Time window and frequency band optimization using regularized neighbourhood component analysis for Multi-View Motor Imagery EEG classification

Malan, Nitesh Singh; Sharma, Shiru

doi:10.1016/j.bspc.2021.102550

Cited by 28 publications

(8 citation statements)

References 49 publications

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“…Exploring the time-frequencychannel combination for the whole task will be a time-consuming process. Therefore, future work can try to use parameter selection methods such as regularized neighborhood component analysis [46], Riemannian distance based channel selection [47], Granger causality channel selection [48] to preoptimize the parameter range based on increasing the number of parameter combinations. Second, the proposed RAVE algorithm uses the predicted value voting method to determine the final output.…”

Section: Discussionmentioning

confidence: 99%

Riemannian geometric and ensemble learning for decoding cross-session motor imagery electroencephalography signals

Pan,

Wang,

et al. 2023

J. Neural Eng.

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Objective. Brain–computer interfaces (BCIs) enable a direct communication pathway between the human brain and external devices, without relying on the traditional peripheral nervous and musculoskeletal systems. Motor imagery (MI)-based BCIs have attracted significant interest for their potential in motor rehabilitation. However, current algorithms fail to account for the cross-session variability of electroencephalography signals, limiting their practical application. Approach. We proposed a Riemannian geometry-based adaptive boosting and voting ensemble (RAVE) algorithm to address this issue. Our approach segmented the MI period into multiple sub-datasets using a sliding window approach and extracted features from each sub-dataset using Riemannian geometry. We then trained adaptive boosting (AdaBoost) ensemble learning classifiers for each sub-dataset, with the final BCI output determined by majority voting of all classifiers. We tested our proposed RAVE algorithm and eight other competing algorithms on four datasets (Pan2023, BNCI001-2014, BNCI001-2015, BNCI004-2015). Main results. Our results showed that, in the cross-session scenario, the RAVE algorithm outperformed the eight other competing algorithms significantly under different within-session training sample sizes. Compared to traditional algorithms that involved a large number of training samples, the RAVE algorithm achieved similar or even better classification performance on the datasets (Pan2023, BNCI001-2014, BNCI001-2015), even when it did not use or only used a small number of within-session training samples. Significance. These findings indicate that our cross-session decoding strategy could enable MI-BCI applications that require no or minimal training process.

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Section: Discussionmentioning

confidence: 99%

Riemannian geometric and ensemble learning for decoding cross-session motor imagery electroencephalography signals

Pan,

Wang,

et al. 2023

J. Neural Eng.

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“…In the pre-processing step, standing out strategies consist of time segmentation of trials, a-priori selection of channels over the sensorimotor area, and bandpass filtering often done over multiple bands [102]. The wavelet transform is frequently exploited too [118,119]. Moreover, among studies using CSP and SVM, least absolute shrinkage and selection operator (LASSO) [120] was also used to select the most discriminating features.…”

Section: Most Performing Approachesmentioning

confidence: 99%

“…: BCI competition, GS: Giga Science. Overlapped time windows, dual tree complex wavelet transform, in [4,8], [8,16] and [16,32] Hz CSP Regularized neighborhood component analysis SVM BCIc.IV-2a (B), BCIc.IV-2b, BCIc.III-3a (B) [118] BP in [8,30] Hz…”

Section: Most Performing Approachesmentioning

confidence: 99%

How to successfully classify EEG in motor imagery BCI: a metrological analysis of the state of the art

et al. 2022

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Objective. Processing strategies are analysed with respect to the classification of electroencephalographic signals related to brain-computer interfaces based on motor imagery. A review of literature is carried out to understand the achievements in motor imagery classification, the most promising trends, and the challenges in replicating these results. Main focus is placed on performance by means of a rigorous metrological analysis carried out in compliance with the international vocabulary of metrology. Hence, classification accuracy and its uncertainty are considered, as well as repeatability and reproducibility. Approach. The paper works included in the review concern the classification of electroencephalographic signals in motor-imagery- based brain-computer interfaces. Article search was carried out in accordance with the PRISMA standard and 89 studies were included. Main results. Statistically-based analyses show that brain-inspired approaches are increasingly proposed, and that these are particularly successful in discriminating against multiple classes. Notably, many proposals involve convolutional neural networks. Instead, classical machine learning approaches are still effective for binary classifications. Many proposals combine common spatial pattern, least absolute shrinkage and selection operator, and support vector machines. Regarding reported classification accuracies, performance above the upper quartile is in the 85 % to 100 % range for the binary case and in the 83 % to 93 % range for multi-class one. Associated uncertainties are up to 6 % while repeatability for a predetermined dataset is up to 8 %. Reproducibility assessment was instead prevented by lack of standardization in experiments. Significance. By relying on the analysed studies, the reader is guided towards the development of a successful processing strategy as a crucial part of a brain-computer interface. Moreover, it is suggested that future studies should extend these approaches on data from more subjects and with custom experiments, even by investigating online operation. This would also enable the quantification of results reproducibility.

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“…In our work, we use bandpass filtering by multiple time windows and frequency bands [52], then choose the top G time-frequency bands for MI classification according to the calculated Riemannian distances. Moreover, we extract the Riemannian features, which are obtained by mapping the covariance matrices to the tangent space.…”

Section: Feature Extraction Combining Dtfrtsmentioning

confidence: 99%

Riemannian distance based channel selection and feature extraction combining discriminative time-frequency bands and Riemannian tangent space for MI-BCIs

Qu¹,

Jin²,

Xu³

et al. 2022

J. Neural Eng.

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Objective. Motor imagery-based brain computer interfaces (MI-BCIs) have been widely researched because they do not demand external stimuli and have a high degree of maneuverability. In most scenarios, superabundant selected channels, fixed time windows, and frequency bands would certainly affect the performance of MI-BCIs due to the neurophysiological diversities between different individuals. In this study, we tended to effectively use the Riemannian geometry of spatial covariance matrix to extract more robust features and thus enhance the decoding efficiency. Approach. First, we propose a Riemannian distance-based EEG channel selection method (RDCS), which preliminarily reduces the information redundancy in the first stage. Second, we extract discriminative Riemannian Tangent Space features of EEG signals of selected channels from the most discriminant time-frequency bands (DTFRTS) to further enhance decoding accuracy for MI-BCIs. Finally, we trained a support vector machine (SVM) model with a linear kernel to classify our extracted discriminative Riemannian features and evaluated our proposed method using publicly available BCI Competition Ⅳ dataset Ⅰ (DS1) and Competition Ⅲ dataset Ⅲa (DS2). Main results. The experimental results showed that the average classification accuracy with the selected 10‐channel EEG signals of our method is 88.1% and 91.6% in DS1 and DS2 respectively. The average improvements are 24.3% & 27.1% on DS1 and 4.4% & 14.2% on DS2 for 10 & 20 selected channels, respectively. Significance. These results showed that our proposed method is a promising candidate for performance improvement of MI-BCIs.

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Time window and frequency band optimization using regularized neighbourhood component analysis for Multi-View Motor Imagery EEG classification

Cited by 28 publications

References 49 publications

Riemannian geometric and ensemble learning for decoding cross-session motor imagery electroencephalography signals

Riemannian geometric and ensemble learning for decoding cross-session motor imagery electroencephalography signals

How to successfully classify EEG in motor imagery BCI: a metrological analysis of the state of the art

Riemannian distance based channel selection and feature extraction combining discriminative time-frequency bands and Riemannian tangent space for MI-BCIs

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