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This article discusses the use of a brain–computer interface (BCI) to obtain emotional feedback from a human in response to the motion of humanoid robots in collaborative environments. The purpose of this study is to detect the human satisfaction level and use it as a feedback for correcting and improving the behavior of the robot to maximize human satisfaction. This article describes experiments and algorithms that use human brains activity collected through BCI in order to estimate the level of satisfaction. Users wear an electroencephalogram (EEG) headset and control the movement of the robot by mental imagination. The robots responds to the mental imagination may not be the same as human mental command and this will affect the emotional satisfaction level. The headset records brain activity from 14 locations on the scalp. Power spectral density of each EEG frequency band and four largest Lyapunov exponents of each EEG signal form the feature vector. The Mann–Whitney–Wilcoxon test is then used to rank all the features. The highest rank features are then selected to train a linear discriminant classifier (LDC) to determine the satisfaction level. Our experimental results show an accuracy of 79.2% in detecting the human satisfaction level.

The corticomuscular coupling (CMC) characterization between the motor cortex and muscles during motion control is a valid biomarker of motor system function after stroke, which can improve clinical decision-making. However, traditional CMC analysis is mainly based on the coherence method that can’t determine the coupling direction, whereas Granger Causality (GC) is limited in identifying linear cause–effect relationship. In this paper, a time-frequency domain copula-based GC (copula-GC) method is proposed to assess CMC characteristic. The 32-channel electroencephalogram (EEG) signals over brain scalp and electromyography (EMG) signals from upper limb were recorded during controlling and maintaining steady-state force output for five stroke patients and five healthy controls. Then, the time-frequency copula-GC analysis was applied to evaluate the CMC strength in both directions. Experimental results show that the CMC strength of descending direction is greater than that of ascending direction in the time domain for healthy controls. With the increase of grip strength, the bi-directional CMC strength has an increasing trend. Meanwhile, the bi-directional CMC strength of right hand is larger than that of left hand. In addition, the bi-directional CMC strength of stroke patients is lower than that of healthy controls. In the frequency domain, the strongest CMC is observed at the beta frequency band. Additionally, the CMC strength of descending direction is slightly larger than that of ascending direction in healthy controls, while the CMC strength of descending direction is lower than that of ascending direction in stroke patients. We suggest that the proposed time-frequency domain analysis approach based on copula-GC can effectively detect complex functional coupling between cortical oscillations and muscle activities, and provide a potential quantitative analysis measure for motion control and rehabilitation evaluation.

Automatic detection of the current task load of a surgeon in the theatre in real time could provide helpful information, to be used in supportive systems. For example, such information may enable the system to automatically support the surgeon when critical or stressful periods are detected, or to communicate to others when a surgeon is engaged in a complex maneuver and should not be disturbed. Passive brain–computer interfaces (BCI) infer changes in cognitive and affective state by monitoring and interpreting ongoing brain activity recorded via an electroencephalogram. The resulting information can then be used to automatically adapt a technological system to the human user. So far, passive BCI have mostly been investigated in laboratory settings, even though they are intended to be applied in real-world settings. In this study, a passive BCI was used to assess changes in task load of skilled surgeons performing both simple and complex surgical training tasks. Results indicate that the introduced methodology can reliably and continuously detect changes in task load in this realistic environment.

This study investigates the effect of haptic control strategies on a subject’s mental engagement during a fine motor handwriting rehabilitation task. The considered control strategies include an error-reduction (ER) and an error-augmentation (EA), which are tested on both dominant and nondominant hand. A noninvasive brain–computer interface is used to monitor the electroencephalogram (EEG) activities of the subjects and evaluate the subject’s mental engagement using the power of multiple frequency bands (theta, alpha, and beta). Statistical analysis of the effect of the control strategy on mental engagement revealed that the choice of the haptic control strategy has a significant effect ($p<0.001$) on mental engagement depending on the type of hand (dominant or nondominant). Among the evaluated strategies, EA is shown to be more mentally engaging when compared with the ER under the nondominant hand.