Cross-task emotion recognition using EEG measures: first step towards practical application

Electroencephalographic(EEG)-based emotion recognition has received increasing attention in the field of human-computer interaction(HCI)recently,there however remains a number of challenges in building a generalized emotion recognition model,one of which includes the difficulty of an EEG-based emotion classifier trained on a specific task to handle other tasks.Lit-tle attention has been paid to this issue.The current study is to determine the feasibility of coping with this challenge using feature selection.12 healthy volunteers were emotionally elicited when conducting picture induced and videoinduced tasks.Firstly,support vector machine(SVM)classifier was examined under within-task conditions(trained and tested on the same task)and cross-task conditions(trained on one task and tested on another task)for pictureinduced and videoinduced tasks.The within-task classification performed fairly well(classification accuracy:51.6%for picture task and 94.4%for video task).Cross-task classification,however,deteriorated to low levels(around 44%).Trained and tested with the most robust feature subset selected by SVM-recursive feature elimination(RFE),the performance of cross-task classifier was significantly improved to above 68%.These results suggest that cross-task emotion recognition is feasible with proper methods and bring EEG-based emotion recognition models closer to being able to discriminate emotion states for any tasks.

A Novel Method for Flatness Pattern Recognition via Least Squares Support Vector Regression

To adapt to the new requirement of the developing flatness control theory and technology, cubic patterns were introduced on the basis of the traditional linear, quadratic and quartic flatness basic patterns. Linear, quadratic, cubic and quartic Legendre orthogonal polynomials were adopted to express the flatness basic patterns. In order to over- come the defects live in the existent recognition methods based on fuzzy, neural network and support vector regres- sion （SVR） theory, a novel flatness pattern recognition method based on least squares support vector regression （LS-SVR） was proposed. On this basis, for the purpose of determining the hyper-parameters of LS-SVR effectively and enhan- cing the recognition accuracy and generalization performance of the model, particle swarm optimization algorithm with leave-one-out （LOO） error as fitness function was adopted. To overcome the disadvantage of high computational complexity of naive cross-validation algorithm, a novel fast cross-validation algorithm was introduced to calculate the LOO error of LDSVR. Results of experiments on flatness data calculated by theory and a 900HC cold-rolling mill practically measured flatness signals demonstrate that the proposed approach can distinguish the types and define the magnitudes of the flatness defects effectively with high accuracy, high speed and strong generalization ability.