eyes closed
Research Papers
The Time-Robustness Analysis of Individual Identification Based on Resting-State EEG
An ongoing interest towards identification based on biosignals, such as electroencephalogram (EEG), magnetic resonance imaging (MRI), is growing in the past decades. Previous studies indicated that the inherent information about brain activity may be used to identify individual during resting-state of eyes open (REO) and eyes closed (REC). Electroencephalographic (EEG) records the data from the scalp, and it is believed that the noisy EEG signals can influence the accuracies of one experiment causing unreliable results. Therefore, the stability and time-robustness of inter-individual features can be investigated for the purpose of individual identification. In this work, we conducted three experiments with the time interval of at least 2 weeks, and used different types of measures (Power Spectral Density, Cross Spectrum, Channel Coherence and Phase Lags) to extract the individual features. The Pearson Correlation Coefficient (PCC) is calculated to measure the level of linear correlation for intra-individual, and Support Vector Machine (SVM) is used to obtain the related classification accuracy. Results show that the classification accuracies of four features were 85-100% for intra-experiment dataset, and were 80-100% for fusion experiments dataset. For inter-experiments classification of REO features, the optimized frequency range is 13-40 Hz for three features, Power Spectral Density, Channel Coherence and Cross Spectrum. For inter-experiments classification of REC, the optimized frequency range is 8-40 Hz for three features, Power Spectral Density, Channel Coherence and Cross Spectrum. The classification results of Phase Lags are much lower than the other three features. These results show the time-robustness of EEG, which can further use for individual identification system.
View Full Paper →Eyes-Closed and Activation QEEG Databases in Predicting Cognitive Effectiveness and the Inefficiency Hypothesis
Background. Quantitative electroencephalography (QEEG) databases have been developed for the eyes closed (EC) condition. The development of a cognitive activation database is a logical and necessary development for the field. Method. Brain activation was examined by QEEG during several tasks including EC rest, visual attention (VA), auditory attention (AA), listening to paragraphs presented auditorily and reading silently. The QEEG measures obtained in the EC and simple, non-cognitive attention task that were significantly related to subsequent cognitive performance were not the same variables which accounted for success during the cognitive task. Results. There were clear differences between relative power, microvolt, coherence and phase values across these different tasks. Conclusions. The conclusions reached are (1) the associations among QEEG variables are complex and vary by task; (2) the QEEG variables which predict cognitive performance under task demands are not the same as the variables which predict to subsequent performance from the EC or simple, non-cognitive attention tasks; (3) a cognitive activation database is clinically useful; and (4) an hypothesis of brain functioning is proposed to explain the findings. The coordinated allocation of resources (CAR) hypothesis states that cognitive effectiveness is a product of multiple specific activities in the brain, which vary according to the task; and (5) the average response pattern does not involve the variables that are critical to success at the task, thus indicating an inefficiency of the normal human brain.
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