A review of researches on decoding algorithms of steady-state visual evoked potentials / 生物医学工程学杂志

Brain-computer interface (BCI) systems based on steady-state visual evoked potential (SSVEP) have become one of the major paradigms in BCI research due to their high signal-to-noise ratio and short training time required by users. Fast and accurate decoding of SSVEP features is a crucial step in SSVEP-BCI research. However, the current researches lack a systematic overview of SSVEP decoding algorithms and analyses of the connections and differences between them, so it is difficult for researchers to choose the optimum algorithm under different situations. To address this problem, this paper focuses on the progress of SSVEP decoding algorithms in recent years and divides them into two categories-trained and non-trained-based on whether training data are needed. This paper also explains the fundamental theories and application scopes of decoding algorithms such as canonical correlation analysis (CCA), task-related component analysis (TRCA) and the extended algorithms, concludes the commonly used strategies for processing decoding algorithms, and discusses the challenges and opportunities in this field in the end.

Indoor simulation training system for brain-controlled wheelchair based on steady-state visual evoked potentials / 生物医学工程学杂志

Brain-controlled wheelchair (BCW) is one of the important applications of brain-computer interface (BCI) technology. The present research shows that simulation control training is of great significance for the application of BCW. In order to improve the BCW control ability of users and promote the application of BCW under the condition of safety, this paper builds an indoor simulation training system based on the steady-state visual evoked potentials for BCW. The system includes visual stimulus paradigm design and implementation, electroencephalogram acquisition and processing, indoor simulation environment modeling, path planning, and simulation wheelchair control, etc. To test the performance of the system, a training experiment involving three kinds of indoor path-control tasks is designed and 10 subjects were recruited for the 5-day training experiment. By comparing the results before and after the training experiment, it was found that the average number of commands in Task 1, Task 2, and Task 3 decreased by 29.5%, 21.4%, and 25.4%, respectively ( < 0.001). And the average number of commands used by the subjects to complete all tasks decreased by 25.4% ( < 0.001). The experimental results show that the training of subjects through the indoor simulation training system built in this paper can improve their proficiency and efficiency of BCW control to a certain extent, which verifies the practicability of the system and provides an effective assistant method to promote the indoor application of BCW.

Direct brain-controlled multi-robot cooperation task / 生物医学工程学杂志

Brain control is a new control method. The traditional brain-controlled robot is mainly used to control a single robot to accomplish a specific task. However, the brain-controlled multi-robot cooperation (MRC) task is a new topic to be studied. This paper presents an experimental research which received the "Innovation Creative Award" in the brain-computer interface (BCI) brain-controlled robot contest at the World Robot Contest. Two effective brain switches were set: total control brain switch and transfer switch, and BCI based steady-state visual evoked potentials (SSVEP) was adopted to navigate a humanoid robot and a mechanical arm to complete the cooperation task. Control test of 10 subjects showed that the excellent SSVEP-BCI can be used to achieve the MRC task by appropriately setting up the brain switches. This study is expected to provide inspiration for the future practical brain-controlled MRC task system.

Study on the feature extraction of combination of mVEP and SSVEP evoked by visual stimulus / 国际生物医学工程杂志

Objective To propose A brain computer interface paradigm based on the combination of the motion-onset visual evoked potential(mVEP) and the steady state visual evoked potential(SSVEP).Methods By designing a 3 ×3 character spelling matrix,a vertical white bar in the column of matrix which flicks at a pre-set frequency induced the corresponding SSVEP.The vertical white bar also randomly moved horizontally so as to induce mVEP.Then the two types of features were extracted by time frequency analysis.Finally the support vector machine was applied to compare the target character identification rate between the proposed paradigm and the single mVEP paradigm.Results The target character identification accuracy of subject S1 and S6 was improved by about 2％ comparing the proposed paradigm to the single paradigm.Other subjects achieved the improvement of 6％ for the same performance comparison.The averaged identification accuracy of the proposed paradigm could reach 91.4％ if the same stimulus repeated for more than 3 times,while the accuracy of single paradigm achieved 85.6％.Conclusions The proposed brain computer interface paradigm could effectively induce many kinds of brain feature signals.The identification accuracy by the proposed paradigm is higher than that by the single paradigm for various numbers of repeated trials.The proposed paradigm of combined visual stimulus merges the motion induction and the flash frequency modulation together and hence reduces the stimulation time and increases the efficiency of the feature extraction.