Steady-state Visual Evoked Potential Frequency Research Articles

Assessing the influence of visual stimulus properties on steady-state visually evoked potentials and pupil diameter

Steady-State Visual Evoked Potentials (SSVEPs) are brain responses measurable via electroencephalography (EEG) in response to continuous visual stimulation at a constant frequency. SSVEPs have been instrumental in advancing our understanding of human vision and attention, as well as in the development of brain-computer interfaces (BCIs). Ongoing questions remain about which type of visual stimulus causes the most potent SSVEP response. The current study investigated the effects of color, size, and flicker frequency on the signal-to-noise ratio of SSVEPs, complemented by pupillary light reflex measurements obtained through an eye-tracker. Six participants were presented with visual stimuli that differed in terms of color (white, red, green), shape (circles, squares, triangles), size (10,000 to 30,000 pixels), flicker frequency (8 to 25 Hz), and grouping (one stimulus at a time versus four stimuli presented in a 2×2 matrix to simulate a BCI). The results indicated that larger stimuli elicited stronger SSVEP responses and more pronounced pupil constriction. Additionally, the results revealed an interaction between stimulus color and flicker frequency, with red being more effective at lower frequencies and white at higher frequencies. Future SSVEP research could focus on the recommended waveform, interactions between SSVEP and power grid frequency, a wider range of flicker frequencies, a larger sample of participants, and a systematic comparison of the information transfer obtained through SSVEPs, pupil diameter, and eye movements.

Filter bank temporally local multivariate synchronization index for SSVEP-based BCI

BackgroundMultivariate synchronization index (MSI) has been successfully applied for frequency detection in steady state visual evoked potential (SSVEP) based brain–computer interface (BCI) systems. However, the standard MSI algorithm and its variants cannot simultaneously take full advantage of the time-local structure and the harmonic components in SSVEP signals, which are both crucial for frequency detection performance. To overcome the limitation, we propose a novel filter bank temporally local MSI (FBTMSI) algorithm to further improve SSVEP frequency detection accuracy. The method explicitly utilizes the temporal information of signal for covariance matrix estimation and employs filter bank decomposition to exploits SSVEP-related harmonic components.ResultsWe employed the cross-validation strategy on the public Benchmark dataset to optimize the parameters and evaluate the performance of the FBTMSI algorithm. Experimental results show that FBTMSI outperforms the standard MSI, temporally local MSI (TMSI) and filter bank driven MSI (FBMSI) algorithms across multiple experimental settings. In the case of data length of one second, the average accuracy of FBTMSI is 9.85% and 3.15% higher than that of the FBMSI and the TMSI, respectively.ConclusionsThe promising results demonstrate the effectiveness of the FBTMSI algorithm for frequency recognition and show its potential in SSVEP-based BCI applications.

Closed Loop Subject-Independent SSVEP Frequency Detection System Using CCA Features and Ensemble Learning Methods

Purpose: In recent years, the use of Steady-State Visual Evoked Potentials (SSVEPs) in Brain-Computer Interface (BCI) systems has dramatically increased across several fields, such as rehabilitation, cognitive impairment, and brain disease or disorder detection, as well as artificial limbs, wheelchairs, and biomechanical systems. In this study, a novel method is proposed to help scientists develop more efficient BCI systems for Machine Learning Operations (MLOps). This study proposed a state-of-the-art method for detecting SSVEP-based stimulation frequencies with statistical models to design an optimal BCI system. Materials and Methods: In this study, the Canonical Correlation Analysis (CCA) method has been implemented to extract features from the accessible-to-the-public Tsinghua University Benchmark dataset. A limited number of subjects are being studied. After completing feature selection methods and selecting the best subset of features using a specified feature selection method, the classification of the best features using machine learning-based classification methods has been completed. Furthermore, it is assumed that scientists will design and implement a system specifically for subjects. Models work for subjects independently. However, because model training is subject-specific, we must execute the proposed methods on each subject separately. Results: The findings indicate that the novel suggested BCI system achieves an average accuracy of 83±9% in stimulation detection, which is higher than that of the traditional CCA approach with an accuracy of 80±11% (p<0.05). Conclusion: Based on the findings, we demonstrated an increase in accuracy with the novel method. It was also discovered that by using the proposed techniques, it is possible to keep MLOps systems as an advantage.

Leveraging Transfer Superposition Theory for StableState Visual Evoked Potential Cross-Subject Frequency Recognition.

In steady-state visual evoked potential (SSVEP)based brain-computer interfaces (BCIs), various spatial filtering methods based on individual calibration data have been proposed to alleviate the interference of spontaneous activities in SSVEP signals for enhancing the SSVEP detection performance. However, the necessary calibration procedures take time, cause visual fatigue and reduce usability. For the calibration-free scenario, we propose a cross-subject frequency identification method based on transfer superimposed theory for SSVEP frequency decoding. First, a multi-channel signal decomposition model was constructed. Next, we used the cross least squares iterative method to create individual specific transfer spatial filters as well as source subject transfer superposition templates in the source subject. Then, we identified common knowledge among source subjects using a prototype spatial filter to make common transfer spatial filters and common impulse responses. Following, we reconstructed a global transfer superimposition template with SSVEP frequency characteristics. Finally, an ensemble cross-subject transfer learning method was proposed for SSVEP frequency recognition by combining the sourcesubject transfer mode, the global transfer mode, and the sinecosine reference template. Offline tests on two public datasets show that the proposed method significantly outperforms the FBCCA, TTCCA, and CSSFT methods. More importantly, the proposed method can be directly used in online SSVEP recognition without calibration. The proposed algorithm was robust, which is important for a practical BCI.

Almost free of calibration for SSVEP-based brain-computer interfaces

Objective. Steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) is a promising technology that can achieve high information transfer rate (ITR) with supervised algorithms such as ensemble task-related component analysis (eTRCA) and task-discriminant component analysis (TDCA). However, training individual models requires a tedious and time-consuming calibration process, which hinders the real-life use of SSVEP-BCIs. A recent data augmentation method, called source aliasing matrix estimation (SAME), can generate new EEG samples from a few calibration trials. But SAME does not exploit the information across stimuli as well as only reduces the number of calibration trials per command, so it still has some limitations. Approach. This study proposes an extended version of SAME, called multi-stimulus SAME (msSAME), which exploits the similarity of the aliasing matrix across frequencies to enhance the performance of SSVEP-BCI with insufficient calibration trials. We also propose a semi-supervised approach based on msSAME that can further reduce the number of SSVEP frequencies needed for calibration. We evaluate our method on two public datasets, Benchmark and BETA, and an online experiment. Main results. The results show that msSAME outperforms SAME for both eTRCA and TDCA on the public datasets. Moreover, the semi-supervised msSAME-based method achieves comparable performance to the fully calibrated methods and outperforms the conventional free-calibrated methods. Remarkably, our method only needs 24 s to calibrate 40 targets in the online experiment and achieves an average ITR of 213.8 bits min−1 with a peak of 242.6 bits min−1. Significance. This study significantly reduces the calibration effort for individual SSVEP-BCIs, which is beneficial for developing practical plug-and-play SSVEP-BCIs.

Genetic algorithm based ensemble system using MLR and MsetCCA methods for SSVEP frequency recognition.

BCI systems provide a direct communication channel between the human and the machine using brain signals. Among the various methods of steady-state visual evoked potential (SSVEP) stimulation frequency detection, multiple linear regression (MLR), and multiset canonical correlation analysis (MsetCCA) methods have achieved high accurate results in recent studies. The purpose of this study is to utilize both approaches and benefit from them using a genetic algorithm (GA). This algorithm leads to high-performance optimization due to its large number of regulatory parameters. Signal analysis was performed for the windows with 0.5 to 4s duration length and with 0.5-second incremental steps. In this paper, we were able to achieve 100% accuracy of recognition for 2-second time-windows using the genetic algorithm to optimally ensemble SSVEP stimulation frequency detection methods. The accuracy of the proposed system indicates a significant improvement in detection compared to either MLR or MsetCCA alone and indicates that the ensemble system is correctly optimized using the genetic algorithm. Genetic algorithm is one of the most widely used algorithms because of its high regulatory parameters leading to its high flexibility. The improvement in detection of the proposed system is due to the use of the strengths of both two methods, and the optimal choice of the system response to visual stimuli.

Enhancing Detection of Control State for High-Speed Asynchronous SSVEP-BCIs Using Frequency-Specific Framework.

This study proposed a novel frequency-specific (FS) algorithm framework for enhancing control state detection using short data length toward high-performance asynchronous steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCI). The FS framework sequentially incorporated task-related component analysis (TRCA)-based SSVEP identification and a classifier bank containing multiple FS control state detection classifiers. For an input EEG epoch, the FS framework first identified its potential SSVEP frequency using the TRCA-based method and then recognized its control state using one of the classifiers trained on the features specifically related to the identified frequency. A frequency-unified (FU) framework that conducted control state detection using a unified classifier trained on features related to all candidate frequencies was proposed to compare with the FS framework. Offline evaluation using data lengths within 1 s found that the FS framework achieved excellent performance and significantly outperformed the FU framework. 14-target FS and FU asynchronous systems were separately constructed by incorporating a simple dynamic stopping strategy and validated using a cue-guided selection task in an online experiment. Using averaged data length of 591.63±5.65 ms, the online FS system significantly outperformed the FU system and achieved an information transfer rate, true positive rate, false positive rate, and balanced accuracy of 124.95±12.35 bits/min, 93.16±4.4%, 5.21±5.85%, and 92.89±4.02%, respectively. The FS system was also of higher reliability by accepting more correctly identified SSVEP trials and rejecting more wrongly identified ones. These results suggest that the FS framework has great potential to enhance the control state detection for high-speed asynchronous SSVEP-BCIs.

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

Objective. Steady-state visual evoked potentials (SSVEPs) based brain–computer interface (BCI) has received great interests owing to the high information transfer rate 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 one-dimensional convolution and long short-term memory (LSTM) module. To enhance the performance of SSVEPNET, 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. Significance. 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 convolutional neural network and LSTM, and the spectral normalization and label smoothing could be useful optimization strategies to design efficient models for electroencephalography data.

Durağan-Durum Görsel Uyarılmış Potansiyellere Dayalı Beyin-Bilgisayar Arayüzünün Zamansal ve Frekanssal Derinliğinden Frekans Tanıma

Brain-computer interface (BCI) system based on steady-state visual evoked potentials (SSVEP) have been acceleratingly used in different application areas from entertainment to rehabilitation, like clinical neuroscience, cognitive, and use of engineering researches. Of various electroencephalography paradigms, SSVEP-based BCI systems enable apoplectic people to communicate with outside world easily, due to their simple system structure, short or no training time, high temporal resolution, high information transfer rate, and affordable by comparing to other methods. SSVEP-based BCIs use multiple visual stimuli flickering at different frequencies to generate distinct commands. In this paper, we compared the classifier performances of combinations of binary commands flickering at seven different frequencies to determine which frequency pair gives the highest performance using temporal and spectral methods. For SSVEP frequency recognition, in total 25 temporal change characteristics of the signals and 15 frequency-based feature vectors extracted from the SSVEP signal. These feature vectors were applied to the input of seven well-known machine learning algorithms (Decision Tree, Discriminant Analysis, Logistic Regression, Naive Bayes, Support Vector Machines, Nearest Neighbour, and Ensemble Learning). In conclusion, we achieved 100% accuracy in 7.5 - 10 frequency pairs among these 2,520 distinct runs and we found that the most successful classifier is the Ensemble Learning classifier. The combination of these methods leads to an appropriate detailed and comparative analysis that represents the robustness and effectiveness of classical approaches.

Comparison of Steady-State Visual Evoked Potential (SSVEP) with LCD vs. LED Stimulation.

The steady-state visual evoked potential (SSVEP) is a robust brain activity that has been used in brain-computer interface (BCI) applications. However, previous studies of SSVEP-based BCIs give contradictory results on which stimulation medium provides the best performance. This paper describes a comparison of electroencephalography (EEG) decoding accuracy between using an LCD screen, clear LEDs, and frosted LEDs to deliver flashing light stimulation. The LCD screen and frosted LEDs achieved similar mean accuracies, and both of them were significantly better than clear LEDs. Background contrast with the LEDs did not significantly influence SSVEP decoding accuracy. A strong correlation was found between SSVEP accuracy and frequency domain magnitudes of EEG measurements.

Spatial fusion of maximum signal fraction analysis for frequency recognition in SSVEP-based BCI

Maximum signal fraction analysis (MSFA) is an efficient method for frequency identification in steady-state visual evoked potential (SSVEP) based brain-computer interface (BCI). However, standard MSFA only utilizes the spatial filter corresponding to the maximum eigenvalue for multichannel EEG signals de-noising and frequency identification, and discards other weight vectors containing discriminant information. In this work, we proposed a new SSVEP frequency recognition method with spatial dimension fusion strategy, i.e., FoMSFA, which employs the information of all spatial filters from standard MSFA, and uses a nonlinear weighting function to fuse multiple sets of correlation coefficients to identify the frequency of SSVEP signal. Numerical results of the benchmark dataset with 35 subjects and our laboratory dataset with 10 subjects show that FoMSFA outperforms the standard MSFA and CCA-based methods. The proposed method has the potential to design high-performance SSVEP-based BCI systems for communication and control.

SSVEP Enhancement Using Moving Average Filter Controlled by Phase Features

Brain-computer interface (BCI) systems translate the human neurophysiological activities into commands through EEG analysis. Improving the BCI performances leads to faster and easier use and less fatigue. In this study, we proposed a new prepossessing approach to increase the robustness of a steady-state visual evoked potential (SSVEP) based BCI. Inspiring from the known properties of the SSVEP frequency components, the goal was to enhance the signal quality by making it more convenient to be interpreted by the decision-making step. We first investigated the potential to detect the deteriorating periods based on the physiological properties of the SSVEP. The proposed system localizes the intervals which can obscure the SSVEP frequencies by a new algorithm founded on the processing and the analysis of the instantaneous phase. The piecewise linear regression allows a sampler comprehension of the phase signal. Then, these intervals are filtered by the moving average filter to enhance the SSVEP quality. Finally, the decision making is made by the canonical correlation analysis (CCA) algorithm. The results of experiments, using real EEG signals from five subjects, show that the proposed approach significantly increases the performances in terms of accuracy and information transfer rate by about 7.3% and 3.85 bits/min, respectively, in case of 2 s segment length. On the other hand, the spatial filtering methods of the literature weaken the system performances.

A Brain Computer Interface based Communication System using SSVEP and EOG

This study aims to design a high performance communication system using steady state visual evoked potential (SSVEP) and electro-oculogram (EOG) signal. The proposed keyboard system consists of twenty three randomly chosen characters and they were indexed into different flickering visual stimuli designed using three SSVEP stimulus frequencies. The same frequency valued visual stimuli were used many times (more than once) in the keyboard layout. In this approach an oddball paradigm is introduced and it randomly highlights three/two unique frequency valued visual stimuli at a time. The system identifies a desired target by user eye blink in accordance to the oddball paradigm and recognized SSVEP frequency value. The signal to noise ratio (SNR) of the real time SSVEP electroencephalogram signal gets increased by the introduction of oddball paradigm in the SSVEP based keyboard system. The paradigm will increase the subject’s attention and concentration on the flickering target stimulus. The real time SSVEP data were classified using extended multivariate synchronization index (EMSI) method. The thresholding method is used for single blink detection. The offline and online experiments were performed by all the subjects for evaluating the proposed communication system. The average classification accuracy and information transfer rate of the system are 96.73 % and 76.02 bits/min respectively.

Efficient representations of EEG signals for SSVEP frequency recognition based on deep multiset CCA

Canonical correlation analysis (CCA) has been widely used for frequency recognition in steady-state visual evoked potential (SSVEP) based brain–computer interfaces (BCIs). However, linear CCA-based methods may be insufficient given the complexity of EEG signals. A nonlinear feature extraction method based on deep multiset CCA (DMCCA) is proposed for SSVEP recognition to fully utilize the real EEG and constructed sine–cosine signals. In DMCCA, neural networks are trained to learn the nonlinear representations of multiple sets of EEG signals at the same frequency by maximizing the overall correlation within the representations and reference signals. Therefore, reference signals are augmented with the extracted features for frequency recognition. Finally, the proposed method is evaluated using SSVEP signals collected from 10 subjects. DMCCA-based method outperforms others in terms of classification accuracy compared with CCA- and multiset CCA-based methods. The proposed DMCCA-based method has substantial potential for improving the recognition performance of SSVEP signals.

Adaptive canonical correlation analysis for harmonic stimulation frequencies recognition in SSVEP-based BCIs

Steady-state visual evoked potential (SSVEP) is the brain's response to quickly repetitive visual stimulus with a certain frequency. To increase the information transfer rate (ITR) in SSVEP-based systems, due to the frequency resolution restriction, we are forced to broaden the frequency range, which causes harmonic frequencies to come into the stimulation frequency range. Conventional canonical correlation analysis (CCA) may be associated with error for SSVEP frequency recognition at stimulation frequencies with harmonic relations. The number of harmonics considered to construct reference signals are determined adaptively; for frequencies whose second harmonic exists in the frequency range, two harmonics are used, and for other frequencies, just one harmonic is used. After constructing reference signals and recognizing the frequency corresponding to the maximum value of correlation by CCA, the target frequency is determined after a postprocessing step. Results show that for the 8-s time window length, the average classification accuracy for the adaptive CCA was 84 %, while the corresponding values for the CCA with one harmonic $(N=1)$ and two harmonics $(N=2)$ were 78 % and 74 %, respectively. For 4-s length, this accuracy for the adaptive CCA was 86 %, while it was 78\% for both harmonic selection modes of the standard CCA, $N=1$ and $N=2$. In SSVEP applications with harmonic stimulation frequencies, the adaptive CCA has significantly improved the frequency recognition accuracy in comparison with the popularly standard CCA method. The proposed method can be useful for SSVEP-based BCI systems that use broad ranges of stimulation frequencies with harmonic relation.

Temporal Modulation of Steady-State Visual Evoked Potentials.

Electroencephalographic responses to periodic stimulation are termed steady-state visual evoked potentials (SSVEP). Their characteristics in terms of amplitude, frequency and phase are commonly assumed to be stationary. In this work, we tested this assumption in 30 healthy participants submitted to 50 trials of 60 s flicker stimulation at 15 Hz frequency. We showed that the amplitude of the first and second harmonic frequency components of SSVEP signals were in general not stable over time. The power (squared amplitude) of the fundamental component was stationary only in 30% the subjects, while the power at the second harmonic frequency was stationary in 66.7% of the group. The phases of both SSVEP frequency components were more stable over time, but could exhibit small drifts. The observed temporal changes were heterogeneous across the subjects, implying that averaging results over participants should be performed carefully. These results may contribute to improved design and analysis of experiments employing prolonged visual stimulation. Our findings offer a novel characterization of the temporal changes of SSVEP that may help to identify their physiological basis.

Bio-Inspired Filter Banks for Frequency Recognition of SSVEP-Based Brain–Computer Interfaces

Brain-computer interfaces (BCIs) and their associated technologies have the potential to shape future forms of communication, control, and security. Specifically, the steady-state visual evoked potential (SSVEP) based BCIs have the advantages of better recognition accuracy, and higher information transfer rate (ITR) compared to other BCI modalities. To fully exploit the capabilities of such devices, it is necessary to understand the underlying biological features of SSVEPs and design the system considering their inherent characteristics. This paper introduces bio-inspired filter banks (BIFBs) for improved SSVEP frequency recognition. SSVEPs are frequency selective, subject-specific, and their power gets weaker as the frequency of the visual stimuli increases. Therefore, the gain and bandwidth of the filters are designed and tuned based on these characteristics while also incorporating harmonic SSVEP responses. The BIFBs are utilized in the feature extraction stage to increase the separability of classes. This method not only improves the recognition accuracy but also increases the total number of available commands in a BCI system by allowing the use of stimuli frequencies that elicit weak SSVEP responses. The BIFBs are promising particularly in the high-frequency band, which causes less visual fatigue. Hence, the proposed approach might enhance user comfort as well. The BIFB method is tested on two online benchmark datasets and outperforms the compared methods. The results show the potential of bio-inspired design, and the findings will be extended by including further SSVEP characteristics for future SSVEP based BCIs.

A Single-Channel SSVEP-Based BCI Speller Using Deep Learning

This paper aims to develop a speller system based on a bipolar single-channel electroencephalogram with sufficient accuracy. The proposed system consists of a custom-designed headset, a new virtual keyboard with 58 characters, special symbols, and digits, and a five-target steady-state visual-evoked potential (SSVEP)-based brain–computer interface (BCI) utilizing one-dimensional convolutional neural network (1-D CNN) for SSVEP frequency detection. The deep learning model is implemented and trained under the training mode before being applied in the operation mode of the system. To validate the proposed model, we acquire the training dataset with numerous testing conditions, including different frequency resolutions of the feature and different time-window lengths of analysis. Two types of features based on the frequency domain are investigated to compare their performances in terms of classification accuracy of the model. The experimental results from eight subjects shows that on average, the proposed model can classify five-class SSVEP data with a high accuracy of 99.2%. The proposed BCI is then employed in an online experiment of spelling the word “SPELLER” using 2-s time window. Consequently, the system achieves an average accuracy of 97.4% and an information transfer rate of 49 ± 7.7 bpm, showing the practicality and feasibility of implementing a reliable single-channel SSVEP-based speller utilizing 1-D CNN.

Comparing Steady-State Visually Evoked Potentials Frequency Estimation Methods in Brain-Computer Interface With the Minimum Number of EEG Channels.

Introduction:Brain-Computer Interface (BCI) systems provide a communication pathway between users and systems. BCI systems based on Steady-State Visually Evoked Potentials (SSVEP) are widely used in recent decades. Different feature extraction methods have been introduced in the literature to estimate SSVEP responses to BCI applications.Methods:In this study, the new algorithms, including Canonical Correlation Analysis (CCA), Least Absolute Shrinkage and Selection Operator (LASSO), L1-regularized Multi-way CCA (L1-MCCA), Multi-set CCA (MsetCCA), Common Feature Analysis (CFA), and Multiple Logistic Regression (MLR) are compared using proper statistical methods to determine which one has better performance with the least number of EEG electrodes.Results:It was found that MLR, MsetCCA, and CFA algorithms provided the highest performances and significantly outperformed CCA, LASSO, and L1-MCCA algorithms when using 8 EEG channels. However, when using only 1 or 2 EEG channels d, CFA method provided the highest F-scores. This algorithm not only outperformed MLR and MsetCCA when applied on different electrode montages but also provided the fastest computation time on the test set.Conclusion:Although MLR method has already demonstrated to have higher performance in comparison with other frequency recognition algorithms, this study showed that in a practical SSVEP-based BCI system with 1 or 2 EEG channels and short-time windows, CFA method outperforms other algorithms. Therefore, it is proposed that CFA algorithm is a promising choice for the expansion of practical SSVEP-based BCI systems.

Application of a reconstruction technique in detection of dominant SSVEP frequency

Abstract Steady-state visual evoked potentials (SSVEPs) have a number of specific properties such as an oscillating feature when compared to event-related potentials (ERPs). Based on this oscillating property, a short electroencephalogram (EEG) segment containing an SSVEP can be used to reconstruct a series of EEG segments where the SSVEP has the same initial phase but the phase of the background EEG varies randomly. When these reconstructed EEG segments are averaged, the background EEG is weakened, whereas the SSVEP is strengthened. Therefore, the signal-to-noise ratio (SNR) of the SSVEP in the averaged signal is significantly improved. This reconstruction technique is first proposed in this work, and the method of extracting SSVEPs based on this technique is referred to as the reconstruction extraction (RE) method. The RE, power spectrum (PS), and canonical correlation analysis (CCA) methods were applied to EEG segments that were 1s in length in order to detect SSVEPs. The results show that the detection accuracy of the RE method is similar to that of the CCA method, although it is higher than that of the PS method in situations where the SSVEP has low strength. However, in contrast to the CCA, the RE can be applied using only one signal electrode. This suggests that the RE method can be adopted in a real-time SSVEP-based brain–computer interface (BCI) system.