An Approach to Quantifying the Multi-Channel EEG Spatial-Temporal Feature

Spatially Nonlinear Interdependence of Alpha-Oscillatory Neural Networks under Chan Meditation

This paper reports the results of our investigation of the effects of Chan meditation on brain electrophysiological behaviors from the viewpoint of spatially nonlinear interdependence among regional neural networks. Particular emphasis is laid on the alpha-dominated EEG (electroencephalograph). Continuous-time wavelet transform was adopted to detect the epochs containing substantial alpha activities. Nonlinear interdependence quantified by similarity index S(X∣Y), the influence of source signal Y on sink signal X, was applied to the nonlinear dynamical model in phase space reconstructed from multichannel EEG. Experimental group involved ten experienced Chan-Meditation practitioners, while control group included ten healthy subjects within the same age range, yet, without any meditation experience. Nonlinear interdependence among various cortical regions was explored for five local neural-network regions, frontal, posterior, right-temporal, left-temporal, and central regions. In the experimental group, the inter-regional interaction was evaluated for the brain dynamics under three different stages, at rest (stage R, pre-meditation background recording), in Chan meditation (stage M), and the unique Chakra-focusing practice (stage C). Experimental group exhibits stronger interactions among various local neural networks at stages M and C compared with those at stage R. The intergroup comparison demonstrates that Chan-meditation brain possesses better cortical inter-regional interactions than the resting brain of control group.

EEG Alpha Blocking Correlated with Perception of Inner Light During Zen Meditation

According to the experimental results and practitioners' subjective experience, we report some hypotheses that may account for meditative phenomena during the practice of Zen-Buddhism. Orthodox Zen-Buddhist practitioners, aiming to prove the most original true-self, discover and uncover the inner energy or light on the way towards their goal. Perception of the inner light can be comprehended as resonance. Uncovering the inner energy optimizes physiological and mental health. In the meditation experiment, a significant correlation was observed between perception of the inner light and electroencephalographic (EEG) alpha blockage. We further examined this phenomenon by recording the EEG from subjects during a blessing that the subjects did not know being given. During the blessing period, significant alpha blocking was observed in experimental subjects who had been practicing meditation for years in preparation for being in resonance with the inner light. This report provides a new insight into the debate that meditation benefits our health.

Estimation of brain state changes associated with behavior, stimulation and epilepsy

Brain state dynamics vary at different spatiotemporal scales with behavior, stimulation, and disease, and may be unobserved (latent). Using a state-space model framework and subspace identification, we estimated spatiotemporally localized, latent state changes associated with the application of transcranial magnetic stimulation (TMS), to assess the effect of stimulation on brain state dynamics. State appeared to be modulated by behavior in a spatially-specific manner and small-amplitude state fluctuations were temporally locked to stimulus presentations. In addition, during and following TMS, an overall, bilateral and spatially nonspecific decrease in brain state was observed. We also estimated brain state changes during seizure evolution (independent of TMS), in focal and generalized seizures, which have very different epileptogenesis and propagation mechanisms, possibly resulting also in distinct spatiotemporal dynamics. Indeed, our preliminary results showed that in focal seizures, temporally localized dynamic state changes occur at least 1 min prior to seizure onset, with a decrease in steady-state followed by an increase which reaches a maximum during the ictal interval. In contrast, no such dynamic pattern was evident in state estimates during generalized seizures.

Novel method for measuring the complexity of schizophrenic EEG based on symbolic entropy analysis

Symbolic dynamics is a useful tool in several fields of complexity analysis in nonlinear science. In order to investigate complexities of the human brain electrical activities under different brain functional states, a novel method in terms of symbolic entropy is defined and proposed in this paper. The novel algorithm based on symbolic dynamics is developed for quantitatively measuring the complexity of the EEG data. Simulated signals derived from chaotic systems and several real EEG data under normal and pathological brain functional states are examined and compared. The experimental results show that the proposed method can distinguish not only the complexities of simulated signals but also the complexities of two groups of EEG data under different brain functional states. It is superior to the traditional entropy methods. Moreover, the algorithm can be easily completed and fast computed.

An efficient method for quantifying the multichannel EEG spatial-temporal complexity

The complexity index (delta) quantifies the intrinsic dimensionality of the global complexity of a point set, and was shown to be able to characterize electroencephalogram spatial-temporal features. The complexity index is conceptually comprehensible and easily implemented, yet, it is time consuming. In this paper, we present an efficient computational method based on the projection of the high-dimensional state-space points onto a one-dimensional axis. The computational time decreases by at least 50%, without affecting the measure accuracy.