Fronto-Temporal Analysis of EEG Signals of Patients with Depression: Characterisation, Nonlinear Dynamics and Surrogate Analysis

The recent advances in signal processing techniques have enabled the analysis of biosignals from brain so as to enhance the predictive capability of mental states. Biosignal analysis has been successfully used to characterise EEG signals of unipolar depression patients. Methods of characterisation of EEG signals and the use of nonlinear parameters are the major highlights of this chapter. Bipolar frontopolar-temporal EEG recordings obtained under eyes open and eyes closed conditions are used for the analysis. A discussion on the reliability of the use of energy distribution and Relative Wavelet Energy calculations for distinguishing unipolar depression patients from healthy controls is presented. The potential of the application of Wavelet Entropy to differentiate states of the brain under normal and pathologic condition is introduced. Details are given on the suitability of ascertaining certain nonlinear indices on the feature extraction, assuming the time series to be highly nonlinear. The assumption of nonlinearity of the measured EEG time series is further verified using surrogate analysis. The studies discussed in this chapter indicate lower values of nonlinear measures for patients. The higher values of signal energy associated with the delta bands of depression patients in the lower frequency range are regarded as a major characteristic indicative of a state of depression. The chapter concludes by presenting the important results in this direction that may lead to better insight on the brain activity and cognitive processes. These measures are hence posited to be potential biomarkers for the detection of depression

Motor Imagery Experiment Using BCI: An Educational Technology Approach

Three individuals participated in the experiment in a medical simulation lab at Bogotá’s Antonio Nariño University. The objective was to compare the power spectral densities of signals obtained with a brain-computer interface (BCI) using a Nautilus g.tec 32, for activities that constitute motor imagination of closing the right and left hand, implementing a protocol designed by the author. The methodology used is closely connected to BCI-based HCIs with educational application. The results obtained indicate a clear intergroup difference in the levels of power spectrum, and a similarity in the intragroup levels. Measuring the signals of cognitive processes in the frontal and parietal cortex is recommended for educational applications. Among the conclusions, we highlight the importance of signal treatment, the differences encountered in spectrum comparison, and the applicability of the technology in education