Informing motor imagery-based brain-computer interface via neuronal avalanches

International audienceBrain-Computer Interfaces (BCIs) constitute a promising tool for communication and control. However, mastering non-invasive BCI systems remains a learned skill difficult to develop for a non-negligible proportion of users. Even though similarities have been shown between MI-based BCI learning and motor sequence learning our understanding of the dynamical processes, and their reflection on brain signals during BCI performance is still incomplete. In particular, whole-brain functional imaging is dominated by a 'bursty' dynamics, "neuronal avalanches", with fast, fat-tailed distributed, aperiodic perturbations spreading across the whole brain. Neuronal avalanches evolve over a manifold during resting-state, generating a rich functional connectivity dynamics. In this work, we evaluated to which extent neuronal avalanches can be used as a tool to differentiate mental states in the context of BCI experiments

Using critical dynamics to capture processes underlying Brain-Computer Interface performance

International audienceDespite being a promising tool for establishing direct communication and control from the brain over external effectors, non-invasive Brain-computer interface (BCI) systems yield suboptimal performance in ~30% of users. Measuring the dynamical features that are relevant to the execution of a task to improve BCI performance remains an open challenge. Functional imaging is dominated by aperiodic and scale-free perturbations called “Neuronal Avalanches”, spreading across the whole brain. The sequence of regions recruited by avalanches could convey the processes underpinning BCI performance. To test our hypothesis, we used source-reconstructed magnetoencephalography signals in a BCI framework, where 20 healthy subjects were compared in resting-state and while performing a motor imagery (MI) task, in order to track the dynamical features related to motor imagery. Each signal was z-scored over time. For each condition, we estimated the avalanche transition matrix (ATM), containing the probability that regions j would be active at time t+1, given region i was active at time t. We computed the difference between the ATMs obtained from the two conditions in each subject, and validated them via permutation analysis. Then, we correlated the difference of the probabilities to BCI performance. All the significantly different edges cluster upon the premotor areas, involved during the MI task, and the cunei, involved during visual processing. The differences in the probabilities associated with edges incident upon areas such as the right paracentral lobule and the caudal middle frontal bilaterally directly correlate with the BCI scores. Our results suggest that avalanches capture functionally-relevant processes which are of interest for alternative BCI designing. Future work will consist of investigating the use of avalanche transition matrices as potential alternative features for classification in BCI

Neuronal avalanches as potential features for Brain-Computer Interfaces

International audienceIntroduction:Brain-Computer Interfaces (BCIs) constitute a promising tool for communication and control. However, controlling non-invasive BCI remains a learned skill difficult to develop for 15-30% of users (Allison et al., 2010). This is mainly due to our poor understanding of the dynamic processes underlying BCI performance. Identifying the dynamical features that are relevant to the execution of a task could be the key to improving the diffusion of the BCI. Current features rely on local measurements without considering the interconnected nature of brain functioning. Whole-brain functional imaging is dominated by 'bursty' dynamics, aperiodic perturbations called "Neuronal Avalanches" spreading across the whole brain (Tagliazucchi et al., 2012). Neuronal avalanches spread preferentially across the white-matter bundles (Sorrentino et al., 2021), are affected by neurodegenerative diseases (Sorrentino, et al., 2021). Here we hypothesise that they could improve BCI classification performance. To test our hypothesis, we used source- reconstructed EEG signals in a BCI framework, where 20 subjects were compared in resting state and while performing a motor imagery task. We obtained the probabilities of each pair of regions being recruited sequentially in an avalanche (Sorrentino et al., 2021), and we used them as features to perform the classification.Methods:Here, we applied the neuronal avalanches approach to EEG data recorded during a BCI training session performed by a group of 20 healthy subjects (aged 27.5±4.0 years old, 12 men). They were instructed to control the vertical position of a moving cursor by modulating their neural activity via a motor imagery task (Corsi et al., 2020). The signal was then zscored (over time) and thresholded, and set to 1 when above threshold, and to zero otherwise (threshold =|3|). An avalanche was defined as starting when at least one region is above threshold, and as finishing when no region is active. For each avalanche, we have estimated an avalanche transition matrix (ATM) containing the probability that regions j would be active at time t+1, when region i was active at time t. Then, for each subject, we obtained one ATM per trial. We explored the performance of the ATMs in the decoding of the task. We compared the ATMs and the Common Spatial Patterns (CSP) approaches, widely used in the BCI domain (Blankertz et al., 2008). In each case, the output was classified using a Support Vector Machine (SVM). The classification scores for all pipelines were evaluated with an accuracy measurement using a random permutation cross-validator. To enable a statistical comparison of the CSP+SVM and the ATM+SVM approaches, 50 re-shuffling and splitting iterations were performed.Results:At the group-level (Fig 1, panel A), the classification performance was greater for ATM+SVM (0.80+/-0.10) with a reduced inter-subject variability as compared to CSP+SVM (0.75+/-0.15).For each subject, we ran t-tests to compare the 50 success rates obtained with CSP+SVM to the 50 success rates obtained using ATM+SVM. ATM+SVM yielded better classification accuracy than CSP+SVM for 12 subjects. In 4 subjects, CSPs yielded better accuracy than ATMs (Fig. 1, panel B). In 5 subjects, there was not any statistically significant difference between the two approaches. We examined the variability of the estimates across the splits. Steady estimates are important to train online algorithms and high variability might be partly responsible for ineffective training. We observed marginally higher intra-subject variability in CSP+SVM (median value of 0.07) as compared to ATM+SVM (median value of 0.06). In particular, the standard deviation across the split is smaller for the ATMs for most subjects.Conclusions:This first proof-of-concept study might capture part of the processes that were typically overlooked in a more oscillatory perspective. Our work paves the way to use aperiodic activities to improve classification performance and tailor BCI training programs.References:Allison, B. Z., & Neuper, C. (2010). Could Anyone Use a BCI? In D. S. Tan & A. Nijholt (Eds.), Brain-Computer Interfaces (pp. 35–54). Springer London. http://link.springer.com/chapter/10.1007/978-1-84996-272-8_3Blankertz, B., Tomioka, R., Lemm, S., Kawanabe, M. & Muller, K. (2008) Optimizing Spatial filters for Robust EEG533 Single-Trial Analysis. IEEE Signal Process. Mag. 25, 41–56.Corsi, M.-C., Chavez, M., Schwartz, D., George, N., Hugueville, L., Kahn, A. E., Dupont, S., Bassett, D. S.,& De Vico Fallani, F. (2020). Functional disconnection of associative cortical areas predicts performance during BCI training. NeuroImage, 209, 116500. https://doi.org/10.1016/ j.neuroimage.2019.116500Sorrentino, P., Seguin, C., Rucco, R., Liparoti, M., Troisi Lopez, E., Bonavita, S., Quarantelli, M., Sorrentino, G., Jirsa, V., & Zalesky, A. (2021). The structural connectome constrains fast brain dynamics. ELife, 10, e67400. https://doi.org/10.7554/eLife.67400Tagliazucchi, E., von Wegner, F., Morzelewski, A., Brodbeck, V., & Laufs, H. (2012). Dynamic BOLD functional connectivity in humans and its electrophysiological correlates. Frontiers in Human Neuroscience, 6(DEC). https://doi.org/10.3389/fnhum.2012.0033

Neuronal avalanches as potential features for Brain-Computer Interfaces

International audienceIntroduction:Brain-Computer Interfaces (BCIs) constitute a promising tool for communication and control. However, controlling non-invasive BCI remains a learned skill difficult to develop for 15-30% of users (Allison et al., 2010). This is mainly due to our poor understanding of the dynamic processes underlying BCI performance. Identifying the dynamical features that are relevant to the execution of a task could be the key to improving the diffusion of the BCI. Current features rely on local measurements without considering the interconnected nature of brain functioning. Whole-brain functional imaging is dominated by 'bursty' dynamics, aperiodic perturbations called "Neuronal Avalanches" spreading across the whole brain (Tagliazucchi et al., 2012). Neuronal avalanches spread preferentially across the white-matter bundles (Sorrentino et al., 2021), are affected by neurodegenerative diseases (Sorrentino, et al., 2021). Here we hypothesise that they could improve BCI classification performance. To test our hypothesis, we used source- reconstructed EEG signals in a BCI framework, where 20 subjects were compared in resting state and while performing a motor imagery task. We obtained the probabilities of each pair of regions being recruited sequentially in an avalanche (Sorrentino et al., 2021), and we used them as features to perform the classification.Methods:Here, we applied the neuronal avalanches approach to EEG data recorded during a BCI training session performed by a group of 20 healthy subjects (aged 27.5±4.0 years old, 12 men). They were instructed to control the vertical position of a moving cursor by modulating their neural activity via a motor imagery task (Corsi et al., 2020). The signal was then zscored (over time) and thresholded, and set to 1 when above threshold, and to zero otherwise (threshold =|3|). An avalanche was defined as starting when at least one region is above threshold, and as finishing when no region is active. For each avalanche, we have estimated an avalanche transition matrix (ATM) containing the probability that regions j would be active at time t+1, when region i was active at time t. Then, for each subject, we obtained one ATM per trial. We explored the performance of the ATMs in the decoding of the task. We compared the ATMs and the Common Spatial Patterns (CSP) approaches, widely used in the BCI domain (Blankertz et al., 2008). In each case, the output was classified using a Support Vector Machine (SVM). The classification scores for all pipelines were evaluated with an accuracy measurement using a random permutation cross-validator. To enable a statistical comparison of the CSP+SVM and the ATM+SVM approaches, 50 re-shuffling and splitting iterations were performed.Results:At the group-level (Fig 1, panel A), the classification performance was greater for ATM+SVM (0.80+/-0.10) with a reduced inter-subject variability as compared to CSP+SVM (0.75+/-0.15).For each subject, we ran t-tests to compare the 50 success rates obtained with CSP+SVM to the 50 success rates obtained using ATM+SVM. ATM+SVM yielded better classification accuracy than CSP+SVM for 12 subjects. In 4 subjects, CSPs yielded better accuracy than ATMs (Fig. 1, panel B). In 5 subjects, there was not any statistically significant difference between the two approaches. We examined the variability of the estimates across the splits. Steady estimates are important to train online algorithms and high variability might be partly responsible for ineffective training. We observed marginally higher intra-subject variability in CSP+SVM (median value of 0.07) as compared to ATM+SVM (median value of 0.06). In particular, the standard deviation across the split is smaller for the ATMs for most subjects.Conclusions:This first proof-of-concept study might capture part of the processes that were typically overlooked in a more oscillatory perspective. Our work paves the way to use aperiodic activities to improve classification performance and tailor BCI training programs.References:Allison, B. Z., & Neuper, C. (2010). Could Anyone Use a BCI? In D. S. Tan & A. Nijholt (Eds.), Brain-Computer Interfaces (pp. 35–54). Springer London. http://link.springer.com/chapter/10.1007/978-1-84996-272-8_3Blankertz, B., Tomioka, R., Lemm, S., Kawanabe, M. & Muller, K. (2008) Optimizing Spatial filters for Robust EEG533 Single-Trial Analysis. IEEE Signal Process. Mag. 25, 41–56.Corsi, M.-C., Chavez, M., Schwartz, D., George, N., Hugueville, L., Kahn, A. E., Dupont, S., Bassett, D. S.,& De Vico Fallani, F. (2020). Functional disconnection of associative cortical areas predicts performance during BCI training. NeuroImage, 209, 116500. https://doi.org/10.1016/ j.neuroimage.2019.116500Sorrentino, P., Seguin, C., Rucco, R., Liparoti, M., Troisi Lopez, E., Bonavita, S., Quarantelli, M., Sorrentino, G., Jirsa, V., & Zalesky, A. (2021). The structural connectome constrains fast brain dynamics. ELife, 10, e67400. https://doi.org/10.7554/eLife.67400Tagliazucchi, E., von Wegner, F., Morzelewski, A., Brodbeck, V., & Laufs, H. (2012). Dynamic BOLD functional connectivity in humans and its electrophysiological correlates. Frontiers in Human Neuroscience, 6(DEC). https://doi.org/10.3389/fnhum.2012.0033

Measuring neuronal avalanches to inform brain-computer interfaces

Summary: Large-scale interactions among multiple brain regions manifest as bursts of activations called neuronal avalanches, which reconfigure according to the task at hand and, hence, might constitute natural candidates to design brain-computer interfaces (BCIs). To test this hypothesis, we used source-reconstructed magneto/electroencephalography during resting state and a motor imagery task performed within a BCI protocol. To track the probability that an avalanche would spread across any two regions, we built an avalanche transition matrix (ATM) and demonstrated that the edges whose transition probabilities significantly differed between conditions hinged selectively on premotor regions in all subjects. Furthermore, we showed that the topology of the ATMs allows task-decoding above the current gold standard. Hence, our results suggest that neuronal avalanches might capture interpretable differences between tasks that can be used to inform brain-computer interfaces

Measuring Neuronal Avalanches to inform Brain-Computer Interfaces

International audienceLarge-scale interactions among multiple brain regions manifest as bursts of activations called neuronal avalanches, which reconfigure according to the task at hand and, hence, might constitute natural candidates to design brain-computer interfaces (BCI). To test this hypothesis, we used source-reconstructed magneto/electroencephalography, during resting state and a motor imagery task performed within a BCI protocol. To track the probability that an avalanche would spread across any two regions we built an avalanche transition matrix (ATM) and demonstrated that the edges whose transition probabilities significantly differed between conditions hinged selectively on premotor regions in all subjects. Furthermore, we showed that the topology of the ATMs allows task-decoding above the current gold standard. Hence, our results suggest that Neuronal Avalanches might capture interpretable differences between tasks that can be used to inform braincomputer interfaces

Measuring Heterogeneity in Job Finding Rates Among the Nonemployed Using Labor Force Status Histories

We use a novel approach to studying the heterogeneity in the job finding rates of the nonemployed by classifying the nonemployed by labor force status (LFS) histories, instead of using only one-month LFS. Job finding rates differ substantially across LFS histories: they are 25-30% among those currently out of the labor force (OLF) with recent employment, 10% among those currently OLF who have been unemployed but not employed in the previous two months, and 2% among those who have been OLF in all three previous months. This heterogeneity cannot be deduced from the one-month LFS or from one-month responses to the CPS survey questions about desire to work or recent search activity. We conclude that LFS histories is an important predictor of the nonemployed's job finding probability, particularly for those OLF