection time and the calibration process. In this study, we propose a novel type of code-VEP called "Burst c-VEP" that consists of brief presentations of aperiodic visual flashes at approximately 3Hz. 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 codes, burst c-VEP exhibits favorable properties to achieve high decoding performance using convolutional neural networks (CNN). We also explore the attenuation of visual stimuli contrast and intensity to further reduce the perceptual saliency of c-VEP. The proposed solutions were tested through an offline 4-classes c-VEP protocol involving 12 participants. Following a factorial design, participants were asked to focus on c-VEP targets whose pattern (burst and maximum-length sequence) and amplitude (100% or 40% amplitude depth modulation) were manipulated across experimental conditions. Firstly, the full amplitude burst c-VEP codes exhibited higher accuracy ranging from 90.5% (with 17.6s of calibration data) to 95.6% (with 52.8s of calibration data) than its m-sequence counterpart (71.4% to 85.0%). The mean selection time for the two types of codes (1.5s) compared favorably compared to existing studies. Secondly, our findings revealed that lowering the intensity of the stimuli barely decreased the accuracy of the burst to 94.2% accuracy while yielding a higher subjective visual user experience. Taken together, these results demonstrate the high potential of the proposed burst codes to advance BCI beyond the confines of the lab. The collected datasets, along with the proposed CNN architecture implementation, are shared through open-access repositories.

neuroscience↗

bioRxiv2021

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 1.6s when exclusively performing the checklist task. Accuracy was up to 98.5% with a mean reaction time of 2.5s 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.

neuroscience↗

bioRxiv2021

Capturing cognitive events embedded in the real-world using mobile EEG and Eye-Tracking

The study of cognitive processes underlying natural behaviours implies to depart from computerized paradigms and artificial experimental probes. The aim of the present study is to assess the feasibility of capturing neural markers of visual attention (P300 Event-Related Potentials) in response to objects embedded in a real-world environment. To this end, electroencephalography 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 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 co-efficients. Together these approaches uncovered P300 ERP responses in the library recordings. These findings high-light the relevance of scalable experimental designs, joint brain and body recordings and template-matching analyses to capture cognitive events during natural behaviours.

neuroscience↗

bioRxiv2020

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 years to develop in human infants, reflecting the huge cognitive demands associated with real-time dynamic planning and control of walking. Despite the importance of walking, surprisingly little is known about the neural and cognitive processes that support ambulatory motor control in humans. In particular, methodological limitations have, to date, largely prevented study of the neural processes involved in detecting and avoiding obstacles during walking. Here, using mobile EEG during real-world ambulatory obstacle avoidance, we captured the dynamic oscillatory response to changes in the environment. Time-frequency analysis of 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 correction 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, as previously thought. 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, our use of mobile EEG 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 Parkinsons disease.

neuroscience↗