A Deep Learning Method for Improving the Classification Accuracy of SSMVEP-Based BCI

Gao, Zhong-Ke; Yuan, Tao; Zhou, Xinjun; Ma, Chao; Ma, Kai; Hui, Pan

doi:10.1109/tcsii.2020.2983389

Cited by 29 publications

(13 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…BCIs have drawn great attention and have been widely applied to various fields, including driver fatigue detection [3], [4], emotion recognition [5], [6], entertainment for healthy users [7], [8], and others [9], [10].…”

mentioning

confidence: 99%

Multiattention Adaptation Network for Motor Imagery Recognition

Chen

Gao

Yin

et al. 2022

IEEE Trans. Syst. Man Cybern, Syst.

Self Cite

View full text Add to dashboard Cite

Brain computer interface (BCI) based on motor imagery Electroencephalogram (EEG) has been widely used in various applications. Despite the previous efforts, the remained major challenges are effective feature extraction and timeconsuming calibration procedure. To address these issues, a novel Multi-Attention Adaptation Network integrating multiple attentions mechanism and transfer learning is proposed to classify the EEG signals. Firstly, the multi-attention layer is introduced to automatically capture the dominant brain regions relevant to mental tasks without incorporating any prior knowledge about the physiology. Then, a multi-attention convolutional neural network is employed to extract deep representation from raw EEG signals. Especially, a domain discriminator is applied to deep representation to reduce the differences between sessions for target subjects. The extensive experiments are conducted on three public EEG datasets (Dataset IIa and IIb of BCI Competition IV, High Gamma dataset), achieving the competitive performance with average classification accuracy of 81.48%, 82.54% and 93.97%, respectively. All the results outperform the state-of-theart algorithms demonstrate the effectiveness and robustness of the proposed method. Importantly, we confirm that it is easier and more appropriate to transfer the information from local brain regions than from the whole brain. This enhances the transfer ability of deep features and hence it improves the performance of BCI systems.

show abstract

mentioning

confidence: 99%

Multiattention Adaptation Network for Motor Imagery Recognition

Chen

Gao

Yin

et al. 2022

IEEE Trans. Syst. Man Cybern, Syst.

Self Cite

View full text Add to dashboard Cite

show abstract

“…Recently, data-driven methods based on deep learning were applied in dealing with EEG signals. For example, Gao et al [113] designed a convolutional neural network with long short-term memory (CNN-LSTM) architecture, which extracts the spectral, spatial as well as temporal features of SSVEPs in order to achieve the high classification performance. However, Ditthapron et al [114] stated that it is complicated and costly to collect a large number of EEG signals for training CNN-LSTM architecture, so a pre-trained model called event-related potential encoder network (ERPENet) was proposed to classify the attended and unattended event.…”

Section: A the Pre-trained Model For Eeg Classificationmentioning

confidence: 99%

Data Analytics in Steady-State Visual Evoked Potential-Based Brain–Computer Interface: A Review

et al. 2021

View full text Add to dashboard Cite

Electroencephalograph (EEG) has been widely applied for brain-computer interface (BCI) which enables paralyzed people to directly communicate with and control of external devices, due to its portability, high temporal resolution, ease of use and low cost. Of various EEG paradigms, steady-state visual evoked potential (SSVEP)-based BCI system which uses multiple visual stimuli (such as LEDs or boxes on a computer screen) flickering at different frequencies has been widely explored in the past decades due to its fast communication rate and high signalto-noise ratio. In this paper, we review the current research in SSVEP-based BCI, focusing on the data analytics that enables continuous, accurate detection of SSVEPs and thus high information transfer rate. The main technical challenges, including signal pre-processing, spectrum analysis, signal decomposition, spatial filtering in particular canonical correlation analysis and its variations, and classification techniques are described in this paper. Research challenges and opportunities in spontaneous brain activities, mental fatigue, transfer learning as well as hybrid BCI are also discussed.

show abstract

“…Gao et al introduced the deep learning (DL) method in a cart control system designed based on SSMVEP signals. The results show that the constructed deep learning model of a convolutional neural network with long and short-term memory (CNN-LSTM) is not only suitable for “EEG illiterate” people but can significantly improve the performance of “EEG illiterate” people (Gao et al, 2020 ). The deep learning method has been widely used in EEG, EMG, and other signals (Waytowich et al, 2018 ; Ravi et al, 2020 ; Zhang et al, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

A Zero-Padding Frequency Domain Convolutional Neural Network for SSVEP Classification

Gao

Zheng

Wang

et al. 2022

Front. Hum. Neurosci.

View full text Add to dashboard Cite

The brain-computer interface (BCI) of steady-state visual evoked potential (SSVEP) is one of the fundamental ways of human-computer communication. The main challenge is that there may be a nonlinear relationship between different SSVEP in other states. For improving the performance of SSVEP BCI, a novel CNN algorithm model is proposed in this study. Based on the discrete Fourier transform to calculate the signal's power spectral density (PSD), we perform zero-padding in the signal's time domain to improve its performance on the PSD and make it more refined. In this way, the frequency point interval in the PSD of the SSVEP is consistent with the minimum gap between the stimulation frequency. Combining the nonlinear transformation capabilities of CNN in deep learning, a zero-padding frequency domain convolutional neural network (ZPFDCNN) model is proposed. Extensive experiments based on the SSVEP dataset validate the effectiveness of our method. The study verifies that the proposed ZPFDCNN method can improve the effectiveness of the SSVEP-based high-speed BCI ITR. It has massive potential in the application of BCI.

show abstract

A Deep Learning Method for Improving the Classification Accuracy of SSMVEP-Based BCI

Cited by 29 publications

References 26 publications

Multiattention Adaptation Network for Motor Imagery Recognition

Multiattention Adaptation Network for Motor Imagery Recognition

Data Analytics in Steady-State Visual Evoked Potential-Based Brain–Computer Interface: A Review

A Zero-Padding Frequency Domain Convolutional Neural Network for SSVEP Classification

Contact Info

Product

Resources

About