Relevant EEG channels were evaluated to localize the part of the brain significantly responsible for RT estimation, followed by the isolation of important features.Clinical relevance- Electroencephalogram (EEG) signals can be used in Brain-computer interfaces (BCIs), enabling people with neuromuscular disorders like brainstem stroke, amyotrophic lateral sclerosis, and spinal cord injury to communicate with assistive devices. However, advancements regarding EEG signal analysis and interpretation are far from adequate, and this study is a step forward.Brain-machine interfaces (BMIs) allow individuals to communicate with computers using neural signals, and Kalman Filter (KF) are prevailingly used to decode movement directions from these neural signals. In this paper, we implemented a multi-layer long short-term memory (LSTM)based artificial neural network (ANN) for decoding BMI neural signals. We collected motor cortical neural signals from a nonhuman primate (NHP), implanted with microelectrode array (MEA) while performing a directional joystick task. Next, we compared the LSTM model in decoding the joystick trajectories from the neural signals against the prevailing KF model. The results showed that the LSTM model yielded significantly improved decoding accuracy measured by mean correlation coefficient (0.84, p less then 10-7) than the KF model (0.72). In addition, using a principal component analysis (PCA)-based dimensionality reduction technique yielded slightly deteriorated accuracies for both the LSTM (0.80) and KF (0.70) models, but greatly reduced the computational complexity. The results showed that the LSTM decoding model holds promise to improve decoding in BMIs for paralyzed humans.Exploring the brain response to stimuli of healthy people in passive state is helpful to understand the brain response mechanism of unresponsive people. Event-related potential (ERP) can reflect the time synchronization of potentials, which is a feasible objective electrophysiological index reflecting the functional status of the brain. In this paper, we used the subjects' own name (SON) as target stimuli and compared with the nontarget stimuli (others' name) of Three Chinese Characters (3CC) and Two Chinese Characters (2CC) with the same stimuli duration (600ms) and inter stimuli interval (500ms-800ms）. Thirteen healthy subjects attended in this study with four conditions ( [active, passive]×[3CC, 2CC] ). We compared the ERP waveforms, the behavior performance, and the classification of four different conditions. ERP results show that the P300 amplitude of conditions with 3CC nontargets is higher than that of conditions with 2CC nontargets. Behavioral results show that the grand accuracy is 97% when the nontargets are 3CC, while the grand accuracy is only 94% when the nontargets are 2CC. The reaction time is also different from the two nontargets (605ms with 3CC vs 635ms with 2CC). Classification results illustrate that in active condition, the accuracy rate is 82.1% when the nontarget is 3CC, and that is 80.9% in passive condition, which are 4.2% and 6.4% higher than the accuracy rate under 2CC cases in both active and passive conditions. This study can provide a scheme for grading diagnosis of consciousness detection, and further applying to clinical evaluation.Clinical Relevance- This study can provide a better paradigm basis for clinical evaluation of unresponsive patients (such as disorder of consciousness, DOC) and may become an effective auxiliary means for clinical rating scales.Mental stress is a prevalent issue in the modern society and a prominent contributing factor to various physical and psychological diseases. This paper investigates the feasibility of detecting different stress levels using electroencephalography (EEG), and evaluates the effectiveness of various stress-relief methods. https://www.selleckchem.com/products/d-1553.html EEG data were collected from 25 subjects while they were at rest and under 3 different levels of stress induced by mental arithmetic tasks. Nine features that correlate with stress from existing literature were extracted. Subsequently, discriminative features were selected by Fisher Ratio and used to train a Linear Discriminant Analysis classifier. Results from 10-fold cross-validation yielded averaged intra-subject classification accuracy of 85.6% for stress versus rest, 7l.2% for two levels of stress and rest, and 58.4% for three levels of stress and rest. The results showed high promise of using EEG to detect level of stress, and the features selected showed that Beta brain waves (13-30HZ) and prefrontal relative Gamma power are most discriminative. Five different stress-relief methods were then evaluated, and the method of hugging a pillow was found to be the most effective measure relatively in decreasing the stress level detected using EEG. These results show promise of future research in real-time stress detection and reduction using EEG for stress management and relief.Brain-computer interfaces (BCIs) provide more independence to people with severe motor disabilities but current BCIs' performance is still not optimal and often the user's intentions are misinterpreted. Error-related potentials (ErrPs) are the neurophysiological signature of error processing and their detection can help improving a BCI's performance.A major inconvenience of BCIs is that they commonly require a long calibration period, before the user can receive feedback of their own brain signals. Here, we use the data of 15 participants and compare the performance of a personalized ErrP classifier with a generic ErrP classifier. We concluded that there was no significant difference in classification performance between the generic and the personalized classifiers (Wilcoxon signed rank tests, two-sided and one-sided left and right). This results indicate that the use of a generic ErrP classifier is a good strategy to remove the calibration period of a ErrP classifier, allowing participants to receive immediate feedback of the ErrP detections.Electroencephalogram (EEG) data during motor imagery tasks regarding small-scale physical dynamics such as finger motions have low discriminability because capturing the spatial difference of the motions is difficult. We assumed that more discriminative features can be captured if spatial filters maximize the independence of each class data. This study constructed spatial filters named multiclass common spatial pattern (CSP), which maximize an approximation of mutual in-formation of extracted components and class labels, and applied them to a five-class motor-imagery dataset containing finger motion tasks. By applying multiclass CSP, the classification accuracies were improved (Mean SD 40.6 ± 10.1%) compared with classical CSP (21.8 ± 2.5%) and no spatial filtering case (38.7±10.0%). In addition, we visualized learned spatial filters to assess the trend of discriminative features of finger motions. For these results, it was clear that multiclass CSP captured task-specific spatial maps for each finger motion and outperformed multiclass motor-imagery classification performance about 2% even when the tasks are small-scale physical dynamics.