EEG intentions recognition in dynamic complex object control task by functional brain networks and regularized discriminant analysis

From Expert to Elite? — Research on Top Archer’s EEG Network Topology

It is not only difficult to be a sports expert but also difficult to grow from a sports expert to a sports elite. Professional athletes are often concerned about the differences between an expert and an elite and how to eventually become an elite athlete. To explore the differences in brain neural mechanism between experts and elites in the process of motor behavior and reveal the internal connection between motor performance and brain activity, we collected and analyzed the electroencephalography (EEG) findings of 14 national archers and 14 provincial archers during aiming and resting states and constructed the EEG brain network of the two archer groups based on weighted phase lag index; the graph theory was used to analyze and compare the network characteristics via local network and global network topologies. The results showed that compared with the expert archers, the elite archers had stronger functional coupling in beta1 and beta2 bands, and the difference was evident in the frontal and central regions; in terms of global characteristics of brain network topology, the average clustering coefficient and global efficiency of elite archers were significantly higher than that of expert archers, and the eigenvector centrality of expert archers was higher; for local characteristics, elite archers had higher local efficient; and the brain network characteristics of expert archers showed a strong correlation with archery performance. This suggests that compared with expert archers, elite archers showed stronger functional coupling, higher integration efficiency of global and local information, and more independent performance in the archery process. These findings reveal the differences in brain electrical network topologies between elite and expert archers in the archery preparation stage, which is expected to provide theoretical reference for further training and promotion of professional athletes.

Discrimination Improvement Through Undesirable Feedback in Coupling Object Manipulation Tasks

Subjective effort can significantly affect the ability of humans to act optimally in dynamic manipulation tasks. In a previous study, we designed a complex object coupling manipulation task that required tight performance and induced high cognitive workload. We hypothesize that strong-effort-related physiological reactivity during the dynamic manipulation task improves the user performance in an undesired task feedback situation. To test this hypothesis, using the motor intentions' discrimination from electroencephalogram (EEG) measurements, we evaluate the effort expended by 20 participants in a controlling task with constraints involving complex coupling objects. Specifically, the finer motor decisions are obtained from the controlling information in EEG by using two fingers from the same hand rather than two hands. The motor intention is decoded from a task-dependent EEG through a regularized discriminant analysis, and the area under the curve is [Formula: see text]. Furthermore, we compare the undesired and desired task feedback conditions along with the individual's effort dynamic adjustment, and investigate whether the undesired task feedback improved the discrimination of the motor activities. A stronger effort to attain the desired feedback state corresponds to improved motor activity discrimination from the EEG in the undesired task feedback scenario. The differences in the brain activities under the undesired and desired task feedback conditions are analyzed using brain-network-based topographical scalp maps. Our experiment provides preliminary evidence that inducing strong effort can improve discrimination performance during highly demanding tasks. This finding can advance our understanding of human attention, potentially improve the accuracy of intention recognition, and may inspire better EEG acquisition contexts.

Improvement motor imagery EEG classification based on sparse common spatial pattern and regularized discriminant analysis

BACKGROUND

The classification of psychological tasks such as motor imagery based on electroencephalography (EEG) signals is an essential issue in the brain computer interface (BCI) system. The feature extraction is an important issue for improving classification accuracy of BCI system.

NEW METHOD

For extracting discriminative features, common spatial pattern (CSP) is an effective feature extraction method. However, features extracted by CSP are dense, and even feature patterns are repeatedly selected in the feature space. A sparse CSP algorithm is proposed, which embeds the sparse techniques and iterative search into the CSP. To improve the classification performance, two regularization parameters are added to the traditional linear discriminant analysis (LDA).

RESULTS

The sparse CSP algorithm can select several channels of EEG signals with the most obvious features. The improved regularized discriminant analysis is used to solve the singularity problem and improve the feature classification accuracy. Comparison with Existing Method(s): The proposed algorithm was evaluated by the data set I of the IVth BCI competition and our dataset. The experimental results of the BCI competition dataset show that accuracy of the improved algorithm is 10.75 % higher than that of the traditional algorithm. Comparing with the currently existing methods for the same data, it also shows excellent classification performance. The effectiveness of the improved algorithm is also shown in experiments on our dataset.

CONCLUSIONS

It sufficiently proves that the improved algorithm proposed in this paper improves the classification performance of motor intent recognition.