Measuring team performance and physiological compliance (PC) within a team have gained interest in the last few decades. The team’s performance or functioning of a team is overseen by attributes such as collaboration, coordination, attitudes and motivation. Team emergent states such as situational awareness, trust, emotions and mutual understanding influence the attributes of teams. This study examines the relationship between PC and collaboration. It also investigates how the cognitive state influences this relationship. Seventeen teams, each with three members, participated in a collaborative simulated task, while their electrocardiogram (ECG) activity was recorded via a chest strap device. Short-term time-domain measures of heart rate variability (HRV) were derived for each participant. PC was established using the mean of the cross-correlation (CC) between dyads within a team. A linear regression model was employed to examine the relationship between PC and self-reported measures of team collaboration. This in turn will be applied to investigate how cognitive state between team members influences that relationship. The results have shown a statistically significant (p<0.05) positive relationship between PC and collaboration. In conclusion, PC has the potential to be an objective method to quantity teamwork effectiveness where it can be assessed via self-reported collaboration.

In this research, we present a new emotion recognition model using wavelet packet energy features using electroencephalography (EEG) data. Wavelet packets has been widely used as a means of time-frequency analysis of EEG in many different applications including brain computer interface systems. Features for emotion recognition are extracted from the EEG signals using a depth 6 wavelet packet tree. Wavelet packet energy of the sub-bands corresponding to delta (0-4Hz), theta (4-8Hz), alpha (8-13Hz), beta (13-30Hz), and gamma (30-49Hz) are taken as emotional features. Feature selection based on feature ranking is applied to select the most prominent EEG channel-frequency combinations for emotion recognition. Four classical classifiers such as linear discriminant analysis, support vector machine, K nearest neighbor and naive Bayes were used to detect the emotional states from the extracted features. To evaluate the effectiveness and validation of the proposed model, the SEED database has been employed. Based on the experiment results obtained, our method demonstrates that the LDA is more suitable for emotion recognition as compared to other classical classifier, which achieving the best average accuracy of 90.9386. Emotion recognition systems with high accuracy give opportunities to study real world applications such as mental state and fatigue monitoring.

Employing electroencephalography (EEG) data for classifying emotion has attracted a significant interest among researchers. With the ever advancing machine learning and neural network computational power, several models have been proposed for classifying the emotion states. Training an efficient classifier requires selection of appropriate features demanding complex computations. The present article investigates whether dense convolutional neural networks can extract the features from just pre-processed raw EEG signals enabling a suitability for online classification. The article presents a fully connected multi-channel neural network model comprising of dense con-volutional layers coupled with sparse autoencoder and dense perceptron layers for classifying emotions from 62-channel EEG data. The proposed model uses the dense convolution layers to learn emotion features directly from each channel. The cascaded SAE layer coupled with dense perceptron network learns correlations between the channels for efficient classification. In addition, different methods exist to generate training and testing sets for validating classifier models. In this light, the article discusses three most recent methods to generate training and testing sets employed in emotion classification. The proposed model achieves a best testing accuracy of 97.42%.

Spike sorting of electrophysiological data plays an important role in deciphering useful information from the brain. Unsupervised clustering of brain data relative to respective neurons is important to understand single cell and networks dynamics. A large number of clustering techniques exist in the literature; however, the dependency of these clustering algorithms on the selection of appropriate parameters, such as, bandwidth or threshold window size is critical. Iterative methods are generally employed to estimate optimal parameters, however, significant computational time and associated large number of iterations make the clustering inefficient to implement. To address this issue, we introduce a robust Optimal Parameter Estimation (OPE) Algorithm that can estimate the optimized parameters in a fast and efficient way. The performance of the OPE algorithm is tested on MeanShift and DBSCAN clustering algorithms. Three different extracellular recorded datasets including two simulated and one single human cell, as well as two feature sets including PCA and Haar Wavelets are used for validation purposes.