If you fail to run the ENA toolbox, please refer to the tool guide given in toolbox.
Here we show some interfaces of ENA system. More details can be seen in the ENA toolbox.
Fig. 1: System main interface.
Fig. 2: EEG data batch interface.
Fig. 3: Independent components of ICA.
Fig. 4: Welch power spectrum parameter setting interface.
Fig. 5: EEG inverse interface.
"The time-varying networks in P300: a task-evoked EEG study". Fali Li, Bei Chen, He Li, Tao Zhang, Fei Wang, Yi Jiang, Peiyang Li, Teng Ma, Rui Zhang, Yin Tian, Tiejun Liu, Daqing Guo, Dezhong Yao, Peng Xu*. IEEE T. Neur. Sys. Reh., 2016, 24(7): 725-733.
To be continue…
Based on Electroencephalogram (EEG), neuroimaging technologies and network analysis approaches are widely used to discover the brain's secret. Also it has many a time been regarded an important role for development of the brain-computer interaction that is used on intelligent robots, unmanned aerial vehicles, brain communications in the future. Most studies have showed that the interactions among different brain areas contribute to the ability of the brain in processing the corresponding information, while the diseases are also reported to be closely related to the connections among the whole brain structures. Until now, many EEG analysis software still lack the systematic brain network analysis function. Based on MATLAB platform, we achieve the graphical user interface system of EEG Network Analysis (ENA) that mainly includs the following aspects: 1. The improvement and innovation of brain electrical data analysis and processing algorithm; 2. The system supports batch processing data and several mainstream data loading and display, according to the diversity of EEG data format; 3. Preproccessing; 4. Using Welch, PCA, RobustICA, MNS methods to analyze the characteristics of EEG from time domain, frequency domain and spatial distribution; and 5. Brain network analysis including the undirected weighted network and directed network.
More details about the (ENA) system can be seen in our original paper, and it may be a promising technique for exploring various EEG data.
