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The Turing Test is a method of testing whether a machine has human intelligence. A novel brain–computer interface (BCI) Turing Test was proposed in the BCI Controlled Robot Contest in World Robot Contest 2022. Contestants developed algorithms that can distinguish if an instruction is issued by a human. Participants collaborated with an electroencephalogram-based BCI to play a soccer game in a virtual scenario. Participants were asked to perform steady-state visual evoked potential (SSVEP) tasks or motor imagery (MI) tasks to control the robots or be in an idle state to mimic the system giving instructions on behalf of the participants. Several algorithms proposed in this competition are developed based on the concept that the idle state is a category in multiclass classification problems. This paper details the algorithms of the top five teams with the best performance in the final, lists the popular classification models and algorithms for MI and SSVEP, and discusses the effectiveness of each approach in improving classification performance and reducing the computation time. [ABSTRACT FROM AUTHOR]

A brain–computer interface (BCI) based on motor imagery (MI) translates the subject’s motor intention into a control signal through classifying electroencephalogram (EEG) patterns of different imagination tasks, for example, hand movements. Auto-regression (AR) model is one of the popular methods to describe motor imagery patterns, which is widely used by researchers to resolve subject’s motor intention. In this paper, we use joint regression (JR) model and propose an algorithm by combining the coefficients of JR model and spectral powers at two specific frequencies to classify different MI patterns. The algorithm produces a classification accuracy of 90 % on the training data of one subject from BCI2003 Data set III and 80 % on the test data. The results are better than that by using AR model. We also apply the algorithm to MI tasks of one subject in our laboratory, and the classification accuracy can reach 97.86 % on the test data. The results demonstrate that the combination of JR model and spectral powers can achieve much higher accuracy for classification of MI tasks. [ABSTRACT FROM AUTHOR]

• A tensor model of the dynamic brain functional network is proposed. • This method relies on the changing of the interaction of various brain regions. • Orthogonal decomposition of partially symmetric tensors can extract MI features. • The identification electrode is located near Cz during the MI period. The classification of motor imagery (MI) task based on Electroencephalography (EEG) is an important problem in brain-computer interface (BCI) system. The high-precision classification of MI is a challenging task in which the process of feature extraction is crucial step. In this work, we propose a tensor model of a dynamic brain functional network (DBFN) to decode motion intentions. First, we construct the brain functional network in each small window. Then, the BFN of each time window is superimposed into a DBFN tensor with time as the axis. A tensor decomposition method with orthogonal and partial symmetric constraints is used to analyze the DBFN. Finally, the core tensor features are used as an input of the extreme learning machine (ELM) for classification. The results show that the proposed method is better than the degree, clustering coefficient of network, and principal component analysis of DBFN matrix model and the average accuracies are improved by 17.33%, 12.91%, and 17.5% under ELM, respectively. Moreover, the classification accuracy of the proposed method has the lowest variance, i.e., 5.96, indicating that the core tensor features are more adaptable to the subjects. The proposed method has the highest accuracy of 95% under both ELM and support vector machine (SVM). The average accuracy rates of ELM and SVM are 87.08% and 85.83%, respectively. The proposed method effectively extracts the EEG signal characteristics of MI and has strong robustness. This provides a reference for further research on the feature extraction algorithm of BCI. [ABSTRACT FROM AUTHOR]