Filter Bank Canonical Correlation Analysis Research Articles

Feature fusion improves brain-interface paradigm based on steady state visual evoked potential blocking response

The steady-state visual evoked potential blocking response (SSVEP-BR) is produced on an electroencephalogram (EEG) when the SSVEP is interrupted or abolished and allows augmentation of brain-computer interface (BCI) paradigms. Integration of the SSVEP-BR with the SSVEP enables an increase in the number of commands without the addition of further stimuli but refinement would improve performance. The current study evaluated SSVEP-BR and a novel method to combine multiple features and enhance the performance of frequency recognition and SSVEP-BR identification is proposed. Correlation features were extracted by filter bank canonical correlation analysis (FBCCA) and synchronization features by multivariate synchronization index (MSI) before being integrated. The performance of correlation features extracted by task-related component analysis (TRCA) were also evaluated. The novel integrated method was compared with FBCCA, MSI and TRCA for performance in frequency recognition and SSVEP-BR identification. The novel integrated method achieved higher classification accuracy than FBCCA and MSI for benchmark datasets when the sliding window was used for EEG data. However, the accuracy of TRCA was not stable when the sliding window was used. The novel integrated method produced an improvement in SSVEP-BR identification over FBCCA and MSI in the blocking dataset. TRCA was not found to be effective for SSVEP-BR identification. A novel integrated method is proposed which gives higher classification accuracy and more stable performance than FBCCA, MSI and TRCA for frequency recognition and SSVEP-BR identification when the EEG data sliding window was used and shows superior performance for the BCI paradigm based on SSVEP and SSVEP-BR.

A comparative study of stereo-dependent SSVEP targets and their impact on VR-BCI performance.

Steady-state visual evoked potential brain-computer interfaces (SSVEP-BCI) have attracted significant attention due to their ease of deployment and high performance in terms of information transfer rate (ITR) and accuracy, making them a promising candidate for integration with consumer electronics devices. However, as SSVEP characteristics are directly associated with visual stimulus attributes, the influence of stereoscopic vision on SSVEP as a critical visual attribute has yet to be fully explored. Meanwhile, the promising combination of virtual reality (VR) devices and BCI applications is hampered by the significant disparity between VR environments and traditional 2D displays. This is not only due to the fact that screen-based SSVEP generally operates under static, stable conditions with simple and unvaried visual stimuli but also because conventional luminance-modulated stimuli can quickly induce visual fatigue. This study attempts to address these research gaps by designing SSVEP paradigms with stereo-related attributes and conducting a comparative analysis with the traditional 2D planar paradigm under the same VR environment. This study proposed two new paradigms: the 3D paradigm and the 3D-Blink paradigm. The 3D paradigm induces SSVEP by modulating the luminance of spherical targets, while the 3D-Blink paradigm employs modulation of the spheres' opacity instead. The results of offline 4-object selection experiments showed that the accuracy of 3D and 2D paradigm was 85.67 and 86.17% with canonical correlation analysis (CCA) and 86.17 and 91.73% with filter bank canonical correlation analysis (FBCCA), which is consistent with the reduction in the signal-to-noise ratio (SNR) of SSVEP harmonics for the 3D paradigm observed in the frequency-domain analysis. The 3D-Blink paradigm achieved 75.00% of detection accuracy and 27.02 bits/min of ITR with 0.8 seconds of stimulus time and task-related component analysis (TRCA) algorithm, demonstrating its effectiveness. These findings demonstrate that the 3D and 3D-Blink paradigms supported by VR can achieve improved user comfort and satisfactory performance, while further algorithmic optimization and feature analysis are required for the stereo-related paradigms. In conclusion, this study contributes to a deeper understanding of the impact of binocular stereoscopic vision mechanisms on SSVEP paradigms and promotes the application of SSVEP-BCI in diverse VR environments.

A calibration-free c-VEP based BCI employing narrow-band random sequences

Objective. Code-modulated visual evoked potential (c-VEP) based brain–computer interfaces (BCIs) exhibit high encoding efficiency. Nevertheless, the majority of c-VEP based BCIs necessitate an initial training or calibration session, particularly when the number of targets expands, which impedes the practicality. To address this predicament, this study introduces a calibration-free c-VEP based BCI employing narrow-band random sequences. Approach. For the encoding method, a series of random sequences were generated within a specific frequency band. The c-VEP signals were subsequently elicited through the application of on-type grid flashes that were modulated by these sequences. For the calibration-free decoding algorithm, filter-bank canonical correlation analysis (FBCCA) was utilized with the reference templates generated from the original sequences. Thirty-five subjects participated into an online BCI experiment. The performances of c-VEP based BCIs utilizing narrow-band random sequences with frequency bands of 15–25 Hz (NBRS-15) and 8–16 Hz (NBRS-8) were compared with that of a steady-state visual evoked potential (SSVEP) based BCI within a frequency range of 8–15.8 Hz. Main results. The offline analysis results demonstrated a substantial correlation between the c-VEPs and the original narrow-band random sequences. After parameter optimization, the calibration-free system employing the NBRS-15 frequency band achieved an average information transfer rate (ITR) of 78.56 ± 37.03 bits/min, which exhibited no significant difference compared to the performance of the SSVEP based system when utilizing FBCCA. The proposed system achieved an average ITR of 102.1 ± 57.59 bits/min in a simulation of a 1000-target BCI system. Significance. This study introduces a novel calibration-free c-VEP based BCI system employing narrow-band random sequences and shows great potential of the proposed system in achieving a large number of targets and high ITR.

Research on SSVEP‐EEG feature enhancement algorithm based on fractional differentiation

AbstractSteady‐state visual evoked potentials (SSVEP), significant in brain‐computer interfaces (BCI) and medical diagnostics, benefit from enhanced signal processing for improved analysis and interpretation. This study introduces a novel enhancement algorithm for SSVEP electroencephalogram (EEG) signals, employing fractional‐order differentiation operators combined with image processing techniques. Utilizing fractional‐order differentiation within a Laplace pyramid framework, the algorithm achieves hierarchical signal enhancement, facilitating detailed feature extraction and emphasizing SSVEP signal characteristics. This innovative approach merges the precision of fractional calculus with the structural benefits of the Laplace pyramid, leading to enhanced signal clarity and feature discrimination. The efficacy of this method was validated using canonical correlation analysis (CCA), filter bank CCA (FBCCA), and task‐related component analysis (TRCA) on a public dataset. Compared to conventional methods, our algorithm not only mitigates trend components in SSVEP signals but also significantly boosts the recognition accuracy of CCA, FBCCA, and TRCA algorithms. Experimental results indicate a marked improvement in recognition precision, underscoring the algorithm's potential to advance SSVEP‐based BCI research.

Control of the robotic arm system with an SSVEP-based BCI

Recent studies on brain–computer interfaces (BCIs) implemented in robotic systems have shown that the system’s effectiveness in assisting individuals with movement disorders to enhance their human–computer interaction skills. However, achieving precise and rapid online completion of tasks remains a challenge for manipulators with multiple degrees of freedom (DOFs). In this paper, we explore a time-sharing control strategy for studying motion control of a robotic arm based on steady-state visual evoked potentials. The signals are generated by the joint frequency-phase modulation method, analyzed with the filter-bank canonical correlation analysis algorithm, and identified to control the six-DOF robotic arm for task execution. The shared control strategy not only reduces user’s cognitive fatigue but also enhances system in practical environments. The use of high-frequency stimuli significantly improves user comfort, and hybrid coding increases the universality of the BCI system. Additionally, by setting multiple locations and actions randomly, the robotic arm can adaptively program the optimal path. The online results showed that BCI instructions of the proposed system could be accurately chosen from six options within 6.45 s. Subjects used an average of 12 commands for the robotic arm to achieve the proposed task with an average accuracy of 98.21%. These findings validate the feasibility and effectiveness of applying the system to robotic control. The control strategy proposed in this study exhibits versatility in controlling robots to perform various complex tasks across different domains.

Hybrid Brain-Computer Interface Controlled Soft Robotic Glove for Stroke Rehabilitation.

Soft robotic glove controlled by a brain-computer interface (BCI) have demonstrated effectiveness in hand rehabilitation for stroke patients. Current systems mostly rely on static visual representations for patients to perform motor imagination (MI) tasks, resulting in lower BCI performance. Therefore, this study innovatively used MI and high-frequency steady-state visual evoked potential (SSVEP) to construct a friendly and natural hybrid BCI paradigm. Specifically, the stimulation interface sequentially presented decomposed action pictures of the left and right hands gripping a ball, with the pictures flashing at specific stimulation frequencies (left: 34 Hz, right: 35 Hz). Integrating soft robotic glove as feedback, we established a comprehensive "peripheral - central - peripheral" hand rehabilitation system to facilitate the hand rehabilitation of patients. Filter bank common spatial pattern (FBCSP) and filter bank canonical correlation analysis (FBCCA) algorithms were used to identify MI and SSVEP signals, respectively. Additionally, to fuse the features of these two signals, we proposed a novel fusion algorithm for improving the recognition accuracy of the system. The feasibility of the proposed system was validated through online experiments involving 12 healthy subjects and 9 stroke patients, achieving accuracy rates of 95.83 ± 6.83% and 63.33 ± 10.38%, respectively. The accuracy of MI and SSVEP in 12 healthy subjects reached 81.67 ± 15.63% and 95.14 ± 7.47%, both lower than the accuracy after fusion, these results confirmed the effectiveness of the proposed algorithm. The accuracy rate was more than 50% in both healthy subjects and patients, confirming the effectiveness of the proposed system.

Filter bank second-order underdamped stochastic resonance analysis for implementing a short-term high-speed SSVEP detection

ObjectiveThe progression of brain-computer interfaces (BCIs) has been propelled by breakthroughs in neuroscience, signal processing, and machine learning, marking it as a dynamic field of study over the past few decades. Nevertheless, the nonlinear and non-stationary characteristics of steady-state visual evoked potentials (SSVEPs), coupled with the incongruity between frequently employed linear techniques and nonlinear signal attributes, resulted in the subpar performance of mainstream non-training algorithms like canonical correlation analysis (CCA), multivariate synchronization index (MSI), and filter bank CCA (FBCCA) in short-term SSVEP detection. MethodsTo tackle this problem, the novel fusions of common filter bank analysis, CCA dimensionality reduction methods, USSR models, and MSI recognition models are used in SSVEP signal recognition. ResultsUnlike conventional linear techniques such as CCA, MSI, and FBCCA, the filter bank second-order underdamped stochastic resonance (FBUSSR) analysis demonstrates superior efficacy in the detection of short-term high-speed SSVEPs. ConclusionThis research enlists 32 subjects and uses a public dataset to assess the proposed approach, and the experimental outcomes indicate that the non-training method can attain greater recognition precision and stability. Furthermore, under the conditions of the newly proposed fusion method and light stimulation, the USSR model exhibits the most optimal enhancement effect. SignificanceThe findings of this study underscore the expansive potential for the application of BCI systems in the realm of neuroscience and signal processing.

A large database towards user-friendly SSVEP-based BCI

Background: Brain-computer interfaces (BCIs) have gained considerable attention for their potential in assisting individuals who have motor impairments with communication and rehabilitation. Among BCIs, steady-state visual evoked potential (SSVEP)-based systems have demonstrated high efficiency in interactive applications. However, ergonomic design challenges have limited their practical implementation in industrial settings. Issues such as visual and mental fatigue caused by flickering stimuli and the time-consuming preparation process hinder user adoption of such systems. Methods: To evaluate these BCI solutions, we introduced an open database comprising Electroencephalogram (EEG) data collected from 59 healthy volunteers using ergonomically designed semi-dry electrodes and grid stimuli. The database was acquired without electromagnetic shielding, and the preparation time for each participant was <5 min. A 40-target SSVEP speller system with cues was used in the experiment. Results: We validate the database by temporal and spectral analyzing methods. To further investigate the database, filter bank canonical correlation analysis (FBCCA), ensemble task-related component analysis (e-TRCA) and multi-stimulus task-related component analysis (msTRCA) were used for classification. The database can be downloaded from the following link: https://drive.google.com/drive/folders/1TXuxU863nZoniZRgNWZy0PRuL8lhBuP4?usp=sharing . Conclusions: This research contributes to enhancing the use of SSVEP-based BCIs in practical settings by addressing user experience and system design challenges. The proposed user-friendly visual stimuli and ergonomic electrode design improve comfort and usability. The open dataset serves as a valuable resource for future studies, enabling the development of robust and efficient SSVEP- BCI systems suitable for industrial applications.

A novel untrained SSVEP-EEG feature enhancement method using canonical correlation analysis and underdamped second-order stochastic resonance.

Compared with the light-flashing paradigm, the ring-shaped motion checkerboard patterns avoid uncomfortable flicker or brightness modulation, improving the practical interactivity of brain-computer interface (BCI) applications. However, due to fewer harmonic responses and more concentrated frequency energy elicited by the ring-shaped checkerboard patterns, the mainstream untrained algorithms such as canonical correlation analysis (CCA) and filter bank canonical correlation analysis (FBCCA) methods have poor recognition performance and low information transmission rate (ITR). To address this issue, a novel untrained SSVEP-EEG feature enhancement method using CCA and underdamped second-order stochastic resonance (USSR) is proposed to extract electroencephalogram (EEG) features. In contrast to typical unsupervised dimensionality reduction methods such as common average reference (CAR), principal component analysis (PCA), multidimensional scaling (MDS), and locally linear embedding (LLE), CCA exhibits higher adaptability for SSVEP rhythm components. This study recruits 42 subjects to evaluate the proposed method and experimental results show that the untrained method can achieve higher detection accuracy and robustness. This untrained method provides the possibility of applying a nonlinear model from one-dimensional signals to multi-dimensional signals.

SSVEP detection assessment by combining visual stimuli paradigms and no-training detection methods.

Brain-Computer Interfaces (BCI) based on Steady-State Visually Evoked Potentials (SSVEP) have great potential for use in communication applications because of their relatively simple assembly and in some cases the possibility of bypassing the time-consuming training stage. However, among multiple factors, the efficient performance of this technology is highly dependent on the stimulation paradigm applied in combination with the SSVEP detection algorithm employed. This paper proposes the performance assessment of the classification of target events with respect to non-target events by applying four types of visual paradigms, rectangular modulated On-Off (OOR), sinusoidal modulated On-Off (OOS), rectangular modulated Checkerboard (CBR), and sinusoidal modulated Checkerboard (CBS), with three types of SSVEP detection methods, Canonical Correlation Analysis (CCA), Filter-Bank CCA (FBCCA), and Minimum Energy Combination (MEC). We set up an experimental protocol in which the four types of visual stimuli were presented randomly to twenty-seven participants and after acquiring their electroencephalographic responses to five stimulation frequencies (8.57, 10.909, 15, 20, and 24 Hz), the three detection methods were applied to the collected data. The results are conclusive, obtaining the best performance with the combination of either OOR or OOS visual stimulus and the FBCCA as a detection method, however, this finding contrasts with the opinion of almost half of the participants in terms of visual comfort, where the 51.9% of the subjects felt more comfortable and focused with CBR or CBS stimulation. Finally, the EEG recordings correspond to the SSVEP response of 27 subjects to four visual paradigms when selecting five items on a screen, which is useful in BCI navigation applications. The dataset is available to anyone interested in studying and evaluating signal processing and machine-learning algorithms for SSVEP-BCI systems.

Spatially-coded SSVEP BCI without pre-training based on FBCCA

Spatially-coded steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) uses the distribution of SSVEP to infer the gaze position of the user relative to stimulus blocks. Unlike conventional frequency-coded BCIs, spatially-coded BCI can encode multiple targets with a few frequencies and does not require the user to gaze directly at flickering light. The previous spatially-coded BCI recognition methods required training data to train the recognition model and had a long preparation time, hindering practical applications. This study proposed spatially-coded filter bank canonical correlation analysis (SCFBCCA) that does not require pre-training, enabling a six-target spatially-coded BCI without pre-training. There were two experiments in this study, each with different visual stimulation. In Experiment 1, we proposed an eigenvalue that can be used to classify the horizontal position of targets and proved the feasibility of methods that do not require training data. Experiment 1 also obtained some conclusions that were favorable to the visual stimulation design. Following the conclusions of Experiment 1, a six-target spatially-coded BCI and its recognition method were designed in Experiment 2. Finally, in the six-target classification experiment, the average accuracy reached 61.6 ± 3.8 % for five subjects. For the first time, a spatially-coded BCI without pre-training was completed. Its accuracy was similar to spatially-coded BCIs that use a small amount of training data. Meanwhile, the preparation time of BCI in this study was very short, which was more conducive to practical application.

A Precise Frequency Recognition Method of Short-time SSVEP Signals Based on Signal Extension.

Improving the Information Transfer Rate (ITR) is a popular research topic in steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs). The higher recognition accuracy of short-time SSVEP signal is critical to improving ITR and achieving high-speed SSVEP-BCIs. However, the existing algorithms have unsatisfactory performance on recognizing short-time SSVEP signals, especially for calibration-free methods. This study for the first time proposed improving the recognition accuracy of short-time SSVEP signals based on the calibration-free method by extending the SSVEP signal length. A signal extension model based on Multi-channel adaptive Fourier decomposition with differ-ent Phase (DP-MAFD) is proposed to achieve signal exten-sion. Then the Canonical Correlation Analysis based on signal extension (SE-CCA) is proposed to complete the recognition and classification of SSVEP signals after exten-sion. The similarity study and SNR comparison analysis on public SSVEP datasets demonstrate that the proposed signal extension model has the ability to extend SSVEP signals. The classification results show that the pro-posed method outperforms Canonical Correlation Analysis (CCA) and Filter Bank Canonical Correlation Analysis (FBCCA) significantly in the measure of classification accu-racy and information transmission rate (ITR), especially for short-time signals. The highest ITR of SE-CCA is improved to 175.61 bits/min at around 1s, while CCA is 100.55 bits/min at 1.75s and FBCCA is 141.76 bits/min at 1.25s. The signal extension method can improve the recognition accuracy of short-time SSVEP signals and further improve the ITR of SSVEP-BCIs.

A High-Frequency SSVEP-BCI System Based on Simultaneous Modulation of Luminance and Motion Using Intermodulation Frequencies.

The low-frequency steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) tend to induce visual fatigue in the subjects. In order to enhance the comfort of SSVEP-BCIs, a novel SSVEP-BCI encoding method based on simultaneous modulation of luminance and motion is proposed. In this work, sixteen stimulus targets are simultaneously flickered and radially zoomed using a sampled sinusoidal stimulation method. The flicker frequency is set to a 30 Hz for all the targets, while assigning different radial zoom frequencies (ranging from 0.4 Hz to 3.4 Hz, with an interval of 0.2 Hz) are assigned to each target separately. Accordingly, an extended vision of the filter bank canonical correlation analysis (eFBCCA) is proposed to detect the intermodulation (IM) frequencies and classify the targets. In addition, we adopt the comfort level scale to evaluate the subjective comfort experience. By optimizing the combination of IM frequencies for the classification algorithm, the average recognition accuracy of the offline and online experiments reaches 92.74 ± 1.53% and 93.33 ± 0.01%, respectively. Most importantly, the average comfort scores are above 5. These results demonstrate the feasibility and comfort of the proposed system using IM frequencies, which provides new ideas for the further development of highly comfortable SSVEP-BCIs.

A Novel SSVEP Brain-Computer Interface System Based on Simultaneous Modulation of Luminance and Motion.

Steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) have received significant attention owing to their high information transfer rate (ITR) and low training requirements. Previous SSVEP-based BCIs mostly adopt the stationary visual flickers where only a few studies have explored the effect of moving visual flickers on the SSVEP-BCI. In this study, a novel stimulus encoding method based on the simultaneous modulation of luminance and motion was proposed. We adopted the sampled sinusoidal stimulation method to encode the frequencies and phases of stimulus targets. In addition to luminance modulation, at the same time, visual flickers also moved horizontally towards right and left at different frequencies (i.e., 0, 0.2, 0.4, and 0.6 Hz) following a sinusoidal function. Accordingly, a nine-target SSVEP-BCI was built to evaluate the influence of motion modulation on the BCI performance. Filter bank canonical correlation analysis (FBCCA) approach was adopted to identify the stimulus targets. Offline experimental results of 17 subjects revealed that the system performance decreased with the increase of superimposed horizontal periodic motion frequency. Our online experimental results showed that the subjects achieved 85.00 ± 6.77 % and 83.15 ± 9.88 % accuracy for the superimposed horizontal periodic motion frequencies of 0 and 0.2 Hz, respectively. These results verified the feasibility of the proposed systems. In addition, the system with 0.2 Hz horizontal motion frequency provided the best visual experience for subjects. These results indicated that moving visual stimulus can provide an alternative option for SSVEP-BCIs. Furthermore, the proposed paradigm is expected to develop a more comfortable BCI system.

A calibration-free hybrid BCI speller system based on high-frequency SSVEP and sEMG.

Hybrid brain-computer interface (hBCI) systems that combine steady-state visual evoked potential (SSVEP) and surface electromyography (sEMG) signals have attracted attention of researchers due to the advantage of exhibiting significantly improved system performance. However, almost all existing studies adopt low-frequency SSVEP to build hBCI. It produces much more visual fatigue than high-frequency SSVEP. Therefore, the current study attempts to build a hBCI based on high-frequency SSVEP and sEMG. With these two signals, this study designed and realized a 32-target hBCI speller system. Thirty-two targets were separated from the middle into two groups. Each side contained 16 sets of targets with different high-frequency visual stimuli (i.e., 31-34.75 Hz with an interval of 0.25 Hz). sEMG was utilized to choose the group and SSVEP was adopted to identify intra-group targets. The filter bank canonical correlation analysis (FBCCA) and the root mean square value (RMS) methods were used to identify signals. Therefore, the proposed system allowed users to operate it without system calibration. A total of 12 healthy subjects participated in online experiment, with an average accuracy of 93.52 ± 1.66% and the average information transfer rate (ITR) reached 93.50 ± 3.10 bits/min. Furthermore, 12 participants perfectly completed the free-spelling tasks. These results of the experiments indicated feasibility and practicality of the proposed hybrid BCI speller system.

Frequency Domain Filtering Method for SSVEP-EEG Preprocessing

Steady-state visual evoked potential (SSVEP) signal collected from the scalp typically contains other types of electric signals, and it is important to remove these noise components from the actual signal by application of a pre-processing step for accurate analysis. High-pass or bandpass filtering of the SSVEP signal in the time domain is the most common pre-processing method. Because frequency is the most important feature information contained in the SSVEP signal, a technique for frequency-domain filtering of SSVEP was proposed here. In this method, the time-domain signal is extended to multi-dimensional signal by empirical mode decomposition (EMD), where each dimension represents a intrinsic mode function (IMF). The multi-dimensional signal is transformed to a frequency-domain signal by 2-D Fourier transform, the Gaussian high-pass filter function is constructed to perform high-pass filtering, and then the filtered signal is transformed to time domain by 2-D inverse Fourier transform. Finally, the filtered multi-dimensional intrinsic mode function is superimposed and averaged as the frequency-domain filtered signal. Compared with the time-domain filtering method, the experimental results revealed that frequency-domain filtering method effectively removed the baseline drift in signal and effectively suppressed the low-frequency interference component. This method was tested using data from public datasets and the results show that the proposed frequency-domain filtering method can significantly improve the feature recognition performance of canonical correlation analysis (CCA), filter bank canonical correlation analysis (FBCCA), and task-related component analysis (TRCA) methods. Thus, the results suggest that the application of frequency-domain filtering in the pre-processing stage allows improved noise removal. The proposed method extends SSVEP signal filtering from time-domain to frequency-domain, and the results suggest that this analysis tool significantly promotes the practical application of SSVEP systems.

A Novel Turbo Detector Design for a High-Speed SSVEP-Based Brain Speller

The past decade has witnessed the rapid development of brain-computer interfaces (BCIs). The contradiction between communication rates and tedious training processes has become one of the major barriers restricting the application of steady-state visual-evoked potential (SSVEP)-based BCIs. A turbo detector was proposed in this study to resolve this issue. The turbo detector uses the filter bank canonical correlation analysis (FBCCA) as the first-stage detector and then utilizes the soft information generated by the first-stage detector and the pool of identified data generated during use to complete the second-stage detection. This strategy allows for rapid performance improvements as the data pool size increases. A standard benchmark dataset was used to evaluate the performance of the proposed method. The results show that the turbo detector can achieve an average ITR of 130 bits/min, which is about 8% higher than FBCCA. As the size of the data pool increases, the ITR of the turbo detector could be further improved.

Novel hybrid visual stimuli incorporating periodic motions into conventional flickering or pattern-reversal visual stimuli for steady-state visual evoked potential-based brain-computer interfaces.

In this study, we proposed a new type of hybrid visual stimuli for steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs), which incorporate various periodic motions into conventional flickering stimuli (FS) or pattern reversal stimuli (PRS). Furthermore, we investigated optimal periodic motions for each FS and PRS to enhance the performance of SSVEP-based BCIs. Periodic motions were implemented by changing the size of the stimulus according to four different temporal functions denoted by none, square, triangular, and sine, yielding a total of eight hybrid visual stimuli. Additionally, we developed the extended version of filter bank canonical correlation analysis (FBCCA), which is a state-of-the-art training-free classification algorithm for SSVEP-based BCIs, to enhance the classification accuracy for PRS-based hybrid visual stimuli. Twenty healthy individuals participated in the SSVEP-based BCI experiment to discriminate four visual stimuli with different frequencies. An average classification accuracy and information transfer rate (ITR) were evaluated to compare the performances of SSVEP-based BCIs for different hybrid visual stimuli. Additionally, the user's visual fatigue for each of the hybrid visual stimuli was also evaluated. As the result, for FS, the highest performances were reported when the periodic motion of the sine waveform was incorporated for all window sizes except for 3 s. For PRS, the periodic motion of the square waveform showed the highest classification accuracies for all tested window sizes. A significant statistical difference in the performance between the two best stimuli was not observed. The averaged fatigue scores were reported to be 5.3 ± 2.05 and 4.05 ± 1.28 for FS with sine-wave periodic motion and PRS with square-wave periodic motion, respectively. Consequently, our results demonstrated that FS with sine-wave periodic motion and PRS with square-wave periodic motion could effectively improve the BCI performances compared to conventional FS and PRS. In addition, thanks to its low visual fatigue, PRS with square-wave periodic motion can be regarded as the most appropriate visual stimulus for the long-term use of SSVEP-based BCIs, particularly for window sizes equal to or larger than 2 s.

Exploring the effects of head movements and accompanying gaze fixation switch on steady-state visual evoked potential.

In a realistic steady-state visual evoked potential (SSVEP) based brain-computer interface (BCI) application like driving a car or controlling a quadrotor, observing the surrounding environment while simultaneously gazing at the stimulus is necessary. This kind of application inevitably could cause head movements and variation of the accompanying gaze fixation point, which might affect the SSVEP and BCI’s performance. However, few papers studied the effects of head movements and gaze fixation switch on SSVEP response, and the corresponding BCI performance. This study aimed to explore these effects by designing a new ball tracking paradigm in a virtual reality (VR) environment with two different moving tasks, i.e., the following and free moving tasks, and three moving patterns, pitch, yaw, and static. Sixteen subjects were recruited to conduct a BCI VR experiment. The offline data analysis showed that head moving patterns [F(2, 30) = 9.369, p = 0.001, effect size = 0.384] resulted in significantly different BCI decoding performance but the moving tasks had no effect on the results [F(1, 15) = 3.484, p = 0.082, effect size = 0.188]. Besides, the canonical correlation analysis (CCA) and filter bank canonical correlation analysis (FBCCA) accuracy were better than the PSDA and MEC methods in all of the conditions. These results implied that head movement could significantly affect the SSVEP performance but it was possible to switch gaze fixation to interact with the surroundings in a realistic BCI application.

Effects of Low Mental Energy from Long Periods of Work on Brain-Computer Interfaces.

Brain-computer interfaces (BCIs) provide novel hands-free interaction strategies. However, the performance of BCIs is affected by the user’s mental energy to some extent. In this study, we aimed to analyze the combined effects of decreased mental energy and lack of sleep on BCI performance and how to reduce these effects. We defined the low-mental-energy (LME) condition as a combined condition of decreased mental energy and lack of sleep. We used a long period of work (>=18 h) to induce the LME condition, and then P300- and SSVEP-based BCI tasks were conducted in LME or normal conditions. Ten subjects were recruited in this study. Each subject participated in the LME- and normal-condition experiments within one week. For the P300-based BCI, we used two decoding algorithms: stepwise linear discriminant (SWLDA) and least square regression (LSR). For the SSVEP-based BCI, we used two decoding algorithms: canonical correlation analysis (CCA) and filter bank canonical correlation analysis (FBCCA). Accuracy and information transfer rate (ITR) were used as performance metrics. The experimental results showed that for the P300-based BCI, the average accuracy was reduced by approximately 35% (with a SWLDA classifier) and approximately 40% (with a LSR classifier); the average ITR was reduced by approximately 6 bits/min (with a SWLDA classifier) and approximately 7 bits/min (with an LSR classifier). For the SSVEP-based BCI, the average accuracy was reduced by approximately 40% (with a CCA classifier) and approximately 40% (with a FBCCA classifier); the average ITR was reduced by approximately 20 bits/min (with a CCA classifier) and approximately 19 bits/min (with a FBCCA classifier). Additionally, the amplitude and signal-to-noise ratio of the evoked electroencephalogram signals were lower in the LME condition, while the degree of fatigue and the task load of each subject were higher. Further experiments suggested that increasing stimulus size, flash duration, and flash number could improve BCI performance in LME conditions to some extent. Our experiments showed that the LME condition reduced BCI performance, the effects of LME on BCI did not rely on specific BCI types and specific decoding algorithms, and optimizing BCI parameters (e.g., stimulus size) can reduce these effects.