Deep Learning Model With Adaptive Regularization for EEG-Based Emotion Recognition Using Temporal and Frequency Features

Since EEG signal acquisition is non-invasive and portable, it is convenient to be used for different applications. Recognizing emotions based on Brain-Computer Interface (BCI) is an important active BCI paradigm for recognizing the inner state of persons. There are extensive studies about emotion recognition, most of which heavily rely on staged complex handcrafted EEG feature extraction and classifier design. In this paper, we propose a hybrid multi-input deep model with convolution neural networks (CNNs) and bidirectional Long Short-term Memory (Bi-LSTM). CNNs extract time-invariant features from raw EEG data, and Bi-LSTM allows long-range lateral interactions between features. First, we propose a novel hybrid multi-input deep learning approach for emotion recognition from raw EEG signals. Second, in the first layers, we use two CNNs with small and large filter sizes to extract temporal and frequency features from each raw EEG epoch of 62-channel 2-s and merge with differential entropy of EEG band. Third, we apply the adaptive regularization method over each parallel CNN’s layer to consider the spatial information of EEG acquisition electrodes. The proposed method is evaluated on two public datasets, SEED and DEAP. Our results show that our technique can significantly improve the accuracy in comparison with the baseline where no adaptive regularization techniques are used

Performance evaluation of a digital electrical impedance tomography system

Performance evaluation of a portable digital multi-frequency electrical impedance tomography system is presented. The instrumentation hardware and image reconstruction are assessed according to a systematic methodology using a practical phantom. The phantom is equipped with eight electrodes in a ring configuration and a sinusoidal current of constant amplitude is injected using an adjacent current injection protocol. Artificial anomalies are introduced as inhomogeneity targets and the boundary potential data is collected. The images are reconstructed from the boundary data using Comsol Multiphysics and Matlab. Signal to noise ratio (SNR) and accuracy of the measurements are calculated. The limits of detectability and distinguishability of contrasts are measured from the collected potential data set for single and double inhomogeneities. The conductivity of the targets is successfully reconstructed from the potential data measurements. The detectability value is found to be high when a single target is close to the electrodes, while the values are less for the target in the centre. Also, the value of distinguishability increases when the targets move further away from each other

Control algorithm for emission tests

Simulating the test drive conditions have become an important area for research and development in automotive industry. The use of dedicated robots instead of human drivers in order to perform vehicle emission tests is so common; however these robots are very heavy, expensive and still perform bodily. The main objective of this study is to design a proper controller to control the vehicle tracking the driving cycles in terms of vehicle velocity for the mentioned tests. The simulation has been executed in LabVIEW which easily can be connected to hardware, CompactRIO, in order to drive the vehicle by wire. In this study based on the vehicle model, control and simulation of the vehicle under emission tests circumstances is discussed. A longitudinal model of the vehicle has been presented. The brake and throttle are the inputs and vehicle velocity and the main components of the exhaust gases are the outputs of the vehicle model. To control the vehicle, a fuzzy control which is nonlinear is designed and the performance of the controller is compared with a classical PID controller. Two individual fuzzy controllers have been applied to control the brake and throttle pedals. Therefore the outputs are the instantaneous throttle opening and the brake position in terms of brake torque. The inputs are the difference between the real speed and the desired speed and the rate of change of the error

Haematoma detection using EIT in a sheep model

Performance evaluation of a portable digital electrical impedance tomography system to detect haematomas using phantom and sheep models is presented. Experiments have been performed using 8-electrode full array configuration. Conductivity changes were detected in phantom model while anomalies were placed at center and close to the edge of the tank. Bleeding rate was successfully monitored in sheep model while blood-like conductivity solution was injecting via the brainstem. EIT images were reconstructed sequentially for different injection volumes and the quantity index (QI) was calculated as a function of the injected solution volume. The results show a linear relationship of QI to the injected volume. Images of the sheep experiment with the simulated haematomas, blood-like conductivity gel, placed on top of the parietal lobes of the brain on the left and right sides were reconstructed and haematomas are clearly detected and localized. These experiments prove that the detection and quantification of haematomas in the brain is possible and encourage further investigation for medical applications

Performance evaluation of a digital electrical impedance tomography system

Performance evaluation of a portable digital multi-frequency electrical impedance tomography system is presented. The instrumentation hardware and image reconstruction are assessed according to a systematic methodology using a practical phantom. The phantom is equipped with eight electrodes in a ring configuration and a sinusoidal current of constant amplitude is injected using an adjacent current injection protocol. Artificial anomalies are introduced as inhomogeneity targets and the boundary potential data is collected. The images are reconstructed from the boundary data using Comsol Multiphysics and Matlab. Signal to noise ratio (SNR) and accuracy of the measurements are calculated. The limits of detectability and distinguishability of contrasts are measured from the collected potential data set for single and double inhomogeneities. The conductivity of the targets is successfully reconstructed from the potential data measurements. The detectability value is found to be high when a single target is close to the electrodes, while the values are less for the target in the centre. Also, the value of distinguishability increases when the targets move further away from each other