Spatio-Spectral CCA (SS-CCA): A novel approach for frequency recognition in SSVEP-based BCI

Cherloo, Mohammad Norizadeh; Amiri, Homa Kashefi; Daliri, Mohammad Reza

doi:10.1016/j.jneumeth.2022.109499

Cited by 11 publications

(2 citation statements)

References 33 publications

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“…The SSVEPs-based early detection for glaucoma requires effective analysis methods for recognizing stimulation frequencies. Traditional analysis methods for SSVEP signal can be mainly divided into two categories: spatial-spectral-temporal (SST) based method ( Mora-Cortes et al, 2018 ; Salelkar and Ray, 2020 ; Zhang et al, 2020 ) and canonical correlation analysis (CCA) based method ( Liu Q. et al, 2020 ; Cherloo et al, 2022 ; Ma et al, 2022 ). The former tries to extract SST features from the EEG signal and use them to execute classification tasks.…”

Section: Introductionmentioning

confidence: 99%

GDNet-EEG: An attention-aware deep neural network based on group depth-wise convolution for SSVEP stimulation frequency recognition

Zhang

Cheng

et al. 2023

Front. Neurosci.

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BackgroundSteady state visually evoked potentials (SSVEPs) based early glaucoma diagnosis requires effective data processing (e.g., deep learning) to provide accurate stimulation frequency recognition. Thus, we propose a group depth-wise convolutional neural network (GDNet-EEG), a novel electroencephalography (EEG)-oriented deep learning model tailored to learn regional characteristics and network characteristics of EEG-based brain activity to perform SSVEPs-based stimulation frequency recognition.MethodGroup depth-wise convolution is proposed to extract temporal and spectral features from the EEG signal of each brain region and represent regional characteristics as diverse as possible. Furthermore, EEG attention consisting of EEG channel-wise attention and specialized network-wise attention is designed to identify essential brain regions and form significant feature maps as specialized brain functional networks. Two publicly SSVEPs datasets (large-scale benchmark and BETA dataset) and their combined dataset are utilized to validate the classification performance of our model.ResultsBased on the input sample with a signal length of 1 s, the GDNet-EEG model achieves the average classification accuracies of 84.11, 85.93, and 93.35% on the benchmark, BETA, and combination datasets, respectively. Compared with the average classification accuracies achieved by comparison baselines, the average classification accuracies of the GDNet-EEG trained on a combination dataset increased from 1.96 to 18.2%.ConclusionOur approach can be potentially suitable for providing accurate SSVEP stimulation frequency recognition and being used in early glaucoma diagnosis.

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Section: Introductionmentioning

confidence: 99%

GDNet-EEG: An attention-aware deep neural network based on group depth-wise convolution for SSVEP stimulation frequency recognition

Zhang

Cheng

et al. 2023

Front. Neurosci.

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“…A prevalent traditional recognition method is the canonical correlation analysis (CCA), which tries to find a pair of projection vectors to obtain maximum correlation coefficients between the test signals and reference signals [4]. Till now, there are many improved algorithms based on CCA, such as individual template canonical correlation analysis (IT-CCA) [5], multiway canonical correlation analysis [6], filter bank canonical correlation analysis [7], spatio-spectral canonical correlation analysis [8], etc. CCA and its variant methods can achieve satisfactory results on the SSVEP-based BCI system that has a small number of classification targets, but the performance drops rapidly when was implemented in the system has a large number of classification targets [9].…”

Section: Introductionmentioning

confidence: 99%

An efficient CNN-LSTM network with spectral normalization and label smoothing technologies for SSVEP frequency recognition

2022

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Objective. Steady-state visual evoked potentials(SSVEPs) based braincomputer interface(BCI) has received great interests owing to the high information transfer rate(ITR) and available large number of targets. However, the performance of frequency recognition methods heavily depends on the amount of the calibration data for intra-subject classiﬁcation. Some research adopted the deep learning(DL) algorithm to conduct the inter-subject classiﬁcation, which could reduce the calculation procedure, but the performance still has large room to improve compared with the intra-subject classiﬁcation. Approach. To address these issues, we proposed an eﬃcient SSVEP DL NETwork (termed SSVEPNET) based on 1D convolution and long short-term memory (LSTM) module. To enhance the performance of SSVEPNT, we adopted the spectral normalization and label smoothing technologies during implementing the network architecture. We evaluated the SSVEPNET and compared it with other methods for the intra- and inter-subject classiﬁcation under diﬀerent conditions, i.e., two datasets, two time-window lengths (1 s and 0.5 s), three sizes of training data. Main results. Under all the experimental settings, the proposed SSVEPNET achieved the highest average accuracy for the intra- and inter-subject classiﬁcation on the two SSVEP datasets, when compared with other traditional and DL baseline methods. Signif icance. The extensive experimental results demonstrate that the proposed DL model holds promise to enhance frequency recognition performance in SSVEP-based BCIs. Besides, the mixed network structures with CNN and LSTM, and the spectral normalization and label smoothing could be useful optimization strategies to design eﬃcient models for EEG data.

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Forty-Class SSVEP-Based Brain-Computer Interface to Inter-subject Using Complex Spectrum Features

Flores,

Attux,

Carvalho

2024

IFMBE Proceedings

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Spatio-Spectral CCA (SS-CCA): A novel approach for frequency recognition in SSVEP-based BCI

Cited by 11 publications

References 33 publications

GDNet-EEG: An attention-aware deep neural network based on group depth-wise convolution for SSVEP stimulation frequency recognition

GDNet-EEG: An attention-aware deep neural network based on group depth-wise convolution for SSVEP stimulation frequency recognition

An efficient CNN-LSTM network with spectral normalization and label smoothing technologies for SSVEP frequency recognition

Forty-Class SSVEP-Based Brain-Computer Interface to Inter-subject Using Complex Spectrum Features

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