High-Frequency Discrete-Interval Binary Sequence in Asynchronous C-VEP-Based BCI for Visual Fatigue Reduction.

In code-modulated visual evoked potential (c-VEP) based BCI systems, flickering visual stimuli may result in visual fatigue. Thus, we introduced a discrete-interval binary sequence (DIBS) as visual stimulus modulation, with its power spectrum optimized to emphasize high-frequency components (40 Hz-60 Hz). 8 and 17 subjects participated, respectively, in offline and online experiments on a 4-target asynchronous c-VEP-based BCI system designed to realize a high positive predictive value (PPV), a low false positive rate (FPR) during idle states, and a high true positive rate (TPR) in control states, while minimizing visual fatigue level. Two visual stimuli modulations were introduced and compared: a maximum length sequence (m-sequence) and the high-frequency discrete-interval binary sequence (DIBS). The decoding algorithm was compared among the canonical correlation analysis (CCA), the task-related component analysis (TRCA), and two approaches of sub-band component weight calculation (the traditional method and the proportional method) for FBCCA and FBTRCA. In the online experiments, the average PPV, FPR and TPR achieved, respectively [Formula: see text], [Formula: see text], [Formula: see text] with m-sequence, while [Formula: see text], [Formula: see text] and [Formula: see text] with DIBS. Estimated by objective eye-related metrics and a subjective questionnaire, the visual fatigue in DIBS cases is significantly smaller than that in m-sequence cases. In this study, the feasibility of a novel modulation approach for visual fatigue reduction was proved in an asynchronous c-VEP system, while maintaining comparable performance to existing methods, which provides further insights towards enhancing this field's long-term viability and user-friendliness.

Effect of motion state variability on error-related potentials during continuous feedback paradigms and their consequences for classification.

BACKGROUND: An erroneous motion would elicit the error-related potential (ErrP) when humans monitor the behavior of the external devices. This EEG modality has been largely applied to brain-computer interface in an active or passive manner with discrete visual feedback. However, the effect of variable motion state on ErrP morphology and classification performance raises concerns when the interaction is conducted with continuous visual feedback. NEW METHOD: In the present study, we designed a cursor control experiment. Participants monitored a continuously moving cursor to reach the target on one side of the screen. Motion state varied multiple times with two factors: (1) motion direction and (2) motion speed. The effects of these two factors on the morphological characteristics and classification performance of ErrP were analyzed. Furthermore, an offline simulation was performed to evaluate the effectiveness of the proposed extended ErrP-decoder in resolving the interference by motion direction changes. RESULTS: The statistical analyses revealed that motion direction and motion speed significantly influenced the amplitude of feedback-ERN and frontal-Pe components, while only motion direction significantly affected the classification performance. COMPARISON WITH EXISTING METHODS: Significant deviation was found in ErrP detection utilizing classical correct-versus-erroneous event training. However, this bias can be alleviated by 16% by the extended ErrP-decoder. CONCLUSION: The morphology and classification performance of ErrP signal can be affected by motion state variability during continuous feedback paradigms. The results enhance the comprehension of ErrP morphological components and shed light on the detection of BCI's error behavior in practical continuous control.

Phase-Locked Time-Shift Data Augmentation Method for SSVEP Brain-Computer Interfaces.

Steady-state visual evoked potential (SSVEP) based brain-computer interfaces (BCIs) have achieved an information transfer rate (ITR) of over 300 bits/min, but abundant training data is required. The performance of SSVEP algorithms deteriorates greatly under limited data, and the existing time-shift data augmentation method fails to improve it because the phase-locked requirement between training samples is violated. To address this issue, this study proposes a novel augmentation method, namely phase-locked time-shift (PLTS), for SSVEP-BCI. The similarity between epochs at different time moments was evaluated, and a unique time-shift step was calculated for each class to augment additional data epochs in each trial. The results showed that the PLTS significantly improved the classification performance of SSVEP algorithms on the BETA SSVEP datasets. Moreover, under the condition of one calibration block, by slightly prolonging the calibration duration (from 48 s to 51.5 s), the ITR increased from 40.88±4.54 bits/min to 122.61±7.05 bits/min with the PLTS. This study provides a new perspective on augmenting data epochs for training-based SSVEP-BCI, promotes the classification accuracy and ITR under limited training data, and thus facilitates the real-life applications of SSVEP-based brain spellers.

Decoding movement frequencies and limbs based on steady-state movement-related rhythms from noninvasive EEG.

Objective.Decoding different types of movements noninvasively from electroencephalography (EEG) is an essential topic in neural engineering, especially in brain-computer interface. Although the widely used sensorimotor rhythm (SMR) is efficient in limb decoding, it lacks efficacy in decoding movement frequencies. Accumulating evidence supports the notion that the movement frequency is encoded in the steady-state movement-related rhythm (SSMRR). Our study has two primary objectives: firstly, to investigate the spatial-spectral representation of SSMRR in EEG during voluntary movements; secondly, to assess whether movement frequencies and limbs can be effectively decoded based on SSMRR.Approach.To comprehensively examine the representation of SSMRR, we investigated the frequency characteristics and spatial patterns associated with various rhythmic finger movements. Coherence analysis was performed between the sensor or source domain EEG and finger movements recorded by data gloves. A fusion model based on spectral SNR features and filter-bank common spatial pattern features was utilized to decode movement frequencies and limbs.Main results.At the group-level, sensor domain, and source domain coherence maps demonstrated that the accurate movement frequency (f0) and its first harmonic (f1) were encoded in the contralateral motor cortex. For the four-class classification, including two movement frequencies for both hands, the decoding accuracies for externally paced and internally paced movements were 73.14 ± 15.86% and 66.30 ± 17.26% (averaged across ten subjects, chance levels at 31.05% and 30.96%). Notably, the average results of five subjects with the highest decoding accuracies reached 87.21 ± 7.44% and 80.44 ± 7.99%.Significance.Our results verified the EEG representation of SSMRR and proved that the movement frequency and limb could be effectively decoded based on spatial-spectral features extracted from SSMRR. We suggest that SSMRR can serve as a complement to SMR to expand the range of decodable movement types and the approaches of limb decoding.

A Calibration-Free Hybrid Approach Combining SSVEP and EOG for Continuous Control.

While SSVEP-BCI has been widely developed to control external devices, most of them rely on the discrete control strategy. The continuous SSVEP-BCI enables users to continuously deliver commands and receive real-time feedback from the devices, but it suffers from the transition state problem, a period the erroneous recognition, when users shift their gazes between targets. To resolve this issue, we proposed a novel calibration-free Bayesian approach by hybridizing SSVEP and electrooculography (EOG). First, canonical correlation analysis (CCA) was applied to detect the evoked SSVEPs, and saccade during the gaze shift was detected by EOG data using an adaptive threshold method. Then, the new target after the gaze shift was recognized based on a Bayesian optimization approach, which combined the detection of SSVEP and saccade together and calculated the optimized probability distribution of the targets. Eighteen healthy subjects participated in the offline and online experiments. The offline experiments showed that the proposed hybrid BCI had significantly higher overall continuous accuracy and shorter gaze-shifting time compared to FBCCA, CCA, MEC, and PSDA. In online experiments, the proposed hybrid BCI significantly outperformed CCA-based SSVEP-BCI in terms of continuous accuracy (77.61 ± 1.36%vs. 68.86 ± 1.08% and gaze-shifting time (0.93 ± 0.06s vs. 1.94 ± 0.08s). Additionally, participants also perceived a significant improvement over the CCA-based SSVEP-BCI when the newly proposed decoding approach was used. These results validated the efficacy of the proposed hybrid Bayesian approach for the BCI continuous control without any calibration. This study provides an effective framework for combining SSVEP and EOG, and promotes the potential applications of plug-and-play BCIs in continuous control.

BCI Control of a Robotic Arm Based on SSVEP With Moving Stimuli for Reach and Grasp Tasks.

Brain-computer interface (BCI) provides a novel technology for patients and healthy human subjects to control a robotic arm. Currently, BCI control of a robotic arm to complete the reaching and grasping tasks in an unstructured environment is still challenging because the current BCI technology does not meet the requirement of manipulating a multi-degree robotic arm accurately and robustly. BCI based on steady-state visual evoked potential (SSVEP) could output a high information transfer rate; however, the conventional SSVEP paradigm failed to control a robotic arm to move continuously and accurately because the users have to switch their gaze between the flickering stimuli and the target frequently. This study proposed a novel SSVEP paradigm in which the flickering stimuli were attached to the robotic arm's gripper and moved with it. First, an offline experiment was designed to investigate the effects of moving flickering stimuli on the SSVEP's responses and decoding accuracy. After that, contrast experiments were conducted, and twelve subjects were recruited to participate in a robotic arm control experiment using both the paradigm one (P1, with moving flickering stimuli) and the paradigm two (P2, conventional fixed flickering stimuli) using a block randomization design to balance their sequences. Double blinks were used to trigger the grasping action asynchronously whenever the subjects were confident that the position of the robotic arm's gripper was accurate enough. Experimental results showed that the paradigm P1 with moving flickering stimuli provided a much better control performance than the conventional paradigm P2 in completing a reaching and grasping task in an unstructured environment. Subjects' subjective feedback scored by a NASA-TLX mental workload scale also corroborated the BCI control performance. The results of this study suggest that the proposed control interface based on SSVEP BCI provides a better solution for robotic arm control to complete the accurate reaching and grasping tasks.

A novel SEA-based haptic force feedback master hand controller for robotic endovascular intervention system.

Robotic Endovascular Intervention System (REIS) has been a focused and interesting area in robot-assisted telesurgery. While, haptic feedback is the latest advancing study in interventional robots. Few systems with haptic feedback are commercialized due to accuracy, instantaneity, and the lack of surgeon previous experience on interventional surgery. In this article, a novel haptic force feedback master hand controller system is proposed to solve the problems. A SEA (Series Elastic Actuators)-based mechanism is designed to provide feedback force with high force/torque fidelity, low impedance, and inertia. Also, the handle of the mechanism is similar to the catheter, making the surgeon operate the telesurgery with his experience. The control model of the system is built, and PID algorithms are explored to realize the motion control. PID parameters are optimized for fast response time and stability. The experiments and results demonstrate that the master hand controller system has maximum feedback force relative error of 3%, instantaneity of 0.89 second and tracking performance of 9 Hz frequency 3 dB bandwidth.