A high-speed hybrid brain-computer interface with more than 200 targets

Optimizing the proportion of stimulation area in a grid stimulus for user-friendly SSVEP-based BCIs

Objective.Steady-state visual evoked potentials (SSVEPs) rely on the photic driving response to encode electroencephalogram (EEG) signals stably and efficiently. However, the user experience of the traditional stimulation with high-contrast flickers urgently needs to be improved. In this study, we introduce a novel paradigm of grid stimulation with weak flickering perception, distinguished by a markedly lower proportion of stimulation area in the overall pattern.Approach.In an offline single-target experiment, we investigated the unique characteristics of SSVEPs evoked by varying proportions in grid stimuli within low and medium frequency bands. Based on the analysis of simulation performance across a four-class brain-computer interface (BCI) task and the evaluation of user experience questionnaires, a subset of paradigms that balance performance and comfort were selected for implementation in four-target online BCI systems.Main results.Our results demonstrate that even ultra-low stimulation proportion paradigms can still evoke strong responses within specific frequency bands, effectively enhancing user experience with low and middle frequency stimuli. Notably, proportions of 0.94% and 2.10% within the 3-5 Hz range provide an optimal balance between performance and user experience. For frequencies extending up to 15 Hz, a 2.10% proportion remains ideal. At 20 Hz, slightly higher proportions of 3.75% and 8.43% maintain these benefits.Significance.These findings are crucial for advancing the development of effective and user-friendly SSVEP-based BCI systems.

Enhancement of Hybrid BCI System Performance Based on Motor Imagery and SSVEP by Transcranial Alternating Current Stimulation

The hybrid brain-computer interface (BCI) is verified to reduce disadvantages of conventional BCI systems. Transcranial electrical stimulation (tES) can also improve the performance and applicability of BCI. However, enhancement in BCI performance attained solely from the perspective of users or solely from the angle of BCI system design is limited. In this study, a hybrid BCI system combining MI and SSVEP was proposed. Furthermore, transcranial alternating current stimulation (tACS) was utilized to enhance the performance of the proposed hybrid BCI system. The stimulation interface presented a depiction of grabbing a ball with both of hands, with left-hand and right-hand flickering at frequencies of 34 Hz and 35 Hz. Subjects watched the interface and imagined grabbing a ball with either left hand or right hand to perform SSVEP and MI task. The MI and SSVEP signals were processed separately using filter bank common spatial patterns (FBCSP) and filter bank canonical correlation analysis (FBCCA) algorithms, respectively. A fusion method was proposed to fuse the features extracted from MI and SSVEP. Twenty healthy subjects took part in the online experiment and underwent tACS sequentially. The fusion accuracy post-tACS reached 90.25% ± 11.40%, which was significantly different from pre-tACS. The fusion accuracy also surpassed MI accuracy and SSVEP accuracy respectively. These results indicated the superior performance of the hybrid BCI system and tACS would improve the performance of the hybrid BCI system.

Application of hybrid SSVEP + P300 brain computer interface to control avatar movement in mobile virtual reality gaming environment

INTRODUCTION

This research evaluated the feasibility of a hybrid SSVEP + P300 brain computer interface (BCI) for controlling the movement of an avatar in a virtual reality (VR) gaming environment (VR + BCI). Existing VR + BCI gaming environments have limitations, such as visual fatigue, a lower communication rate, minimum accuracy, and poor system comfort. Hence, there is a need for an optimized hybrid BCI system that can simultaneously evoke the strongest P300 and SSVEP potentials in the cortex.

METHODS

A BCI headset was coupled with a VR headset to generate a VR + BCI environment. The author developed a VR game in which the avatar's movement is controlled using the user's cortical responses with the help of a BCI headset. Specifically designed visual stimuli were used in the proposed system to elicit the strongest possible responses from the user's brain. The proposed system also includes an auditory feedback mechanism to facilitate precise avatar movement.

RESULTS AND CONCLUSIONS

Conventional P300 BCI and SSVEP BCI were also used to control the movements of the avatar, and their performance metrics were compared to those of the proposed system. The results demonstrated that the hybrid SSVEP + P300 BCI system was superior to the other systems for controlling avatar movement.

The use of happy faces as visual stimuli improves the performance of the hybrid SSVEP+P300 brain computer interface

BACKGROUND

This study illustrates a hybrid brain-computer interface (BCI) in which steady-state visual evoked potentials (SSVEP) and event-related potentials (P300) are evoked simultaneously. The goal of this study was to improve the performance of the current hybrid SSVEP+P300 BCI systems by incorporating a happy face into visual stimuli.

NEW METHOD

In this study, happy and sad faces were added to a visual stimulus to induce stronger cortical signals in a hybrid SSVEP+P300 BCI. Additionally, we developed a paradigm in which SSVEP responses were triggered by non-face stimuli, whereas P300 responses were triggered by face stimuli. We tested four paradigms: happy face paradigm (HF), sad face paradigm (SF), happy face and flicker paradigm (HFF), and sad face and flicker paradigm (SFF).

RESULTS AND CONCLUSIONS

The results demonstrated that the HFF paradigm elicited more robust cortical responses, which resulted in enhanced system accuracy and information transfer rate (ITR). The HFF paradigm has a system communication rate of 25.9 bits per second and an average accuracy of 96.1%. Compared with other paradigms, the HFF paradigm is the best choice for BCI applications because it has the highest ITR and maximum level of comfort.

The role of eye movement signals in non-invasive brain-computer interface typing system

Brain-Computer Interfaces (BCIs) have shown great potential in providing communication and control for individuals with severe motor disabilities. However, traditional BCIs that rely on electroencephalography (EEG) signals suffer from low information transfer rates and high variability across users. Recently, eye movement signals have emerged as a promising alternative due to their high accuracy and robustness. Eye movement signals are the electrical or mechanical signals generated by the movements and behaviors of the eyes, serving to denote the diverse forms of eye movements, such as fixations, smooth pursuit, and other oculomotor activities like blinking. This article presents a review of recent studies on the development of BCI typing systems that incorporate eye movement signals. We first discuss the basic principles of BCI and the recent advancements in text entry. Then, we provide a comprehensive summary of the latest advancements in BCI typing systems that leverage eye movement signals. This includes an in-depth analysis of hybrid BCIs that are built upon the integration of electrooculography (EOG) and eye tracking technology, aiming to enhance the performance and functionality of the system. Moreover, we highlight the advantages and limitations of different approaches, as well as potential future directions. Overall, eye movement signals hold great potential for enhancing the usability and accessibility of BCI typing systems, and further research in this area could lead to more effective communication and control for individuals with motor disabilities.

Effect of inverted faces as visual stimuli on the performance of the hybrid SSVEP + P300 brain computer interface

INTRODUCTION

This study proposes a hybrid brain-computer interface (BCI) system that simultaneously evokes steady-state visual evoked potentials (SSVEP) and event-related potentials (P300). The goal of this study was to improve the performance of the current hybrid SSVEP + P300 BCI systems by incorporating inverted faces into visual stimuli.

METHODS

In this study, upright and inverted faces were added to visual stimulus to elicit stronger cortical responses in a hybrid SSVEP + P300 BCI. We also considered triggering the P300 signals with facial stimuli and the SSVEP signals with non-facial stimuli. We have tested four paradigms: the upright face paradigm (UF), the inverted face paradigm (IF), the upright face and flicker paradigm (UFF), and the inverted face and flicker paradigm (IFF).

RESULTS AND CONCLUSIONS

The results showed that the IFF paradigm evoked more robust cortical responses, which led to enhanced system accuracy and ITR. The IFF paradigm had an average accuracy of 96.6% and a system communication rate of 26.45 bits per second. The UFF paradigm is the best candidate for BCI applications among other paradigms because it provides maximum comfort while maintaining a reasonable ITR.

High-Density Electroencephalogram Facilitates the Detection of Small Stimuli in Code-Modulated Visual Evoked Potential Brain-Computer Interfaces

In recent years, there has been a considerable amount of research on visual evoked potential (VEP)-based brain-computer interfaces (BCIs). However, it remains a big challenge to detect VEPs elicited by small visual stimuli. To address this challenge, this study employed a 256-electrode high-density electroencephalogram (EEG) cap with 66 electrodes in the parietal and occipital lobes to record EEG signals. An online BCI system based on code-modulated VEP (C-VEP) was designed and implemented with thirty targets modulated by a time-shifted binary pseudo-random sequence. A task-discriminant component analysis (TDCA) algorithm was employed for feature extraction and classification. The offline and online experiments were designed to assess EEG responses and classification performance for comparison across four different stimulus sizes at visual angles of 0.5°, 1°, 2°, and 3°. By optimizing the data length for each subject in the online experiment, information transfer rates (ITRs) of 126.48 ± 14.14 bits/min, 221.73 ± 15.69 bits/min, 258.39 ± 9.28 bits/min, and 266.40 ± 6.52 bits/min were achieved for 0.5°, 1°, 2°, and 3°, respectively. This study further compared the EEG features and classification performance of the 66-electrode layout from the 256-electrode EEG cap, the 32-electrode layout from the 128-electrode EEG cap, and the 21-electrode layout from the 64-electrode EEG cap, elucidating the pivotal importance of a higher electrode density in enhancing the performance of C-VEP BCI systems using small stimuli.

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.

Effects of the presentation order of stimulations in sequential ERP/SSVEP Hybrid Brain-Computer Interface

Hybrid Brain-Computer Interface (hBCI) combines multiple neurophysiology modalities or paradigms to speed up the output of a single command or produce multiple ones simultaneously. Concurrent hBCIs that employ endogenous and exogenous paradigms are limited by the reduced set of possible commands. Conversely, the fusion of different exogenous visual evoked potentials demonstrated impressive performances; however, they suffer from limited portability. Yet, sequential hBCIs did not receive much attention mainly due to slower transfer rate and user fatigue during prolonged BCI use (Lorenz et al 2014 J. Neural Eng. 11 035007). Moreover, the crucial factors for optimizing the hybridization remain under-explored. In this paper, we test the feasibility of sequential Event Related-Potentials (ERP) and Steady-State Visual Evoked Potentials (SSVEP) hBCI and study the effect of stimulus order presentation between ERP-SSVEP and SSVEP-ERP for the control of directions and speed of powered wheelchairs or mobile robots with 15 commands. Exploiting the fast single trial face stimulus ERP, SSVEP and modern efficient convolutional neural networks, the configuration with SSVEP presented at first achieved significantly (p < 0.05) higher average accuracy rate with 76.39% ( ± 7.30 standard deviation) hybrid command accuracy and an average Information Transfer Rate (ITR) of 25.05 ( ± 5.32 standard deviation) bits per minute (bpm). The results of the study demonstrate the suitability of a sequential SSVEP-ERP hBCI with challenging dry electroencephalography (EEG) electrodes and low-compute capacity. Although it presents lower ITR than concurrent hBCIs, our system presents an alternative in small screen settings when the conditions for concurrent hBCIs are difficult to satisfy.

MetaBCI: An open-source platform for brain-computer interfaces

BACKGROUND

Recently, brain-computer interfaces (BCIs) have attracted worldwide attention for their great potential in clinical and real-life applications. To implement a complete BCI system, one must set up several links to translate the brain intent into computer commands. However, there is not an open-source software platform that can cover all links of the BCI chain.

METHOD

This study developed a one-stop open-source BCI software, namely MetaBCI, to facilitate the construction of a BCI system. MetaBCI is written in Python, and has the functions of stimulus presentation (Brainstim), data loading and processing (Brainda), and online information flow (Brainflow). This paper introduces the detailed information of MetaBCI and presents four typical application cases.

RESULTS

The results showed that MetaBCI was an extensible and feature-rich software platform for BCI research and application, which could effectively encode, decode, and feedback brain activities.

CONCLUSIONS

MetaBCI can greatly lower the BCI's technical threshold for BCI beginners and can save time and cost to build up a practical BCI system. The source code is available at https://github.com/TBC-TJU/MetaBCI, expecting new contributions from the BCI community.

Almost free of calibration for SSVEP-based brain-computer interfaces

Objective. Steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) is a promising technology that can achieve high information transfer rate (ITR) with supervised algorithms such as ensemble task-related component analysis (eTRCA) and task-discriminant component analysis (TDCA). However, training individual models requires a tedious and time-consuming calibration process, which hinders the real-life use of SSVEP-BCIs. A recent data augmentation method, called source aliasing matrix estimation (SAME), can generate new EEG samples from a few calibration trials. But SAME does not exploit the information across stimuli as well as only reduces the number of calibration trials per command, so it still has some limitations.Approach. This study proposes an extended version of SAME, called multi-stimulus SAME (msSAME), which exploits the similarity of the aliasing matrix across frequencies to enhance the performance of SSVEP-BCI with insufficient calibration trials. We also propose a semi-supervised approach based on msSAME that can further reduce the number of SSVEP frequencies needed for calibration. We evaluate our method on two public datasets, Benchmark and BETA, and an online experiment.Main results. The results show that msSAME outperforms SAME for both eTRCA and TDCA on the public datasets. Moreover, the semi-supervised msSAME-based method achieves comparable performance to the fully calibrated methods and outperforms the conventional free-calibrated methods. Remarkably, our method only needs 24 s to calibrate 40 targets in the online experiment and achieves an average ITR of 213.8 bits min-1with a peak of 242.6 bits min-1.Significance. This study significantly reduces the calibration effort for individual SSVEP-BCIs, which is beneficial for developing practical plug-and-play SSVEP-BCIs.

Non-invasive EEG-based BCI spellers from the beginning to today: a mini-review

The defeat of the central motor neuron leads to the motor disorders. Patients lose the ability to control voluntary muscles, for example, of the upper limbs, which introduces a fundamental dissonance in the possibility of daily use of a computer or smartphone. As a result, the patients lose the ability to communicate with other people. The article presents the most popular paradigms used in the brain-computer-interface speller system and designed for typing by people with severe forms of the movement disorders. Brain-computer interfaces (BCIs) have emerged as a promising technology for individuals with communication impairments. BCI-spellers are systems that enable users to spell words by selecting letters on a computer screen using their brain activity. There are three main types of BCI-spellers: P300, motor imagery (MI), and steady-state visual evoked potential (SSVEP). However, each type has its own limitations, which has led to the development of hybrid BCI-spellers that combine the strengths of multiple types. Hybrid BCI-spellers can improve accuracy and reduce the training period required for users to become proficient. Overall, hybrid BCI-spellers have the potential to improve communication for individuals with impairments by combining the strengths of multiple types of BCI-spellers. In conclusion, BCI-spellers are a promising technology for individuals with communication impairments. P300, MI, and SSVEP are the three main types of BCI-spellers, each with their own advantages and limitations. Further research is needed to improve the accuracy and usability of BCI-spellers and to explore their potential applications in other areas such as gaming and virtual reality.

A novel visual brain-computer interfaces paradigm based on evoked related potentials evoked by weak and small number of stimuli

Introduction

Traditional visual Brain-Computer Interfaces (v-BCIs) usually use large-size stimuli to attract more attention from users and then elicit more distinct and robust EEG responses, which would cause visual fatigue and limit the length of use of the system. On the contrary, small-size stimuli always need multiple and repeated stimulus to code more instructions and increase separability among each code. These common v-BCIs paradigms can cause problems such as redundant coding, long calibration time, and visual fatigue.

Methods

To address these problems, this study presented a novel v-BCI paradigm using weak and small number of stimuli, and realized a nine-instruction v-BCI system that controlled by only three tiny stimuli. Each of these stimuli were located between instructions, occupied area with eccentricities subtended 0.4°, and flashed in the row-column paradigm. The weak stimuli around each instruction would evoke specific evoked related potentials (ERPs), and a template-matching method based on discriminative spatial pattern (DSP) was employed to recognize these ERPs containing the intention of users. Nine subjects participated in the offline and online experiments using this novel paradigm.

Results

The average accuracy of the offline experiment was 93.46% and the online average information transfer rate (ITR) was 120.95 bits/min. Notably, the highest online ITR achieved 177.5 bits/min.

Discussion

These results demonstrate the feasibility of using a weak and small number of stimuli to implement a friendly v-BCI. Furthermore, the proposed novel paradigm achieved higher ITR than traditional ones using ERPs as the controlled signal, which showed its superior performance and may have great potential of being widely used in various fields.

A hybrid P300-SSVEP brain-computer interface speller with a frequency enhanced row and column paradigm

Objective

This study proposes a new hybrid brain-computer interface (BCI) system to improve spelling accuracy and speed by stimulating P300 and steady-state visually evoked potential (SSVEP) in electroencephalography (EEG) signals.

Methods

A frequency enhanced row and column (FERC) paradigm is proposed to incorporate the frequency coding into the row and column (RC) paradigm so that the P300 and SSVEP signals can be evoked simultaneously. A flicker (white-black) with a specific frequency from 6.0 to 11.5 Hz with an interval of 0.5 Hz is assigned to one row or column of a 6 × 6 layout, and the row/column flashes are carried out in a pseudorandom sequence. A wavelet and support vector machine (SVM) combination is adopted for P300 detection, an ensemble task-related component analysis (TRCA) method is used for SSVEP detection, and the two detection possibilities are fused using a weight control approach.

Results

The implemented BCI speller achieved an accuracy of 94.29% and an information transfer rate (ITR) of 28.64 bit/min averaged across 10 subjects during the online tests. An accuracy of 96.86% is obtained during the offline calibration tests, higher than that of only using P300 (75.29%) or SSVEP (89.13%). The SVM in P300 outperformed the previous linear discrimination classifier and its variants (61.90–72.22%), and the ensemble TRCA in SSVEP outperformed the canonical correlation analysis method (73.33%).

Conclusion

The proposed hybrid FERC stimulus paradigm can improve the performance of the speller compared with the classical single stimulus paradigm. The implemented speller can achieve comparable accuracy and ITR to its state-of-the-art counterparts with advanced detection algorithms.