视觉P300脑机接口中的SOA扰动现象

异步操控性是脑机接口走向实际应用的关键技术之一。其关键点在于寻找一种可有效区分脑机接口工作状态和空闲状态的指标。建立了针对P300脑机接口的刺激起始异步(SOA)扰动模型,在仿真实验和实测数据中观察到SOA扰动谱线,并根据所提出的模型给出了合理解释。研究了SOA扰动的频域特性,结果表明,当SOA位于220ms附近时,SOA扰动的强度最大,而当SOA低于150ms时,SOA扰动强度将急剧减小。同时SOA扰动所具有的锁相性使得可以通过时域相干平均法进一步提高信噪比。SOA扰动可作为脑机接口处于工作状态的标志,为异步脑机接口的实现提供了一种新的研究思路。

Toward the Optimization of the Region-Based P300 Speller

Technology has tremendously contributed to improving communication and facilitating daily activities.Brain-Computer Interface(BCI)study particularly emerged from the need to serve people with disabilities such as Amyotrophic Lateral Sclerosis(ALS).However,with the advancements in cost-effective electronics and computer interface equipment,the BCI study is flourishing,and the exploration of BCI applications for people without disabilities,to enhance normal functioning,is increasing.Particularly,the P300-based spellers are among the most promising applications of the BCI technology.In this context,the region-based paradigm for P300 BCI spellers was introduced in an effort to reduce the crowding effect and adjacency problem that might affect the detection of P300 peak.This study extends this line of research by investigating the effect,in terms of accuracy and usability,of the letters’distribution among the speller’s regions.For this purpose,a clustering algorithm is proposed,and two region-based layouts were generated by redistributing the letters based on their dissimilarity or their similarity.A pilot usability evaluation was also conducted in order to assess the usability of the different layouts in terms of effectiveness,efficiency,and satisfaction.The results indicate that the distribution of the letters has an effect on the classification accuracy as well as the user experience.Particularly,when considering short-term accuracy and cognitive effort,the original region-based layout outperforms other layouts.

Optimizing feature vectors and removal unnecessary channels in BCI speller application

In this paper we will discuss novel algorithms to develop the brain-computer interface (BCI) system in speller application based on single-trial classification of electroencephalogram (EEG) signal. The idea is to employ proper methods for reducing the number of channels and optimizing feature vectors. Removal unnecessary channels and reducing feature dimension result in cost decrement, time saving and improve the BCI implementation eventually. Optimal channels will be gotten after two stages sifting. In the first stage, the channels reduced up to 30% based on channels of the important event related potential (ERP) components and in the next stage, optimal channels were extracted by backward forward selection (BFS) algorithm. Also we will show that suitable single-trial analysis requires applying proper feature vector that was constructed by recognizing important ERP components, so as to propose an algorithm to distinguish less important features in feature vectors. F-Score criteria used to recognize effective features which created more discrimination between different classes and feature vectors were reconstructed based on effective features. Our algorithm has tested on dataset II of BCI competition III. The results show that we achieve accuracy up to 31% in single-trial, which is better than the performance of winner who is in this competition (about 25.5%). Also we use simple classifier and few channels to compute output performances while more complicated classifier and all channels are used by them.

Overview of recognition methods for SSVEP-based BCIs in World Robot Contest 2022: MATLAB undergraduate group

The steady-state visual evoked potential(SSVEP)-based speller has emerged as a widely adopted paradigm in current brain–computer interface(BCI) systems due to its rapid processing and consistent performance across different individuals. Calibration-free SSVEP algorithms, as opposed to their calibration-based counterparts, offer clear and intuitive mathematical principles, making them accessible to novice developers. During the World Robot Contest(WRC)2022, participants in the undergraduate category utilized various approaches to accomplish target detection in the calibration-free setting, successfully implementing the algorithms using MATLAB.The winning approach achieved an average information transfer rate of 198.94 bits/min in the final test, which is notably high given the calibration-free scenario. This paper presents an introduction to the underlying principles of the selected methods, accompanied by a comparison of their effectiveness through analysis of results from both the final test and offline experiments. Additionally, we propose that the youth competition of WRC could serve as an ideal starting point for beginners interested in studying and developing their own BCI systems.

Overview of the winning approaches in BCI Controlled Robot Contest in World Robot Contest 2021:Calibration-free SSVEP

Recently,steady-state visual evoked potential(SSVEP)has become one of the most popular electroencephalography paradigms due to its high information transfer rate.Several approaches have been proposed to improve the performance of SSVEP.The calibration-free scenario is significant in SSVEP-based brain-computer interface systems,where the subject is the first time to use the system.The participating teams proposed several effective calibration-free algorithm frameworks in the SSVEP competition(calibration-free)of the BCI Controlled Robot Contest in World Robot Contest 2021.This paper introduces the approaches used in the algorithms of the top five teams in the final.The results of the five subjects in the final proved the effectiveness of the approaches.This paper discusses the effectiveness of each approach in improving the system performance in the calibration-free scenario and gives suggestions on how to use these approaches in a real-world system.

Exploiting Sparse Representation in the P300 Speller Paradigm

A Brain-Computer Interface(BCI) aims to produce a new way for people to communicate with computers.Brain signal classification is a challenging issue owing to the high-dimensional data and low Signal-to-Noise Ratio(SNR). In this paper, a novel method is proposed to cope with this problem through sparse representation for the P300 speller paradigm. This work is distinguished using two key contributions. First, we investigate sparse coding and its feasibility for brain signal classification. Training signals are used to learn the dictionaries and test signals are classified according to their sparse representation and reconstruction errors. Second, sample selection and a channel-aware dictionary are proposed to reduce the effect of noise, which can improve performance and enhance the computing efficiency simultaneously. A novel classification method from the sample set perspective is proposed to exploit channel correlations. Specifically, the brain signal of each channel is classified jointly using its spatially neighboring channels and a novel weighted regulation strategy is proposed to overcome outliers in the group. Experimental results have demonstrated that our methods are highly effective. We achieve a state-of-the-art recognition rate of 72.5%, 88.5%, and 98.5% at 5, 10, and 15 epochs, respectively, on BCI Competition Ⅲ Dataset Ⅱ.