Simultaneous Decoding of Eccentricity and Direction Information for a Single-Flicker SSVEP BCI

EEG measurement for the effect of perceptual eye position and eye position training on comitant strabismus

One of the clinical features of comitant strabismus is that the deviation angles in the first and second eye positions are equal. However, there has been no report of consistency in the electroencephalography (EEG) signals between the 2 positions. In order to address this issue, we developed a new paradigm based on perceptual eye position. We collected steady-state visual evoked potentials (SSVEPs) signals and resting-state EEG data before and after the eye position training. We found that SSVEP signals could characterize the suppression effect and eye position effect of comitant strabismus, that is, the SSVEP response of the dominant eye was stronger than that of the strabismus eye in the first eye position but not in the second eye position. Perceptual eye position training could modulate the frequency band activities in the occipital and surrounding areas. The changes in the visual function of comitant strabismus after training could also be characterized by SSVEP. There was a correlation between intermodulation frequency, power of parietal electrodes, and perceptual eye position, indicating that EEG might be a potential indicator for evaluating strabismus visual function.

A high-performance SSVEP-based BCI using imperceptible flickers

Objective.Existing steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) struggle to balance user experience and system performance. This study proposed an individualized space and phase modulation method to code imperceptible flickers at 60 Hz towards a user-friendly SSVEP-based BCI with high performance.Approach.The individualized customization of visual stimulation took the subject-to-subject variability in cortex geometry into account. An annulus global-stimulation was divided into local-stimulations of eight annular sectors and presented to subjects separately. The local-stimulation SSVEPs were superimposed to simulate global-stimulation SSVEPs with 4⁷space and phase coding combinations. A four-class phase-coded BCI diagram was used to evaluate the simulated classification performance. The performance ranking of all simulated global-stimulation SSVEPs were obtained and three performance levels (optimal, medium, worst) of individualized modulation groups were searched for each subject. The standard-modulation group conforming to the V1 'cruciform' geometry and the non-modulation group were involved as controls. A four-target phase-coded BCI system with SSVEPs at 60 Hz was implemented with the five modulation groups and questionnaires were used to evaluate user experience.Main results.The proposed individualized space and phase modulation method effectively modulated the SSVEP intensity without affecting the user experience. The online BCI system using the 60 Hz stimuli achieved mean information transfer rates of 52.8 ± 1.9 bits min^-1, 16.8 ± 2.4 bits min^-1, and 42.4 ± 3.0 bits min^-1with individualized optimal-modulation, individualized worst-modulation, and non-modulation groups, respectively.Significance.Structural and functional characteristics of the human visual cortex were exploited to enhance the response intensity of SSVEPs at 60 Hz, resulting in a high-performance BCI system with good user experience. This study has important theoretical significance and application value for promoting the development of the visual BCI technology.

Improving AR-SSVEP Recognition Accuracy Under High Ambient Brightness Through Iterative Learning

Augmented reality-based brain-computer interface (AR-BCI) system is one of the important ways to promote BCI technology outside of the laboratory due to its portability and mobility, but its performance in real-world scenarios has not been fully studied. In the current study, we first investigated the effect of ambient brightness on AR-BCI performance. 5 different light intensities were set as experimental conditions to simulate typical brightness in real scenes, while the same steady-state visual evoked potentials (SSVEP) stimulus was displayed in the AR glass. The data analysis results showed that SSVEP can be evoked under all 5 light intensities, but the response intensity became weaker when the brightness increased. The recognition accuracies of AR-SSVEP were negatively correlated to light intensity, the highest accuracies were 89.35% with FBCCA and 83.33% with CCA under 0 lux light intensity, while they decreased to 62.53% and 49.24% under 1200 lux. To solve the accuracy loss problem in high ambient brightness, we further designed a SSVEP recognition algorithm with iterative learning capability, named ensemble online adaptive CCA (eOACCA). The main strategy is to provide initial filters for high-intensity data by iteratively learning low-light-intensity AR-SSVEP data. The experimental results showed that the eOACCA algorithm had significant advantages under higher light intensities ( 600 lux). Compared with FBCCA, the accuracy of eOACCA under 1200 lux was increased by 13.91%. In conclusion, the current study contributed to the in-depth understanding of the performance variations of AR-BCI under different lighting conditions, and was helpful in promoting the AR-BCI application in complex lighting environments.

Training the spatially-coded SSVEP BCI on the fly

BACKGROUND

The spatially-coded SSVEP BCI employs the retinotopic map in the human visual pathway to infer the gaze direction of the operator relative to a flicker stimulus inducing steady-state visual evoked potentials (SSVEPs) in the brain. It has been shown that with this method, up to 16 channels can be encoded using only a single flicker stimulus. Another advantage over conventional frequency-coded SSVEP BCIs, in which channels are encoded by different combinations of frequencies and phases, is that the operator does not have to gaze directly at flickering lights. This can reduce visual fatigue and improve user comfort. Whereas the frequency of the SSVEP response is well predictable, which has enabled the development of frequency-coded SSVEP BCIs which do not require training data, the spatial distribution of the SSVEP response over the scalp differs much more between different people. This requires collecting a substantial amount of training data before the spatially-coded BCI could be put into operation.

NEW METHOD

In this study we address this issue by combining the spatially-coded BCI with a feedback channel which the operator uses to flag classification errors, and which allows the system to accumulate valid training data while the BCI is used to solve a spatial navigation task.

RESULTS

Starting from the minimal number of samples required by the classification method, the approach achieved an average accuracy of 69 ± 15 %, corresponding to an ITR of 31 ± 17 bits/min, in solving the task for the first time. This accuracy improved to 87 ± 9 % (ITR: 54 ± 14 bits/min) after completing the task 2 more times. Further we show that participants with a stable SSVEP topography over repeated stimulation enable the BCI to achieve higher accuracies.

COMPARISON WITH EXISTING METHODS

Compared to a similar system with separate training and application phases, the time to achieve the same output is reduced by more than 50 %.

CONCLUSIONS

Evaluating the approach in 17 participants suggests that the performance of the spatially-coded BCI with a minimal set of training samples is sufficient to be operational, and that performance keeps improving in the course of its application.

A Hybrid Brain-Computer Interface Based on Visual Evoked Potential and Pupillary Response

Brain-computer interface (BCI) based on steady-state visual evoked potential (SSVEP) has been widely studied due to the high information transfer rate (ITR), little user training, and wide subject applicability. However, there are also disadvantages such as visual discomfort and “BCI illiteracy.” To address these problems, this study proposes to use low-frequency stimulations (12 classes, 0.8–2.12 Hz with an interval of 0.12 Hz), which can simultaneously elicit visual evoked potential (VEP) and pupillary response (PR) to construct a hybrid BCI (h-BCI) system. Classification accuracy was calculated using supervised and unsupervised methods, respectively, and the hybrid accuracy was obtained using a decision fusion method to combine the information of VEP and PR. Online experimental results from 10 subjects showed that the averaged accuracy was 94.90 ± 2.34% (data length 1.5 s) for the supervised method and 91.88 ± 3.68% (data length 4 s) for the unsupervised method, which correspond to the ITR of 64.35 ± 3.07 bits/min (bpm) and 33.19 ± 2.38 bpm, respectively. Notably, the hybrid method achieved higher accuracy and ITR than that of VEP and PR for most subjects, especially for the short data length. Together with the subjects’ feedback on user experience, these results indicate that the proposed h-BCI with the low-frequency stimulation paradigm is more comfortable and favorable than the traditional SSVEP-BCI paradigm using the alpha frequency range.

Optimizing a left and right visual field biphasic stimulation paradigm for SSVEP-based BCIs with hairless region behind the ear

Objective.Steady-state visual evoked potential (SSVEP) based brain-computer interface (BCI) has the characteristics of fast communication speed, high stability, and wide applicability, thus it has been widely studied. With the rapid development in paradigm, algorithm, and system design, SSVEP-BCI is gradually applied in clinical and real-life scenarios. In order to improve the ease of use of the SSVEP-BCI system, many studies have been focusing on developing it on the hairless area, but due to the lack of redesigning the stimulation paradigm to better adapt to the new area, the electroencephalography response in the hairless area is worse than occipital region.Approach. This study first proposed a phase difference estimation method based on stimulating the left and right visual field separately, then developed and optimized a left and right visual field biphasic stimulation paradigm for SSVEP-based BCIs with hairless region behind the ear.Main results.In the 12-target online experiment, after a short model estimation training, all 16 subjects used their best stimulus condition. The paradigm designed in this study can increase the proportion of applicable subjects for the behind-ear SSVEP-BCI system from 58.3% to 75% and increase the accuracy from 74.6 ± 20.0% (the existing best SSVEP stimulus with hairless region behind the ear) to 84.2±14.7%, and the information transfer rate from 14.2±6.4 bits min^-1to 17.8±5.7 bits min^-1.Significance.These results demonstrated that the proposed paradigm can effectively improve the BCI performance using the signal from the hairless region behind the ear, compared with the standard SSVEP stimulation paradigm.

Detection of fixation points using a small visual landmark for brain-computer interfaces

Objective.The speed of visual brain-computer interfaces (v-BCIs) has been greatly improved in recent years. However, the traditional v-BCI paradigms require users to directly gaze at the intensive flickering items, which would cause severe problems such as visual fatigue and excessive visual resource consumption in practical applications. Therefore, it is imperative to develop a user-friendly v-BCI.Approach.According to the retina-cortical relationship, this study developed a novel BCI paradigm to detect the fixation point of eyes using a small visual stimulus that subtended only 0.6° in visual angle and was out of the central visual field. Specifically, the visual stimulus was treated as a landmark to judge the eccentricity and polar angle of the fixation point. Sixteen different fixation points were selected around the visual landmark, i.e. different combinations of two eccentricities (2° and 4°) and eight polar angles (0,π4,π2,3π4,π,5π4,3π2and7π4). Twelve subjects participated in this study, and they were asked to gaze at one out of the 16 points for each trial. A multi-class discriminative canonical pattern matching (Multi-DCPM) algorithm was proposed to decode the user's fixation point.Main results.We found the visual stimulation landmark elicited different spatial event-related potential patterns for different fixation points. Multi-DCPM could achieve an average accuracy of 66.2% with a standard deviation of 15.8% for the classification of the sixteen fixation points, which was significantly higher than traditional algorithms (p⩽0.001). Experimental results of this study demonstrate the feasibility of using a small visual stimulus as a landmark to track the relative position of the fixation point.Significance.The proposed new paradigm provides a potential approach to alleviate the problem of irritating stimuli in v-BCIs, which can broaden the applications of BCIs.

A Spatially-Coded Visual Brain-Computer Interface for Flexible Visual Spatial Information Decoding

Conventional visual BCIs, in which control channels are tagged with stimulation patterns to elicit distinguishable brain patterns, has made impressive progress in terms of the information transfer rates (ITRs). However, less development has been seen with respect to user experience and complexity of the technical setup. The requirement to tag each of targets by a unique stimulus substantially limits the flexibility of conventional visual BCI systems. A method for decoding the targets in the environment flexibly was therefore proposed in the present study. A BCI speller with thirteen symbols drawn on paper was developed. The symbols were interspersed with four flickers with distinct frequencies, but the user did not have to gaze at flickers. Rather, subjects could spell a sequence by looking at the symbols on the paper. In a cue-guided spelling task, the average offline and online accuracies reached 89.3± 7.3% and 90.3± 6.9% for 13 subjects, corresponding to ITRs of 43.0± 7.4 bit/min and 43.8± 6.8 bit/min. In an additional free-spelling task for seven out of thirteen subjects, an accuracy of 92.3± 3.1% and an ITR of 45.6± 3.3 bit/min were achieved. Analysis of a simulated online system showed the possibility to reach an average ITR of 105.8 bit/min by reducing the epoch duration from 4 to 1 second. Reliable BCI control is possible by gazing at targets in the environment instead of dedicated stimuli which encode control channels. The proposed method can drastically reduce the technical effort for visual BCIs and thereby advance their applications outside the laboratory.