Correlation-Based Regularized Common Spatial Patterns for Classification of Motor Imagery EEG Signals

Ghanbar, Khatereh Darvish; Rezaii, Tohid Yousefi; Tinati, Mohammad Ali; Farzamnia, Ali

doi:10.1109/iraniancee.2019.8786490

Cited by 3 publications

(2 citation statements)

References 14 publications

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“…Fundamentally, EEG signals in BCIs are collected via electrode channels, with the potential and timeline of each channel forming a one-dimensional (1D) time series. MI can be classified using common spatial pattern (CSP) or independent component analysis (ICA) methods ( Ghanbar et al, 2019 ; Wu et al, 2020 ), while the wavelet transform (WT) ( Geng et al, 2022 ) enables more detailed EEG analysis and application. In recent years, the rapid development of hardware has led to the emergence of deep learning, which has naturally combined with BCI.…”

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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Section: Introductionmentioning

confidence: 99%

Design of virtual BCI channels based on informer

Sun

Zhang

2023

Front. Hum. Neurosci.

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“…In this work, our focus is on obtaining optimized spatial filters using the proposed method for MI signals and all the simulation steps are done using this kind of signals as inputs. It should be noted that the primary results of this paper have been published in a conference paper [27].…”

Section: Introductionmentioning

confidence: 99%

Correlation-based common spatial pattern (CCSP): A novel extension of CSP for classification of motor imagery signal

et al. 2021

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Common spatial pattern (CSP) is shown to be an effective pre-processing algorithm in order to discriminate different classes of motor-based EEG signals by obtaining suitable spatial filters. The performance of these filters can be improved by regularized CSP, in which available prior information is added in terms of regularization terms into the objective function of conventional CSP. Variety of prior information can be used in this way. In this paper, we used time correlation between different classes of EEG signal as the prior information, which is clarified similarity between different classes of signal for regularizing CSP. Furthermore, the proposed objective function can be easily extended to more than two-class problems. We used three different standard datasets to evaluate the performance of the proposed method. Correlation-based CSP (CCSP) outperformed original CSP as well as the existing regularized CSP, Principle Component Cnalysis (PCA) and Fisher Discriminate Analysis (FDA) in both two-class and multi-class scenarios. The simulation results showed that the proposed method outperformed conventional CSP by 6.9% in 2-class and 2.23% in multi-class problem in term of mean classification accuracy.

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