summary: Researchers are investigating the synchronization of brain regions to help control brain-machine interfaces.
sauce: UPF Barcelona
Just a few decades ago, the possibility of connecting your brain to a computer and translating neural signals into tangible actions would have seemed like science fiction.
However, in recent years there have been some scientific advances in this regard through the so-called Bran-Computer Interfaces (BCIs), which establish a communication bridge between the human brain and computers.
Recent research by UPF continues in this direction and makes new contributions in pursuing this desirable neuroscience milestone.
Findings from the UPF Center for Brain Cognition (CBC) are the subject of an article published in the journal on February 7. e NeuroMartín Esparza-Iaizzo (UPF and University College of London), Salvador Soto-Faraco (UPF and ICREA), Irene Vigué-Guix, entitled “Long Range Alpha Synchronization as a Control Signal for BCI: A Feasibility Study” Co-authored by (UPF), Mireia Torralba Cuello (UPF) and Manuela Ruzzoli (Basque Center for Cognitive Brain and Language).
One of the major current challenges in neuroscience is the identification of brain signals robust enough to control devices in real time. Neuroscientists have already realized devices that can be controlled by the mind, using only the activity of one or more areas of the brain.
However, it is not yet possible to do this via communication and synchronization of different areas of the brain.The article published by e Neuro Make a significant contribution to moving forward to achieve this goal.
Brain activity during a visuospatial attention task
This study is based on the analysis of brain activity in 10 people during a visuospatial attention task, performing up to 200 measurements per subject and relying on the concept of cross-laterality. What you see on the right side of your visual field is represented by the left hemisphere of your brain, and vice versa.
The level of brain signals, known as the alpha band, decreases in the hemisphere where the images we observe are represented. The researcher compares the alpha band level fluctuations to the balance plate. It is precisely on the side of the scale that is loaded with more weight that the plate descends more, but on the side that has less weight, it rises.
The same applies to the alpha band levels. It is in the hemisphere where the image is represented that the level of the alpha band drops the most and rises in the opposite hemisphere. It should be noted that the alpha band inhibits neuronal excitability and thus induces a relaxed state of the neuronal population. It is therefore not surprising that their levels are low in the hemispheres of the brain that process images.
Also note that the brain is divided into different regions that communicate by synchronizing neural fluctuations such as the alpha range. Precisely, one of the objectives of this study was to analyze whether long-range synchrony of alpha bands between brain regions exhibits a lateralization pattern, which has been confirmed by the authors of the study.
Specifically, attention to the right increases communication between the frontal and parietal regions of the left hemisphere, and attention to the left increases communication between these same regions in the right hemisphere. .
So far, signals from the alpha band, with which the frontal and parietal lobes of the brain communicate, can only be fully captured by aggregation of data from different measurements, rather than a single trial. Therefore, another aim of this study was to examine exactly how to capture these neural patterns at a single test level.
To achieve this, Principal Investigator Martín Esparza-Iaizzo explains that his work contributes from a methodological point of view. at the level of individual trials rather than aggregate data.”
However, he cautions that limitations of current electroencephalographs to achieve this goal have been pointed out:
“Current brain imaging has limitations in terms of spatial resolution and noise due to respiratory and cardiac activity, etc.”
However, the findings of this study provide a good basis for future research. I hope it will serve as a paradigm for future attempts. ”
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About this Neurotech Research News
author: press office
sauce: UPF Barcelona
contact: Press Office – UPF Barcelona
image: image is public domain
Original research: closed access.
“Long-range alpha synchronization as a control signal for BCI: a feasibility study” Martín Esparza-Iaizzo et al. e Neuro
overview
Long-range alpha synchronization as a control signal for BCI: a feasibility study
Changes in spatial attention are associated with variations in alpha-band (α, 8–14 Hz) activity, particularly interhemispheric imbalances. The underlying mechanisms are attributed to local α-synchrony, which regulates local inhibition of neural excitability, and fronto-parietal synchrony, which reflects long-range communication.
The direction-specific nature of this neural correlation opens up its potential as a control signal in brain-computer interfaces (BCIs). In the present study, we investigated whether long-range α-synchronization exhibits voluntary attentional orientation-dependent lateralization patterns, and whether these neural patterns can be picked up at the single-trial level to provide control signals for active BCI. I researched whether it could be provided. We collected electroencephalogram (EEG) data from a cohort of healthy adults (n = 10) while performing a covert visuospatial attention (CVSA) task.
The data show a lateralized pattern of α-band phase coupling between frontal and parieto-occipital regions after target presentation, replicating previous findings. However, this pattern was not evident during the cue-to-target orientation interval, which is the ideal timeframe for BCI. Moreover, deciphering the direction of attention on a trial-by-trial basis from queue-locked synchronization using support vector machines (SVMs) was chance-level.
Current findings suggest that EEG may fail to detect long-range alpha synchrony in attentional orientation on a single-trial basis, thus suggesting this metric as a reliable signal for BCI control. highlights the limitations of
