Conscious mental tasks and their EEG signals

Comparative study of SVM methods combined with voxel selection for object category classification on fMRI data

BACKGROUND

Support vector machine (SVM) has been widely used as accurate and reliable method to decipher brain patterns from functional MRI (fMRI) data. Previous studies have not found a clear benefit for non-linear (polynomial kernel) SVM versus linear one. Here, a more effective non-linear SVM using radial basis function (RBF) kernel is compared with linear SVM. Different from traditional studies which focused either merely on the evaluation of different types of SVM or the voxel selection methods, we aimed to investigate the overall performance of linear and RBF SVM for fMRI classification together with voxel selection schemes on classification accuracy and time-consuming.

METHODOLOGY/PRINCIPAL FINDINGS

Six different voxel selection methods were employed to decide which voxels of fMRI data would be included in SVM classifiers with linear and RBF kernels in classifying 4-category objects. Then the overall performances of voxel selection and classification methods were compared. Results showed that: (1) Voxel selection had an important impact on the classification accuracy of the classifiers: in a relative low dimensional feature space, RBF SVM outperformed linear SVM significantly; in a relative high dimensional space, linear SVM performed better than its counterpart; (2) Considering the classification accuracy and time-consuming holistically, linear SVM with relative more voxels as features and RBF SVM with small set of voxels (after PCA) could achieve the better accuracy and cost shorter time.

CONCLUSIONS/SIGNIFICANCE

The present work provides the first empirical result of linear and RBF SVM in classification of fMRI data, combined with voxel selection methods. Based on the findings, if only classification accuracy was concerned, RBF SVM with appropriate small voxels and linear SVM with relative more voxels were two suggested solutions; if users concerned more about the computational time, RBF SVM with relative small set of voxels when part of the principal components were kept as features was a better choice.

Artificial neural network detects changes in electro-encephalogram power spectrum of different sleep-wake states in an animal model of heat stress

An effective application is presented of a back-propagation artificial neural network (ANN) in differentiating electro-encephalogram (EEG) power spectra of stressed and normal rats in three sleep-wakefulness stages. The rats were divided into three groups, one subjected to acute heat stress, one subjected to chronic heat stress and one a handling control group. The polygraphic sleep recordings were performed by simultaneous recording of cortical EEG, electro-oculogram (EOG) and electromyogram (EMG) on paper and in digital form on a computer hard disk. The preprocessed EEG signals (after removal of DC components and reduction of baseline movement) were fragmented into 2 s artifact-free epochs for the calculation of power spectra. The slow-wave sleep (SWS), rapid eye movement (REM) sleep and awake (AWA) states were analysed separately. The power spectrum data for all three sleep-wake states in the three groups of rats were tested by a back-propagation ANN. The network contained 60 nodes in the input layer, weighted from power spectrum data from 0 to 30 Hz, 18 nodes in the hidden layer and an output node. The ANN was found effective in differentiating the EEG power spectra from stressed to normal spectral patterns following acute (92% in SWS, 85.5% in REM sleep, 91% in AWA state) as well as chronic heat exposure (95.5% in SWS, 93.8% in REM sleep, 98.5% in AWA state).