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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]

Electroencephalography (EEG) signal classification is a crucial part in motor imagery brain–computer interface (BCI) system. Traditional supervised learning methods have performed well pleasing in EEG classification. Unfortunately, the unlabeled samples are easier to collect than labeled samples. In addition, recent studies have shown that it may degenerate performance of semi-supervised learning by exploiting unlabeled samples without selection. To address these issues, a novel semi-supervised broad learning system with transfer learning (TSS-BLS) is proposed in this paper. First, the pseudo-labels of unlabeled samples are obtained using the joint distribution adaptation algorithm. TSS-BLS is then constructed by an improved manifold regularization framework containing both labeled and pseudo-label information. Finally, the effectiveness of the proposed TSS-BLS is evaluated on three BCI competition datasets and four benchmark datasets from UCI repository and compared with seven state-of-the-art algorithms, including ELM, SS-ELM, HELM, SVM, LapSVM, BLS and GSS-BLS. Experimental results show that the performance of TSS-BLS is superior to BLS and GSS-BLS on average. It is thereby shown that TSS-BLS is safe and efficient for EEG classification. [ABSTRACT FROM AUTHOR]

Emotions play a crucial role in human communication and affect all aspects of human life. However, to date, there have been few studies conducted on how movements under different emotions influence human brain activity and cortico-muscular coupling (CMC). In this study, for the first time, electroencephalogram (EEG) and electromyogram physiological electrical signals were used to explore this relationship. We performed frequency domain and nonlinear dynamics analyses on EEG signals and used transfer entropy to explore the CMC associated with the emotion-movement relationship. To study the transmission of information between different brain regions, we also constructed a functional brain network and calculated various network metrics using graph theory. We found that, compared with a neutral emotional state, movements made during happy and sad emotions had increased CMC strength and EEG power and complexity. The functional brain network metrics of these three emotional states were also different. Much of the emotion-movement relationship research has been based on subjective expression and external performance. Our research method, however, focused on the processing of physiological electrical signals, which contain a wealth of information and can objectively reveal the inner mechanisms of the emotion-movement relationship. Different emotional states can have a significant influence on human movement. This study presents a detailed introduction to brain activity and CMC. • Combine EEG and EMG to study the relationship between emotion and movement. • Movement associated with happy emotions can increase the complexity of the left hemisphere. • Movement associated with sad emotions can increase the complexity of the right hemisphere. • Increased grip strength leads to increased the CMC strength of EEG→EMG. • The network topology of movement under different emotions is significantly different. [ABSTRACT FROM AUTHOR]

• A new experimental paradigm for exploring the different effects of anode tDCS on M1 and SMA. • The tDCS's effect on the brain-SMA is more obvious in the motor preparation stage. • The effect of tDCS is more obvious in the execution of the entire motor imagination task, but not in the motor preparation stage. • The effect of tDCS on the motor area of the brain is significant, especially in the M1. Related experiments have shown that transcranial direct current stimulation (tDCS) anodal stimulation of the brain's primary motor cortex (M1) and supplementary motor area (SMA) can improve the motor control and clinical manifestations of stroke patients with aphasia and dyskinesia. In this study, to explore the different effects of tDCS on the M1 and SMA in motor imagery, 35 healthy volunteers participated in a double-blind randomized controlled experiment. Five subjects underwent sham stimulation (control), 15 subjects underwent tDCS anode stimulation of the M1, and the remaining 15 subjects underwent tDCS anode stimulation of the SMA. The electroencephalogram data of the subjects' left- and right-hand motor imagery under different stimulation paradigms were recorded. We used a functional brain network and sample entropy to examine the different complexities and functional connectivities in subjects undergoing sham-tDCS and the two stimulation paradigms. The results show that tDCS anodal stimulation of the SMA produces less obvious differences in the motor preparation phase, while tDCS anodal stimulation of the M1 produces significant differences during the motor imaging task execution phase. The effect of tDCS on the motor area of the brain is significant, especially in the M1. [ABSTRACT FROM AUTHOR]