Multivariate Synchronization Index 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 Time-Local Weighted Transformation Recognition Framework for Steady State Visual Evoked Potentials Based Brain-Computer Interfaces.

Canonical correlation analysis (CCA), Multivariate synchronization index (MSI), and their extended methods have been widely used for target recognition in Brain-computer interfaces (BCIs) based on Steady State Visual Evoked Potentials (SSVEP), and covariance calculation is an important process for these algorithms. Some studies have proved that embedding time-local information into the covariance can optimize the recognition effect of the above algorithms. However, the optimization effect can only be observed from the recognition results and the improvement principle of time-local information cannot be explained. Therefore, we propose a time-local weighted transformation (TT) recognition framework that directly embeds the time-local information into the electroencephalography signal through weighted transformation. The influence mechanism of time-local information on the SSVEP signal can then be observed in the frequency domain. Low-frequency noise is suppressed on the premise of sacrificing part of the SSVEP fundamental frequency energy, the harmonic energy of SSVEP is enhanced at the cost of introducing a small amount of high-frequency noise. The experimental results show that the TT recognition framework can significantly improve the recognition ability of the algorithms and the separability of extracted features. Its enhancement effect is significantly better than the traditional time-local covariance extraction method, which has enormous application potential.

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.

Time-frequency feature extraction based on multivariable synchronization index for training-free SSVEP-based BCI.

Multivariate synchronization index (MSI), as an effective recognition algorithm for steady-state visual evoked potential (SSVEP) brain-computer interface (BCI), can accurately decode target frequencies without training. To further consider temporal features or extract harmonic components, extended MSI (EMSI), temporally local MSI (TMSI), and filter bank MSI (FBMSI) have been proposed. However, the promotion effects of the above three strategies on MSI have not been compared in detail. In this paper, the performance of EMSI, TMSI, and FBMSI under different time windows was analyzed with the same dataset. The results indicated that the improvement effect of the temporally local method on MSI was better than that of the other two methods under the short time window, and the effect of the filter bank method was better when the time window was greater than 0.8s. Based on the idea of simultaneously extracting time-frequency features, FBEMSI and FBTMSI were proposed by integrating time delay embedding and temporally local method into FBMSI respectively. The two improved methods, which has no significant difference, can improve the recognition effect of FBMSI. But the computing time of FBEMSI was shorter, which can be a potential method for SSVEP-BCI.

Bichromatic visual stimulus with subharmonic response to achieve a high-accuracy SSVEP BCI system with low eye irritation

ObjectiveWhile various Steady State Visual Evoked Potential Brain-Computer Interfaces (SSVEP BCI) with very high performance have already been developed, eye irritation is a significant burden in their practical use. Generally, there is a tradeoff between eye irritation and BCI accuracy. However, this study proposes a bichromatic visual stimulus with low eye irritation while providing accurately detectable electroencephalogram responses. MethodsThe proposed visual stimulus, which is high-frequency in terms of luminance but with a subharmonic color component, was compared to the conventional high-frequency SSVEP on 16 subjects. Canonical Correlation Analysis, Least Absolute Shrinkage and Selection Operator, Multivariate Synchronization Index (MSI), and Extended MSI were used separately to recognize aimed visual stimuli in a 4-target BCI system. ResultsThe proposed stimulus resulted in significantly (p-value = 0.024) higher accuracy (96 ± 4.1%) compared to the reference (87.1 ± 14.6%) for a processing window of 4.5 s using the Extended MSI algorithm. Eye irritation scores (3.8 ± 1.0 on a 1–9 scale) were also significantly lower (p-value = 0.035) than that of the reference stimulus (4.3 ± 1.1). No cases of illiteracy were observed with the proposed waveform, whereas there were 2 with the reference stimulus. ConclusionThe proposed bichromatic waveform provided significantly higher accuracy, especially for users who do not have strong high-frequency SSVEP responses. Eye irritation was also significantly lower than with conventional reference visual stimulus. The proposed visual stimulus can generate electroencephalogram responses, not only in the high-frequency range but also at half the main frequency. SignificanceThe results are significant for realizing practical, accurate SSVEP BCI systems.

Novel Hybrid Brain-Computer Interface for Virtual Reality Applications Using Steady-State Visual-Evoked Potential-Based Brain-Computer Interface and Electrooculogram-Based Eye Tracking for Increased Information Transfer Rate.

Brain–computer interfaces (BCIs) based on electroencephalogram (EEG) have recently attracted increasing attention in virtual reality (VR) applications as a promising tool for controlling virtual objects or generating commands in a “hands-free” manner. Video-oculography (VOG) has been frequently used as a tool to improve BCI performance by identifying the gaze location on the screen, however, current VOG devices are generally too expensive to be embedded in practical low-cost VR head-mounted display (HMD) systems. In this study, we proposed a novel calibration-free hybrid BCI system combining steady-state visual-evoked potential (SSVEP)-based BCI and electrooculogram (EOG)-based eye tracking to increase the information transfer rate (ITR) of a nine-target SSVEP-based BCI in VR environment. Experiments were repeated on three different frequency configurations of pattern-reversal checkerboard stimuli arranged in a 3 × 3 matrix. When a user was staring at one of the nine visual stimuli, the column containing the target stimulus was first identified based on the user’s horizontal eye movement direction (left, middle, or right) classified using horizontal EOG recorded from a pair of electrodes that can be readily incorporated with any existing VR-HMD systems. Note that the EOG can be recorded using the same amplifier for recording SSVEP, unlike the VOG system. Then, the target visual stimulus was identified among the three visual stimuli vertically arranged in the selected column using the extension of multivariate synchronization index (EMSI) algorithm, one of the widely used SSVEP detection algorithms. In our experiments with 20 participants wearing a commercial VR-HMD system, it was shown that both the accuracy and ITR of the proposed hybrid BCI were significantly increased compared to those of the traditional SSVEP-based BCI in VR environment.

Multivariate synchronization curve: A measure of synchronization in different multivariate signals.

As a method to measure the synchronization between two different sets of signals, the multivariate synchronization index (MSI) has played an irreplaceable role in the field of frequency recognition of brain-computer interface since it was proposed. On this basis, we make a generalization of MSI by using the escort distribution to replace the original distribution. In this way, MSI can be converted from a determined value to the multivariate synchronization curve, which will vary as the parameter q of the escort distribution changes. Numerical experiments are carried out on both simulated and real-world data to confirm the effectiveness of this new method. Compared with the case of MSI (i.e., q = 1), the extended form of MSI proposed in this article can obviously capture the relationship between signals more comprehensively, implying that it is a more perfect method to describe the synchronization between them. The results reveal that this method can not only effectively extract the important information contained in different signals, but also has the potential to become a practical synchronization measurement method of multivariate signals.

Relation of canonical correlation analysis and multivariate synchronization index in SSVEP detection

Canonical correlation analysis (CCA) and multivariate synchronization index (MSI) are two most extensively signal processing algorithms to detect steady-state visual evoked potentials (SSVEP) in brain-computer interfaces (BCI). However, there is no standard principle to choose the proper algorithm in a practical system. Conventionally, algorithm selection is determined by experimental comparisons. A drawback of this method is that the corresponding reference significance reduces sharply as the experimental conditions are changed. In this paper, the conversion relationship between the above algorithms is verified through a rigorous mathematical derivation, which also provides the basis for future signal processing algorithm selection. In addition, almost all the approaches designed to enhance CCA can directly transfer to MSI by the proposed method. Finally, the conclusions are validated with a benchmark data set.

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

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

Decoding SSVEP patterns from EEG via multivariate variational mode decomposition-informed canonical correlation analysis

Steady-state visual evoked potential (SSVEP) is one of the most popular neural patterns used to develop brain-computer interface (BCI). To address the issue of electroencephalogram (EEG) is easily interfered with noise artifacts and differences in SSVEP components between different channels and different frequency bands. We propose multivariate variational mode decomposition-informed canonical correlation analysis (MVMD-CCA) to improve the decoding performance of SSVEP patterns. Firstly, the multivariate variational mode decomposition method is used to decompose electroencephalogram into inherent mode functions (IMFs) of different frequency bands for minimizing the effects of artifacts. Then, the obtained inherent mode functions components of various frequencies and channels are weighted to reconstruct the electroencephalogram signal. Furthermore, it has a fast convergence speed and good stability when using the sparrow search algorithm (SSA) to optimize the weight parameters. Finally, the weighted reconstructed signal is classified by the canonical correlation analysis (CCA). An extensive experimental analysis is implemented with electroencephalogram collected from nine subjects use an eight-target SSVEP system. Results show that the multivariate variational mode decomposition-informed canonical correlation analysis significantly outperforms the canonical correlation analysis, with a maximum increase of 14.2% in SSVEP decoding accuracy. Simultaneously, the information transfer rate (ITR) increased by 6.5. An extensive comparison was performed between the proposed method and other competing approaches, including multivariate synchronization index (MSI), temporally local multivariate synchronization index (TMSI), and filter bank canonical correlation analysis (FBCCA). The superiority of our method demonstrates its great promise in the development of improved BCI systems.

Enhancing Performance of SSVEP-Based Visual Acuity via Spatial Filtering.

The purpose of this study was to enhance the performance of steady-state visual evoked potential (SSVEP)-based visual acuity assessment with spatial filtering methods. Using the vertical sinusoidal gratings at six spatial frequency steps as the visual stimuli for 11 subjects, SSVEPs were recorded from six occipital electrodes (O1, Oz, O2, PO3, POz, and PO4). Ten commonly used training-free spatial filtering methods, i.e., native combination (single-electrode), bipolar combination, Laplacian combination, average combination, common average reference (CAR), minimum energy combination (MEC), maximum contrast combination (MCC), canonical correlation analysis (CCA), multivariate synchronization index (MSI), and partial least squares (PLS), were compared for multielectrode signals combination in SSVEP visual acuity assessment by statistical analyses, e.g., Bland–Altman analysis and repeated-measures ANOVA. The SSVEP signal characteristics corresponding to each spatial filtering method were compared, determining the chosen spatial filtering methods of CCA and MSI with a higher performance than the native combination for further signal processing. After the visual acuity threshold estimation criterion, the agreement between the subjective Freiburg Visual Acuity and Contrast Test (FrACT) and SSVEP visual acuity for the native combination (0.253 logMAR), CCA (0.202 logMAR), and MSI (0.208 logMAR) was all good, and the difference between FrACT and SSVEP visual acuity was also all acceptable for the native combination (−0.095 logMAR), CCA (0.039 logMAR), and MSI (−0.080 logMAR), where CCA-based SSVEP visual acuity had the best performance and the native combination had the worst. The study proved that the performance of SSVEP-based visual acuity can be enhanced by spatial filtering methods of CCA and MSI and also recommended CCA as the spatial filtering method for multielectrode signals combination in SSVEP visual acuity assessment.

Enhancing detection of steady-state visual evoked potentials using channel ensemble method

Objective. This study proposed and evaluated a channel ensemble approach to enhance detection of steady-state visual evoked potentials (SSVEPs). Approach. Collected multi-channel electroencephalogram signals were classified into multiple groups of new analysis signals based on correlation analysis, and each group of analysis signals contained signals from a different number of electrode channels. These groups of analysis signals were used as the input of a training-free feature extraction model, and the obtained feature coefficients were converted into feature probability values using the softmax function. The ensemble value of multiple sets of feature probability values was determined and used as the final discrimination coefficient. Main results. Compared with canonical correlation analysis, likelihood ratio test, and multivariate synchronization index analysis methods using a standard approach, the recognition accuracies of the methods using a channel ensemble approach were improved by 5.05%, 3.87%, and 3.42%, and the information transfer rates (ITRs) were improved by 6.00%, 4.61%, and 3.71%, respectively. The channel ensemble method also obtained better recognition results than the standard algorithm on the public dataset. This study validated the efficiency of the proposed method to enhance the detection of SSVEPs, demonstrating its potential use in practical brain–computer interface (BCI) systems. Significance. A SSVEP-based BCI system using a channel ensemble method could achieve high ITR, indicating great potential of this design for various applications with improved control and interaction.

Filter Bank-Driven Multivariate Synchronization Index for Training-Free SSVEP BCI.

In recent years, multivariate synchronization index (MSI) algorithm, as a novel frequency detection method, has attracted increasing attentions in the study of brain-computer interfaces (BCIs) based on steady state visual evoked potential (SSVEP). However, MSI algorithm is hard to fully exploit SSVEP-related harmonic components in the electroencephalogram (EEG), which limits the application of MSI algorithm in BCI systems. In this paper, we propose a novel filter bank-driven MSI algorithm (FBMSI) to overcome the limitation and further improve the accuracy of SSVEP recognition. We evaluate the efficacy of the FBMSI method by developing a 6-command SSVEP-NAO robot system with extensive experimental analyses. An offline experimental study is first performed with EEG collected from nine subjects to investigate the effects of varying parameters on the model performance. Offline results show that the proposed method has achieved a stable improvement effect. We further conduct an online experiment with six subjects to assess the efficacy of the developed FBMSI algorithm in a real-time BCI application. The online experimental results show that the FBMSI algorithm yields a promising average accuracy of 83.56% using a data length of even only one second, which was 12.26% higher than the standard MSI algorithm. These extensive experimental results confirmed the effectiveness of the FBMSI algorithm in SSVEP recognition and demonstrated its potential application in the development of improved BCI systems.

SSVEP signal classification and recognition based on individual signal mixing template multivariate synchronization index algorithm

With the development of automation technology, Brain Computer Interface (BCI) has been increasingly integrated into people's daily life, among which Steady State Visual Evoked Potential (SSVEP) has attracted much attention due to its high signal-to-noise ratio (SNR) and wide application scenarios. To improve the classification accuracy of SSVEP signals, a novel individual signal mixing template multivariate synchronization index algorithm (IST-MSI) was proposed in this paper, which incorporated individual training template and individual harmonic sensitivity coefficient into the standard MSI algorithm. Specifically, the proposed method first enlarged the frequency-domain power spectrum of the fundamental frequency and its harmonics to reduce the redundant information in the individual training template. The synchronization index values at non-target frequency identified by MSI algorithm are significantly reduced through unequal ratio scaling of harmonic sensitivity coefficient, thereby improving the SSVEP recognition. The experimental results showed that under the signal length of 1.2 s, the average classification accuracy of IST-MSI algorithm reached 84.3 % in six target frequencies, which was 5.8 % higher than that of standard MSI algorithm. This study confirmed the efficacy of the proposed IST-MSI algorithm for SSVEP recognition, demonstrating its promise in developing an improved BCI system.

Inter- and Intra-subject Template-Based Multivariate Synchronization Index Using an Adaptive Threshold for SSVEP-Based BCIs

The steady-state visually evoked potential (SSVEP) has been widely used in brain-computer interfaces (BCIs). Many studies have proved that the Multivariate synchronization index (MSI) is an efficient method for recognizing the frequency components in SSVEP-based BCIs. Despite its success, the recognition accuracy has not been satisfactory because the simplified pre-constructed sine-cosine waves lack abundant features from the real electroencephalogram (EEG) data. Recent advances in addressing this issue have achieved a significant improvement in recognition accuracy by using individual calibration data. In this study, a new extension based on inter- and intra-subject template signals is introduced to improve the performance of the standard MSI method. Through template transfer, inter-subject similarity and variability are employed to enhance the robustness of SSVEP recognition. Additionally, most existed methods for SSVEP recognition utilize a fixed time window (TW) to perform frequency domain analysis, which limits the information transfer rate (ITR) of BCIs. For addressing this problem, a novel adaptive threshold strategy is integrated into the extension of MSI, which uses a dynamic window to extract the temporal features of SSVEPs and recognizes the stimulus frequency based on a pre-set threshold. The pre-set threshold contributes to obtaining an appropriate and shorter signal length for frequency recognition and filtering ignored-invalid trials. The proposed method is evaluated on a 12-class SSVEP dataset recorded from 10 subjects, and the result shows that this achieves higher recognition accuracy and information transfer rate when compared with the CCA, MSI, Multi-set CCA, and Individual Template-based CCA. This paper demonstrates that the proposed method is a promising approach for developing high-speed BCIs.

Brain–Robot Interface-Based Navigation Control of a Mobile Robot in Corridor Environments

This paper proposes a brain–robot interface (BRI)-based control strategy in combination with the simultaneous localization and mapping (SLAM) to achieve the navigation and control of a mobile robot in uncertain environments. The BRI is based on steady state visually evoked potentials, utilizing the multivariate synchronization index classification algorithm to analyze the human electroencephalograph (EEG) signals in such a manner that human intentions can be recognized and motion commands can be produced for the brain controlled robot. The entire system is semi-autonomous since the navigation of mobile robot is commanded by the BRI, and the low-level motion of the mobile robot is autonomous with a designed kinematic controller. By utilizing vanishing points and door plates as the environmental features, a global metric map of the environment has been built by a sequential SLAM algorithm. The main contribution of this paper is the combination of an artificial potential field (APF) and the brain signals, which builds up the relationship between the strength of EEG signals and the intensity of the potential field. Through the proposed EEG-APF method, motion commands that would plan an obstacle-free trajectory in un-structured environments, can be obtained. The entire system has been tested with eight volunteer subjects, and all subjects are able to successfully fulfill manipulating mobile robot in the experiments.

АЛГОРИТМИЧЕСКОЕ ОБЕСПЕЧЕНИЕ ИНТЕРФЕЙСА УПРАВЛЕНИЯ РОБОТ-ЧЕЛОВЕК ПРИ ВЫДЕЛЕНИИ ЗРИТЕЛЬНЫХ ВЫЗВАННЫХ ПОТЕНЦИАЛОВ НА ОСНОВЕ МНОГОМЕРНОГО ИНДЕКСА СИНХРОНИЗАЦИИ

The aim of the study is to build human-machine control systems. The main methods for con-structing such systems, methods for isolating evoked potentials in electroencephalograms. The article presents studies of electroencephalogram signals with steady state visual evoked potentials for different frequencies of photostimulation, based on the method of multivariate synchronization index. The influence of the length of the processed window on the accuracy of recognition of the frequency of the studied signal is considered. In the course of research, the authors verify the need for pre-processing of the original signals by means of bandpass signal filtering. In addition, the possibility of using a multi-dimensional synchronization index in multi-channel mode is being considered. The result of the authors study is recommendations on the parameters used to high-light the established visual evoked potentials in the method of multivariate synchronization index. The possibility of using algorithms based on a multivariate synchronization index in real time is shown. The results obtained are of practical importance, since they can be used to build neurocomputer interfaces based on visual evoked potentials and can be further used in the for-mation of control theory of robotic systems for various purposes and in the implementation of solutions for the organization of human-machine interaction in narrow practical problems.

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.

Assessment of high-frequency steady-state visual evoked potentials from below-the-hairline areas for a brain-computer interface based on Depth-of-Field

Background and ObjectiveRecently, a promising Brain-Computer Interface based on Steady-State Visual Evoked Potential (SSVEP-BCI) was proposed, which composed of two stimuli presented together in the center of the subject’s field of view, but at different depth planes (Depth-of-Field setup). Thus, users were easily able to select one of them by shifting their eye focus. However, in that work, EEG signals were collected through electrodes placed on occipital and parietal regions (hair-covered areas), which demanded a long preparation time. Also, that work used low-frequency stimuli, which can produce visual fatigue and increase the risk of photosensitive epileptic seizures. In order to improve the practicality and visual comfort, this work proposes a BCI based on Depth-of-Field using the high-frequency SSVEP response measured from below-the-hairline areas (behind-the-ears). MethodsTwo high-frequency stimuli (31 Hz and 32 Hz) were used in a Depth-of-Field setup to study the SSVEP response from behind-the-ears (TP9 and TP10). Multivariate Spectral F-test (MSFT) method was used to verify the elicited response. Afterwards, a BCI was proposed to command a mobile robot in a virtual reality environment. The commands were recognized through Temporally Local Multivariate Synchronization Index (TMSI) method. ResultsThe data analysis reveal that the focused stimuli elicit distinguishable SSVEP response when measured from hairless areas, in spite of the fact that the non-focused stimulus is also present in the field of view. Also, our BCI shows a satisfactory result, reaching average accuracy of 91.6% and Information Transfer Rate (ITR) of 5.3 bits/min. ConclusionThese findings contribute to the development of more safe and practical BCI.

Brain Teleoperation Control of a Nonholonomic Mobile Robot Using Quadrupole Potential Function

This paper presents the development of a brain-machine interfacing teleoperation control framework of a mobile robot using quadrupole potential function (QPF). The online brain-computer interface is based on steady-state visually evoked potentials, which employs the multivariate synchronization index classification algorithm to decode the human electroencephalograph (EEG) signals. In this way, human intentions can be recognized and EEG control commands can be produced for the mobile robot. Besides, a novel artificial potential function named QPF is designed to produce the potential fields to parameterize the EEG control commands. The classification results of the EEG signal are related to the distribution of the potential fields, and the collision-free path to a target position is automatically planned by the potential field. Such semi-automatic design builds the direct mapping from human intentions to the mobile robot's behavior and reduces mental effort or exhaustion on subject's part. Besides, based on the QPF with the capability of handling nonholonomic constraints, the integrated system can not only achieve obstacle avoidance but is also able to automatically stabilize the robot to a target configuration. Extensive experiment studies were conducted on several subjects to demonstrate the effectiveness of the proposed approaches.