Dynamical nonstationarity of resting EEGs in patients with attention-deficit/hyperactivity disorder (AD/HD)

Attentional State Classification Using Amplitude and Phase Feature Extraction Method Based on Filter Bank and Riemannian Manifold

As a significant aspect of cognition, attention has been extensively studied and numerous measurements have been developed based on brain signal processing. Although existing attentional state classification methods have achieved good accuracy by extracting a variety of handcrafted features, spatial features have not been fully explored. This paper proposes an attentional state classification method based on Riemannian manifold to utilize spatial information. Based on the concept of Riemannian manifold of symmetric positive definite (SPD) matrix, the proposed method exploits the structure of covariance matrix to extract spatial features instead of using spatial filters. Specifically, Riemannian distances from intra-class Riemannian means are extracted as features for their robustness. To fully extend the potential of electroencephalograph (EEG) signal, both amplitude and phase information is utilized. In addition, to solve the variance of frequency bands, a filter bank is employed to process the signal of different frequency bands separately. Finally, features are fed into a support vector machine with a polynomial kernel to obtain classification results. The proposed attentional state classification using amplitude and phase feature extraction method based on filter bank and Riemannian manifold (AP-FBRM) method is evaluated on two open datasets including EEG data of 29 and 26 subjects. According to the experimental results, the optimal set of filter bank and the optimal technique to extract features containing both amplitude and phase information are determined. The proposed method respectively achieves accuracies of 88.06% and 80.00% and outperforms 8 baseline methods, which manifests that the proposed method creates an efficient way to recognize attentional state.

Abnormal dynamics of EEG oscillations in schizophrenia patients on multiple time scales

Neuronal oscillations reflect the activity of neuronal ensembles engaged in integrative cognition, and may serve as a functional measure for the cognitive impairment in schizophrenia. This study aims to reveal the abnormal amplitude dynamics of electroencephalogram (EEG) oscillations in schizophrenia patients on multiple time scales. EEGs were recorded from schizophrenia patients ( n = 19) and healthy controls ( n = 16) while they were at resting state with eyes closed, at resting state with eyes open, and at watching video. Detrended fluctuation analysis and measures of life-time and waiting-time were used to characterize the abnormal dynamics of EEG oscillations on both long (1-20 s) and short (≤1 s) time scales. Abnormal dynamics of EEG oscillations in alpha and beta bands were observed. In particular, compared with healthy controls, schizophrenia patients have smaller DFA exponent (implying weaker long-range temporal correlation) in the left fronto-temporal area and smaller DFA exponent, smaller life-time (indicating shorter oscillation burst), and smaller waiting-time in the occipital area in beta band at resting state with eyes open. In addition, schizophrenia patients have larger DFA exponent, larger life-time, and larger waiting-time at some clustered channels in the temporo-parietal area in alpha band at watching video. The present results provide new insights for cognitive deficits and the underlying neuronal dysfunction in schizophrenia.

Decoding covert motivations of free riding and cooperation from multi-feature pattern analysis of EEG signals

Cooperation and free riding are among the most frequently observed behaviors in human social decision-making. In social interactions, the effects of strategic decision processes have been consistently reported in iterative cooperation decisions. However, the neural activity immediately after new information is presented, the time at which strategy learning potentially starts has not yet been investigated with high temporal resolution. Here, we implemented an iterative, binary public goods game that simulates cooperation/free riding behavior. We applied the multi-feature pattern analysis method by using a support vector machine and the unique combinatorial performance measure, and identified neural features from the single-trial, event-related spectral perturbation at the result-presentation of the current round that predict participants' decisions to cooperate or free ride in the subsequent round. We found that neural oscillations in centroparietal and temporal regions showed the highest predictive power through 10-fold cross-validation; these predicted the participants' next decisions, which were independent of the neural responses during their own preceding choices. We suggest that the spatial distribution and time-frequency information of the selected features represent covert motivations to free ride or cooperate in the next round and are separately processed in parallel with information regarding the preceding results.