Recognizing emotions induced by wearable haptic vibration using noninvasive electroencephalogram.

The integration of haptic technology into affective computing has led to a new field known as affective haptics. Nonetheless, the mechanism underlying the interaction between haptics and emotions remains unclear. In this paper, we proposed a novel haptic pattern with adaptive vibration intensity and rhythm according to the volume, and applied it into the emotional experiment paradigm. To verify its superiority, the proposed haptic pattern was compared with an existing haptic pattern by combining them with conventional visual-auditory stimuli to induce emotions (joy, sadness, fear, and neutral), and the subjects' EEG signals were collected simultaneously. The features of power spectral density (PSD), differential entropy (DE), differential asymmetry (DASM), and differential caudality (DCAU) were extracted, and the support vector machine (SVM) was utilized to recognize four target emotions. The results demonstrated that haptic stimuli enhanced the activity of the lateral temporal and prefrontal areas of the emotion-related brain regions. Moreover, the classification accuracy of the existing constant haptic pattern and the proposed adaptive haptic pattern increased by 7.71 and 8.60%, respectively. These findings indicate that flexible and varied haptic patterns can enhance immersion and fully stimulate target emotions, which are of great importance for wearable haptic interfaces and emotion communication through haptics.

Classification of motor imagery using multisource joint transfer learning.

As an important way for human-computer interaction, the motor imagery brain-computer interface (MI-BCI) can decode personal motor intention directly by analyzing electroencephalogram (EEG) signals. However, a large amount of labeled data has to be collected for each new subject since EEG patterns vary between individuals. The long calibration phase severely limits the further development of MI-BCI. To tackle this problem, multi-source joint domain adaption (MJDA) and multi-source joint Riemannian adaption (MJRA) algorithms are proposed in this paper. Both methods aim to transfer knowledge from other subjects to the current subject who has only a small amount of labeled data. First, the common spatial pattern with Euclidean alignment is used to select source subjects who have similar spatial patterns to the target subject. Second, the covariance matrices of EEG trials are aligned in Riemannian space by removing subject-specific baselines. These two steps are shared by MJDA and MJRA. In the last step, MJDA attempts to minimize the feature distribution mismatch in the Riemannian tangent space, while MJRA attempts to find an adaptive Riemannian classifier. Finally, the proposed methods are validated on two datasets: BCI Competition IV 2a and online event-related desynchronization (ERD)-BCI. The experimental results demonstrate that both MJDA and MJRA outperform the state-of-the-art approaches. The MJDA provides a new idea for the offline analysis of MI-BCI, while MJRA could make a big difference to the online calibration of MI-BCI.

Partial directed coherence based graph convolutional neural networks for driving fatigue detection.

The mental state of a driver can be accurately and reliably evaluated by detecting the driver's electroencephalogram (EEG) signals. However, traditional machine learning and deep learning methods focus on the single electrode feature analysis and ignore the functional connection of the brain. In addition, the recent brain function connection network method needs to manually extract substantial brain network features, which results in cumbersome operation. For this reason, this paper introduces graph convolution combined with brain function connection theory into the study of mental fatigue and proposes a method for driving fatigue detection based on the partial directed coherence graph convolutional neural network (PDC-GCNN), which can analyze the characteristics of single electrodes while automatically extracting the topological features of the brain network. We designed a fatigue driving simulation experiment and collected the EEG signals. In the present work, the PDC method constructs the adjacency matrix to describe the relationship between EEG channels, and the GCNN combines single-electrode local brain area information and brain area connection information to further improve the performance of detecting fatigue states. Based on the features of differential entropy (DE) and power spectral density (PSD), the average recognition accuracy of ten-fold cross validation is 84.32% and 83.84%, respectively. For further experiments on each subject, the average recognition results are 95.24%/5.10% (PSD) and 96.01%/3.81% (DE). This research can be embedded in the vehicle driving fatigue detection system, which has practical application value.

Motor imagery classification using geodesic filtering common spatial pattern and filter-bank feature weighted support vector machine.

In recent years, Brain Computer Interface (BCI) based on motor imagery has been widely used in the fields of medicine, active safe systems for automobiles, entertainment, and so on. Motor imagery relevant electroencephalogram (EEG) signals are weak, nonlinear, and susceptible to interference. As a feature extraction method for motor imagery, Common Spatial Pattern (CSP) has been proven to be very effective. However, its effectiveness depends heavily on the choice of frequency bands, and Euclidean space cannot effectively describe the inner relationship. To solve these problems, a classification approach for motor imagery using the Geodesic Filtering Common Spatial Pattern (GFCSP) and filter-bank Feature Weighted Support Vector Machine (FWSVM) is presented. First, GFCSP based on the Riemannian manifold is proposed, in which the extracted covariance features are spatially filtered in Riemannian tangent space, and the average covariance matrix is replaced by Riemannian mean in CSP. Second, filter-bank FWSVM with a feature weighted matrix is proposed. EEG signals are filtered into 8-12 Hz, 12-16 Hz, 18-22 Hz, 22-26 Hz, and a wide band of 8-24 Hz, and GFCSP features of these filtered signals are extracted. A feature weighted matrix is calculated using mutual information and the Pearson correlation coefficient from these features and class information. Then, the Support Vector Machine (SVM) is used for classification with the feature weighted matrix. Finally, the proposed method is validated on the dataset IVa in BCI competition III. Classification accuracies of the five subjects are 92.31%, 99.03%, 80.36%, 96.30%, and 97.67%, which demonstrate the effectiveness of our proposed method.