Extraction of high-frequency SSVEP for BCI control using iterative filtering based empirical mode decomposition

Hsu, Chuan Chih; Yeh, Chia Lung; Lee, Wai Keung; Hsu, Hui-Yu; Shyu, Kuo-Kai; Li, Lieber Po Hung; Wu, Tien Yu; Lee, Po Lei

doi:10.1016/j.bspc.2020.102022

Cited by 20 publications

(6 citation statements)

References 32 publications

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“…(2) Which BCI paradigm suits or satisfies a specific user Although the current personalized BCI research allows specific users to freely choose existing paradigms [11] and automatically adjust the established paradigm through neural feedback [18], the existing paradigms (such as the MI paradigm, P300 paradigm, and SSVEP paradigm) have inherent defects. For example, Chuan-Chih Hsu et al proposed that low-frequency SSVEP would lead to the risk of photosensitive epilepsy [100]. The P300 experiment took too long, and MI had high requirements of user imagination.…”

Section: Challenges Faced By Personalized Bcimentioning

confidence: 99%

Personalized Brain–Computer Interface and Its Applications

Gong

Nan

et al. 2022

JPM

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Brain–computer interfaces (BCIs) are a new technology that subverts traditional human–computer interaction, where the control signal source comes directly from the user’s brain. When a general BCI is used for practical applications, it is difficult for it to meet the needs of different individuals because of the differences among individual users in physiological and mental states, sensations, perceptions, imageries, cognitive thinking activities, and brain structures and functions. For this reason, it is necessary to customize personalized BCIs for specific users. So far, few studies have elaborated on the key scientific and technical issues involved in personalized BCIs. In this study, we will focus on personalized BCIs, give the definition of personalized BCIs, and detail their design, development, evaluation methods and applications. Finally, the challenges and future directions of personalized BCIs are discussed. It is expected that this study will provide some useful ideas for innovative studies and practical applications of personalized BCIs.

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Section: Challenges Faced By Personalized Bcimentioning

confidence: 99%

Personalized Brain–Computer Interface and Its Applications

Gong

Nan

et al. 2022

JPM

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“…Therefore, evaluating the RES of various subject-independent algorithms would be one of the promising topics that we plan to pursue in future studies. Recently, SSVEPbased BCIs employing high-frequency bands have been actively studied because they could effectively reduce visual fatigue, compared to those employing low-and mid-frequency bands (Liang et al, 2019;Hsu et al, 2020). Despite its improved usability, however, its performance was not as high as that of the low-and mid-frequency SSVEP-based BCIs.…”

Section: Discussionmentioning

confidence: 99%

Influence of the Number of Channels and Classification Algorithm on the Performance Robustness to Electrode Shift in Steady-State Visual Evoked Potential-Based Brain-Computer Interfaces

Kim

2021

Front. Neuroinform.

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There remains an active investigation on elevating the classification accuracy and information transfer rate of brain-computer interfaces based on steady-state visual evoked potential. However, it has often been ignored that the performance of steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) can be affected through the minor displacement of the electrodes from their optimal locations in practical applications because of the mislocation of electrodes and/or concurrent use of electroencephalography (EEG) devices with external devices, such as virtual reality headsets. In this study, we evaluated the performance robustness of SSVEP-based BCIs with respect to the changes in electrode locations for various channel configurations and classification algorithms. Our experiments involved 21 participants, where EEG signals were recorded from the scalp electrodes densely attached to the occipital area of the participants. The classification accuracies for all the possible cases of electrode location shifts for various channel configurations (1–3 channels) were calculated using five training-free SSVEP classification algorithms, i.e., the canonical correlation analysis (CCA), extended CCA, filter bank CCA, multivariate synchronization index (MSI), and extended MSI (EMSI). Then, the performances of the BCIs were evaluated using two measures, i.e., the average classification accuracy (ACA) across the electrode shifts and robustness to the electrode shift (RES). Our results showed that the ACA increased with an increase in the number of channels regardless of the algorithm. However, the RES was enhanced with an increase in the number of channels only when MSI and EMSI were employed. While both ACA and RES values for the five algorithms were similar under the single-channel condition, both ACA and RES values for MSI and EMSI were higher than those of the other algorithms under the multichannel (i.e., two or three electrodes) conditions. In addition, EMSI outperformed MSI when comparing the ACA and RES values under the multichannel conditions. In conclusion, our results suggested that the use of multichannel configuration and employment of EMSI could make the performance of SSVEP-based BCIs more robust to the electrode shift from the optimal locations.

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“…Steady-state visually evoked potential is an exogenous electroencephalography (EEG) rhythmic signal induced by periodic flashing stimulus 1,2 . Compared with the EEG rhythmic components induced by thinking activities, SSVEP has good signal-to-noise ratio (SNR) and is easy to detect 3,4 , so it has been widely concerned and applied in the field of brain computer interface and brain cognition. The accurate analysis and acquisition of frequency and spatial distribution characteristics of SSVEP are two key factors for its effective application 5 .…”

Section: Introductionmentioning

confidence: 99%

Analysis of SSVEP frequency-spatial characteristics based on EhythmNet and its application in identity recognition

Ma,

Zhang,

et al. 2024

Fifteenth International Conference on Signal Processing Systems (ICSPS 2023)

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The frequency space characteristic is an important characteristic of steady-state visually evoked potential (SSVEP). The frequency space characteristic of traditional SSVEP is generally obtained by analyzing the power spectrum of EEG of scalp electrodes, but it is vulnerable to noise interference and data quality. This article proposes a new SSVEP frequency space feature analysis method. The proposed method consists of two main parts: 1) A shallow convolutional neural network, called EhythmNet in this paper, is designed for EEG rhythm analysis; 2) Based on multi channels EEG data obtained from a set of stimulus frequencies, single channel EEG of SSVEP dominant electrodes (such as Oz) was used as the training data for EhythmNet, and data from other channels were used as the test set to obtain the recognition rate of eachchannel, namely single channel recognition rate (SCA). The experimental results indicate that SCAs can accurately reflect the spatial distribution of SSVEP in the scalp electrode, and it is found that the spatial distribution of SCA has good individual stability and differences between individuals. In order to verify this feature, an identity recognition test based on SCA was conducted in the article, and more than 98.5% of the recognition results were achieved.

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Extraction of high-frequency SSVEP for BCI control using iterative filtering based empirical mode decomposition

Cited by 20 publications

References 32 publications

Personalized Brain–Computer Interface and Its Applications

Personalized Brain–Computer Interface and Its Applications

Influence of the Number of Channels and Classification Algorithm on the Performance Robustness to Electrode Shift in Steady-State Visual Evoked Potential-Based Brain-Computer Interfaces

Analysis of SSVEP frequency-spatial characteristics based on EhythmNet and its application in identity recognition

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