Estimating Intracranial EEG Signals at Missing Electrodes in Epileptic Networks

Gunnarsdottir, Kristin M.; Bulacio, Juan; González-Martínez, Jorge; Sarma, Sridevi V.

doi:10.1109/embc.2019.8856601

Cited by 6 publications

(6 citation statements)

References 9 publications

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“…Next, the new model is used to obtain new estimates of the missing signals recursively until it meets a user-specified stopping criterion. We have shown that this algorithm reliably estimates the missing signals (Gunnarsdottir et al, 2019).…”

Section: Algorithm -Estimating the Network Model And Missing Signalsmentioning

confidence: 91%

“…The tool constructs excellent estimates of missing electrode signals when the model A is known (Gunnarsdottir et al, 2019). However, in reality we do not know the model A.…”

Section: Estimation Of Initial Network Modelmentioning

confidence: 99%

“…In our previous work, we tested whether we could employ simple linear time-varying models to characterize invasive SEEG and ECoG data (Gunnarsdottir et al, 2019;Gunnarsdottir et al, 2017;Li et al, 2017), and then we used a reduced-order observer to simulate SEEG signals of unmeasured, "missing" electrodes. Fig.…”

Section: Preliminary Datamentioning

confidence: 99%

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Emerging roles of network analysis for epilepsy

et al. 2020

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In recent years there has been increasing interest in applying network science tools to EEG data. At the 2018 American Epilepsy Society conference in New Orleans, LA, the yearly session of the Engineering and Neurostimulation Special Interest Group focused on emerging, translational technologies to analyze seizure networks. Each speaker demonstrated practical examples of how network tools can be utilized in clinical care and provide additional data to help care for patients with intractable epilepsy. The groups presented advances using tools from functional connectivity, control theory, and graph theory to analyze human EEG data. These tools have great potential to augment clinical interpretation of EEG signals.

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Section: Algorithm -Estimating the Network Model And Missing Signalsmentioning

confidence: 91%

“…The tool constructs excellent estimates of missing electrode signals when the model A is known (Gunnarsdottir et al, 2019). However, in reality we do not know the model A.…”

Section: Estimation Of Initial Network Modelmentioning

confidence: 99%

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Emerging roles of network analysis for epilepsy

et al. 2020

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“…Consequently, experiments may be halted due to channel damage or data loss, prompting the use of interpolation or tensor methods to supplement missing information ( Sole-Casals et al, 2019 ). The expectation maximization-based Kalman filter can also estimate missing model signals ( Gunnarsdottir et al, 2019 ). The second approach is to optimize EEG acquisition location and quantity.…”

Section: Introductionmentioning

confidence: 99%

Design of virtual BCI channels based on informer

Sun

Zhang

2023

Front. Hum. Neurosci.

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The precision and reliability of electroencephalogram (EEG) data are essential for the effective functioning of a brain-computer interface (BCI). As the number of BCI acquisition channels increases, more EEG information can be gathered. However, having too many channels will reduce the practicability of the BCI system, raise the likelihood of poor-quality channels, and lead to information misinterpretation. These issues pose challenges to the advancement of BCI systems. Determining the optimal configuration of BCI acquisition channels can minimize the number of channels utilized, but it is challenging to maintain the original operating system and accommodate individual variations in channel layout. To address these concerns, this study introduces the EEG-completion-informer (EC-informer), which is based on the Informer architecture known for its effectiveness in time-series problems. By providing input from four BCI acquisition channels, the EC-informer can generate several virtual acquisition channels to extract additional EEG information for analysis. This approach allows for the direct inheritance of the original model, significantly reducing researchers’ workload. Moreover, EC-informers demonstrate strong performance in damaged channel repair and poor channel identification. Using the Informer as a foundation, the study proposes the EC-informer, tailored to BCI requirements and demanding only a small number of training samples. This approach eliminates the need for extensive computing units to train an efficient, lightweight model while preserving comprehensive information about target channels. The study also confirms that the proposed model can be transferred to other operators with minimal loss, exhibiting robust applicability. The EC-informer’s features enable original BCI devices to adapt to a broader range of classification algorithms and relax the operational requirements of BCI devices, which could facilitate the promotion of the use of BCI devices in daily life.

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“…However, because the information used for interpolation is local, this method may fall into a local trap of complex signals. Gunnarsdottir et al [12] proposed a method to complete the missing EEG signals under the assumption that some electrodes are missing. The EEG data were represented as sequences of linear time-variant models.…”

Section: Introductionmentioning

confidence: 99%

On the robustness of EEG tensor completion methods

Duan

Hao

Zhang

et al. 2021

Sci. China Technol. Sci.

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During the acquisition of electroencephalographic (EEG) signals, data may be missing or corrupted by noise and artifacts. To reconstruct the incomplete data, EEG signals are firstly converted into a three-order tensor (multi-dimensional data) of shape time × channel × trial. Then, the missing data can be efficiently recovered by applying a tensor completion method (TCM). However, there is not a unique way to organize channels and trials in a tensor, and different numbers of channels are available depending on the EEG setting used, which may affect the quality of the tensor completion results. The main goal of this paper is to evaluate the robustness of EEG completion methods with several designed parameters such as the ordering of channels and trials, the number of channels, and the amount of missing data. In this work, the results of completing missing data by several TCMs were compared. To emulate different scenarios of missing data, three different patterns of missing data were designed. Firstly, the amount of missing data on completion effects was analyzed, including the time lengths of missing data and the number of channels or trials affected by missing data. Secondly, the numerical stability of the completion methods was analyzed by shuffling the indices along channels or trials in the EEG data tensor. Finally, the way that the number of electrodes of EEG tensors influences completion effects was assessed by changing the number of channels. Among all the applied TCMs, the simultaneous tensor decomposition and completion (STDC) method achieves the best performance in providing stable results when the amount of missing data or the electrode number of EEG tensors is changed. In other words, STDC proves to be an excellent choice of TCM, since permutations of trials or channels have almost no influence on the complete results. The STDC method can efficiently complete the missing EEG signals. The designed simulations can be regarded as a procedure to validate whether or not a completion method is useful enough to complete EEG signals.

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Estimating Intracranial EEG Signals at Missing Electrodes in Epileptic Networks

Cited by 6 publications

References 9 publications

Emerging roles of network analysis for epilepsy

Emerging roles of network analysis for epilepsy

Design of virtual BCI channels based on informer

On the robustness of EEG tensor completion methods

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