Hybrid BCI System Research Articles

A High-Rate Hybrid BCI System Based on High-Frequency SSVEP and sEMG.

Recently, various biosignals have been combined with electroencephalography (EEG) to build hybrid brain-computer interface (BCI) systems to improve system performance. Since steady-state visual evoked potential (SSVEP) and surface electromyography (sEMG) are easy-to-use, non-invasive techniques, and have high signal-to-noise ratio (SNR), hybrid BCI systems combining SSVEP and sEMG have received much attention in the BCI literature. However, most existing studies regarding hybrid BCIs based on SSVEP and sEMG adopt low-frequency visual stimuli to induce SSVEPs. The comfort of these systems needs further improvement to meet the practical application requirements. The present study realized a novel hybrid BCI combining high-frequency SSVEP and sEMG signals for spelling applications. EEG and sEMG were obtained simultaneously from the scalp and skin surface of subjects, respectively. These two types of signals were analyzed independently and then combined to determine the target stimulus. Our online results demonstrated that the developed hybrid BCI yielded a mean accuracy of 88.07 ± 1.43% and ITR of 159.12 ± 4.31 bits/min. These results exhibited the feasibility and effectiveness of fusing high-frequency SSVEP and sEMG towards improving the total BCI system performance.

An online hybrid BCI combining SSVEP and EOG-based eye movements.

Hybrid brain-computer interface (hBCI) refers to a system composed of a single-modality BCI and another system. In this paper, we propose an online hybrid BCI combining steady-state visual evoked potential (SSVEP) and eye movements to improve the performance of BCI systems. Twenty buttons corresponding to 20 characters are evenly distributed in the five regions of the GUI and flash at the same time to arouse SSVEP. At the end of the flash, the buttons in the four regions move in different directions, and the subject continues to stare at the target with eyes to generate the corresponding eye movements. The CCA method and FBCCA method were used to detect SSVEP, and the electrooculography (EOG) waveform was used to detect eye movements. Based on the EOG features, this paper proposes a decision-making method based on SSVEP and EOG, which can further improve the performance of the hybrid BCI system. Ten healthy students took part in our experiment, and the average accuracy and information transfer rate of the system were 94.75% and 108.63 bits/min, respectively.

Enhancing the Hybrid BCI Performance With the Common Frequency Pattern in Dual-Channel EEG.

The brain-computer interface establishes a direct communication pathway between the human brain and an external device by recognizing specific patterns in cortical activities. The principle of hybridization stands for combining at least two different BCI modalities into a single interface with the aim of improving the information transfer rate by increasing the recognition accuracy and number of choices available for the user. This study proposes a simultaneous hybrid BCI system that recognizes the motor imagery (MI) and the steady-state visually evoked potentials (SSVEP) using the EEG signals from a dual-channel EEG setting with sensors placed over the central area (C3 and C4 channels). The data processing implements a supervised optimization algorithm for the feature extraction, named the common frequency pattern, which finds the optimal spectral filter that maximizes the separability of the data by classes. The experiment compares the classification accuracy in a two-class task using the MI, SSVEP and hybrid approaches on seventeen healthy 18-29 years old subjects with various dual-channel setups and complete set of thirty EEG electrodes. The designed system reaches a high accuracy of 97.4 ± 1.1% in the hybrid task using the C3-C4 channel configuration, which is marginally lower than the 98.8 ± 0.5% accuracy achieved with the complete set of channels while applying the support vector classifier; in the plain SSVEP task the accuracy drops from 91.3 ± 3.9% to 86.0 ± 2.5% while moving from the occipital to central area under the dual-channel condition. The results demonstrate that by combining the principles of hybridization and data-driven spectral filtering for the feature selection it is feasible to compensate a lack of spatial information and implement the proposed BCI using a portable few channel EEG device even under sub-optimal conditions for the sensors placement.

An error-aware gaze-based keyboard by means of a hybrid BCI system

Gaze-based keyboards offer a flexible way for human-computer interaction in both disabled and able-bodied people. Besides their convenience, they still lead to error-prone human-computer interaction. Eye tracking devices may misinterpret user’s gaze resulting in typesetting errors, especially when operated in fast mode. As a potential remedy, we present a novel error detection system that aggregates the decision from two distinct subsystems, each one dealing with disparate data streams. The first subsystem operates on gaze-related measurements and exploits the eye-transition pattern to flag a typo. The second, is a brain-computer interface that utilizes a neural response, known as Error-Related Potentials (ErrPs), which is inherently generated whenever the subject observes an erroneous action. Based on the experimental data gathered from 10 participants under a spontaneous typesetting scenario, we first demonstrate that ErrP-based Brain Computer Interfaces can be indeed useful in the context of gaze-based typesetting, despite the putative contamination of EEG activity from the eye-movement artefact. Then, we show that the performance of this subsystem can be further improved by considering also the error detection from the gaze-related subsystem. Finally, the proposed bimodal error detection system is shown to significantly reduce the typesetting time in a gaze-based keyboard.

The hybrid BCI system for movement control by combining motor imagery and moving onset visual evoked potential

Objective. Movement control is an important application for EEG-BCI (EEG-based brain–computer interface) systems. A single-modality BCI cannot provide an efficient and natural control strategy, but a hybrid BCI system that combines two or more different tasks can effectively overcome the drawbacks encountered in single-modality BCI control. Approach. In the current paper, we developed a new hybrid BCI system by combining MI (motor imagery) and mVEP (motion-onset visual evoked potential), aiming to realize the more efficient 2D movement control of a cursor. Main result. The offline analysis demonstrates that the hybrid BCI system proposed in this paper could evoke the desired MI and mVEP signal features simultaneously, and both are very close to those evoked in the single-modality BCI task. Furthermore, the online 2D movement control experiment reveals that the proposed hybrid BCI system could provide more efficient and natural control commands. Significance. The proposed hybrid BCI system is compensative to realize efficient 2D movement control for a practical online system, especially for those situations in which P300 stimuli are not suitable to be applied.

Design of a Multimodal EEG-based Hybrid BCI System with Visual Servo Module

Current EEG-based brain-computer interface technologies mainly focus on how to independently use SSVEP, motor imagery, P300, or other signals to recognize human intention and generate several control commands. SSVEP and P300 require external stimulus, while motor imagery does not require it. However, the generated control commands of these methods are limited and cannot control a robot to provide satisfactory service to the user. Taking advantage of both SSVEP and motor imagery, this paper aims to design a hybrid BCI system that can provide multimodal BCI control commands to the robot. In this hybrid BCI system, three SSVEP signals are used to control the robot to move forward, turn left, and turn right; one motor imagery signal is used to control the robot to execute the grasp motion. In order to enhance the performance of the hybrid BCI system, a visual servo module is also developed to control the robot to execute the grasp task. The effect of the entire system is verified in a simulation platform and a real humanoid robot, respectively. The experimental results show that all of the subjects were able to successfully use this hybrid BCI system with relative ease.

An online hybrid brain-computer interface combining multiple physiological signals for webpage browse.

The hybrid brain computer interface (hBCI) could provide higher information transfer rate than did the classical BCIs. It included more than one brain-computer or human-machine interact paradigms, such as the combination of the P300 and SSVEP paradigms. Research firstly constructed independent subsystems of three different paradigms and tested each of them with online experiments. Then we constructed a serial hybrid BCI system which combined these paradigms to achieve the functions of typing letters, moving and clicking cursor, and switching among them for the purpose of browsing webpages. Five subjects were involved in this study. They all successfully realized these functions in the online tests. The subjects could achieve an accuracy above 90% after training, which met the requirement in operating the system efficiently. The results demonstrated that it was an efficient system capable of robustness, which provided an approach for the clinic application.

Towards Brain-Computer Interface Control of a 6-Degree-of-Freedom Robotic Arm Using Dry EEG Electrodes

Introduction. Development of a robotic arm that can be operated using an exoskeletal position sensing harness as well as a dry electrode brain-computer interface headset. Design priorities comprise an intuitive and immersive user interface, fast and smooth movement, portability, and cost minimization.Materials and Methods. A robotic arm prototype capable of moving along 6 degrees of freedom has been developed, along with an exoskeletal position sensing harness which was used to control it. Commercially available dry electrode BCI headsets were evaluated. A particular headset model has been selected and is currently being integrated into the hybrid system.Results and Discussion. The combined arm-harness system has been successfully tested and met its design targets for speed, smooth movement, and immersive control. Initial tests verify that an operator using the system can perform pick and place tasks following a rather short learning curve. Further evaluation experiments are planned for the integrated BCI-harness hybrid setup.Conclusions. It is possible to design a portable robotic arm interface comparable in size, dexterity, speed, and fluidity to the human arm at relatively low cost. The combined system achieved its design goals for intuitive and immersive robotic control and is currently being further developed into a hybrid BCI system for comparative experiments.