RT-NET: real-time reconstruction of neural activity using high-density electroencephalography

Guarnieri, Roberto; Zhao, Mingqi; Taberna, Gaia Amaranta; Ganzetti, Marco; Swinnen, Stephan P.; Mantini, Dante

doi:10.1007/s12021-020-09479-3

Cited by 12 publications

(9 citation statements)

References 80 publications

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“…Our network interpretation is necessarily constrained by the limitations of our EEG setup, as more detailed head models, with higher density recordings would be required for a finer spatial source localization 59 . More refined EEG measurements could also provide access to subcortical regions 60 , which are known to be involved in the processing of reward 61 and might be recruited to generate a sense of valence for social pressure.…”

Section: Limitations and Future Workmentioning

confidence: 99%

Behavioural and Neural Correlates of Social Pressure during Decision-Making of Precision Reaches

Cos

Deco

Gilson³

2022

Preprint

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Although reward exerts a crucial influence on human behaviour, internal processes are equally as important to purposely analyse and integrate sensory information into behavioural drives. Extensive research has identified pre-frontal cortical areas as the central brain subsystem processing reward, and pre-motor and motor cortices the mergers of that influence onto action planning and control. However, few studies have analyzed the brain distribution of contextual variables, such as social pressure, and their influence on internal processes and on behavior. To this end, here we designed a decision-making task between precision reachings, in which we varied a context of social pressure, and assessed its influence on our participants’ kinematics, pupil diameter and electro-encephalography. Our results show that social pressure exerts a non-trivial bias on the participants’ behaviour, influencing reaching precision, movement parameters and decision-making policies. Furthermore, these modulations co-varied consistently with pupil diameter and the activation of specific cortical sources, portraying a distributed brain network for social pressure that includes prefrontal, motor and occipital cortical areas. Overall, this is consistent with a broad physiological process, transforming an implicit variable, such as social pressure, onto an internal bias that energises and influences decision-making in a principled fashion, complementary to explicit reward.

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Section: Limitations and Future Workmentioning

confidence: 99%

Behavioural and Neural Correlates of Social Pressure during Decision-Making of Precision Reaches

Cos

Deco

Gilson³

2022

Preprint

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“…The low computation time may permit its use in online modality not only with low-density but also high-density EEG recordings. Importantly, the compatibility with high-density EEG may enable the use of AC-REG for source-level closed-loop neuromodulation (Guarnieri et al 2020).…”

Section: Discussionmentioning

confidence: 99%

“…Notably, AC-REG permitted the simultaneous processing of up to 256 channels (figure 4) with relatively low processing time, theoretically compatible with its use in quasi real-time applications. It should be considered that the processing of all EEG channels simultaneously can enable source localization in real-time (Guarnieri et al 2020).…”

Section: Analysis Of Methods Performancementioning

confidence: 99%

A computationally efficient method for the attenuation of alternating current stimulation artifacts in electroencephalographic recordings

et al. 2020

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Objective. Recent studies suggest that the use of noninvasive closed-loop neuromodulation combining electroencephalography (EEG) and transcranial alternating current stimulation (tACS) may be a promising avenue for the treatment of neurological disorders. However, the attenuation of tACS artifacts in EEG data is particularly challenging, and computationally efficient methods are needed to enable closed-loop neuromodulation experiments. Here we introduce an original method to address this methodological issue. Approach. Our alternating current regression (AC-REG) method is an adaptive (time-varying) spatial filtering method. It relies on a data buffer of preset size, on which principal component analysis (PCA) is applied. The resulting components are used to build a spatial filter capable of regressing periodic signals in phase with the stimulation. PCA is performed each time that a new sample enters the buffer, such that the spatial filter can be continuously updated and applied to the EEG data. Main results. The AC-REG accuracy in terms of tACS artifact attenuation was assessed using simulated and real EEG data. Alternative offline processing techniques, such as the superimposition of moving averages (SMA) and the Helfrich method (HeM), were used as benchmark. Using simulations, we found that AC-REG can yield a more reliable reconstruction of the stimulation signal for any frequency between 1 and 80 Hz. Analysis of real EEG data of 18 healthy volunteers showed that AC-REG was able to better recover hidden neural activity as compared to SMA and HeM. Also, significantly higher correlations between power spectrum densities in tACS on and off conditions, respectively, were obtained using AC-REG (r = 0.90) than using SMA (r = 0.80) and HeM (r = 0.86). Significance. Thanks to its low computational complexity, the AC-REG method can be employed in noninvasive closed-loop neuromodulation experiments, with potential applications both in healthy individuals and in neurological patients.

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“…e representative algorithms include maximum a posterior probability (MAP), local indirect maximum posterior probability method, global indirect maximum posterior probability method, and hybrid MAP-POCS algorithm [14][15][16].…”

Section: Related Workmentioning

confidence: 99%

A Music Curriculum Integration and Reconstruction Model Based on Advanced Iterative Reconstruction Algorithm

Zeng

2022

Scientific Programming

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Music curriculum fusion and super-resolution reconstruction based on musical elements have gradually attracted the attention of researchers. The traditional music element fusion and reconstruction algorithm is based on the fusion and reconstruction of all the pixel information of the source music element, which has the problem of high time and space complexity. Based on the advanced iterative reconstruction theory, this paper uses the measurement matrix to measure the dimensionality of the music signal, compresses the music element data while acquiring the music elements, reduces the sampling frequency, reduces the sampling amount of the music element data, and greatly reduces the data. It solves the problem of spatial resolution reduction caused by degradation and the problem of music element feature extraction, classification, and identification and can obtain more music element features and detailed parameters. Aiming at the back-projection algorithm of musical symbol filtering, the advanced iterative reconstruction algorithm of musical symbol filtering of a triangular line array is simulated. The experimental results show that the feasibility analysis factor of the scheme reaches 0.917, and the running time of the reconstruction algorithm is reduced to 0.131 s, which promotes a large amount of data in music curriculum element fusion and super-resolution reconstruction.

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RT-NET: real-time reconstruction of neural activity using high-density electroencephalography

Cited by 12 publications

References 80 publications

Behavioural and Neural Correlates of Social Pressure during Decision-Making of Precision Reaches

Behavioural and Neural Correlates of Social Pressure during Decision-Making of Precision Reaches

A computationally efficient method for the attenuation of alternating current stimulation artifacts in electroencephalographic recordings

A Music Curriculum Integration and Reconstruction Model Based on Advanced Iterative Reconstruction Algorithm

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