Graph-based Recurrence Quantification Analysis of EEG Spectral Dynamics for Motor Imagery-based BCIs.

Despite continuous research, communication approaches based on brain-computer interfaces (BCIs) are not yet an efficient and reliable means that severely disabled patients can rely on. To date, most motor imagery (MI)-based BCI systems use conventional spectral analysis methods to extract discriminative features and classify the associated electroencephalogram (EEG)-based sensorimotor rhythms (SMR) dynamics that results in relatively low performance. In this study, we investigated the feasibility of using recurrence quantification analysis (RQA) and complex network theory graph-based feature extraction methods as a novel way to improve MI-BCIs performance. Rooted in chaos theory, these features explore the nonlinear dynamics underlying the MI neural responses as a new informative dimension in classifying MI. METHOD: EEG time series recorded from six healthy participants performing MI-Rest tasks were projected into multidimensional phase space trajectories in order to construct the corresponding recurrence plots (RPs). Eight nonlinear graph-based RQA features were extracted from the RPs then compared to the classical spectral features through a 5-fold nested cross-validation procedure for parameter optimization using a linear support vector machine (SVM) classifier. RESULTS: Nonlinear graph-based RQA features were able to improve the average performance of MI-BCI by 5.8% as compared to the classical features. SIGNIFICANCE: These findings suggest that RQA and complex network analysis could represent new informative dimensions for nonlinear characteristics of EEG signals in order to enhance the MI-BCI performance.

Impaired auditory evoked potentials and oscillations in frontal and auditory cortex of a schizophrenia mouse model.

OBJECTIVES: In patients with schizophrenia, γ-band (30-70 Hz) auditory steady-state electroencephalogram responses (ASSR) are reduced in power and phase locking. Here, we examined whether γ-ASSR deficits are also present in a mouse model of schizophrenia, whose behavioural changes have shown schizophrenia-like endophenotypes. METHODS: Electroencephalogram in frontal cortex and local field potential in primary auditory cortex were recorded in phospholipase C ß1 (PLC-ß1) null mice during auditory binaural click trains at different rates (20-50 Hz), and compared with wild-type littermates. RESULTS: In mutant mice, the ASSR power was reduced at all tested rates. The phase locking in frontal cortex was reduced in the ß band (20 Hz) but not in the γ band, whereas the phase locking in auditory cortex was reduced in the γ band. The cortico-cortical connectivity between frontal and auditory cortex was significantly reduced in mutant mice. CONCLUSIONS: The tested mouse model of schizophrenia showed impaired electrophysiological responses to auditory steady state stimulation, suggesting that it could be useful for preclinical studies of schizophrenia".