This paper introduces the basic theory and arithmetic of Independent Component Analysis which is a novel technology developed for Blind Source Separation. The Fast ICA arithmetic is applied as an example in analysis and separation of artifacts in clinical multiple channel EEG. The experiment results testify that by using ICA method artifacts in EEG such as EOG and power line interference can be separated and removed effectively.
Algorithms ; Artifacts ; Electroencephalography ; methods

At the present time, a kind of monitoring technology assuring highly effectual anesthesia is urgently required in the clinical practice. Electroencephalogram (EEG) assumes a dominant position in the current research of the depth detection of anesthesia. In this paper, the monitoring technique of the depth detection of anesthesia by bispectral index (BIS) is systematically showed. The bispectral index is a compound parameter which is composed of time domain, frequency domain, and high order spectral subparameters of the electroencephalograph. This nonlinear compound calculation method is worth research and is of great significance to the development of a new monitor of closed-loop control of anesthesia.
Anesthesia ; methods ; Electroencephalography ; methods ; Monitoring, Intraoperative ; methods

Establishing the significance of observed effects is a preliminary requirement for any meaningful interpretation of clinical and experimental Electroencephalography or Magnetoencephalography (MEG) data. We propose a method to evaluate significance on the level of sensors whilst retaining full temporal or spectral resolution. Input data are multiple realizations of sensor data. In this context, multiple realizations may be the individual epochs obtained in an evoked-response experiment, or group study data, possibly averaged within subject and event type, or spontaneous events such as spikes of different types. In this contribution, we apply Statistical non-Parametric Mapping (SnPM) to MEG sensor data. SnPM is a non-parametric permutation or randomization test that is assumption-free regarding distributional properties of the underlying data. The method, referred to as Maps SnPM, is demonstrated using MEG data from an auditory mismatch negativity paradigm with one frequent and two rare stimuli and validated by comparison with Topographic Analysis of Variance (TANOVA). The result is a time- or frequency-resolved breakdown of sensors that show consistent activity within and/or differ significantly between event or spike types. TANOVA and Maps SnPM were applied to the individual epochs obtained in an evoked-response experiment. The TANOVA analysis established data plausibility and identified latencies-of-interest for further analysis. Maps SnPM, in addition to the above, identified sensors of significantly different activity between stimulus types.
Electroencephalography ; Magnetoencephalography ; Methods ; Random Allocation

The electroencephalogram (EEG) is a widely used non-invasive method for monitoring the brain. It is based upon placing conductive electrodes on the scalp which measure the small electrical potentials that arise outside of the head due to neuronal action within the brain. Historically this has been a large and bulky technology, restricted to the monitoring of subjects in a lab or clinic while they are stationary. Over the last decade much research eff ort has been put into the creation of “wearable EEG” which overcomes these limitations and allows the long term non-invasive recording of brain signals while people are out of the lab and moving about. This paper reviews the recent progress in this fi eld, with particular emphasis on the electrodes used to make connections to the head and the physical EEG hardware. The emergence of conformal “tattoo” type EEG electrodes is highlighted as a key next step for giving very small and socially discrete units. In addition, new recommendations for the performance validation of novel electrode technologies are given, with standards in this area seen as the current main bottleneck to the wider take up of wearable EEG. The paper concludes by considering the next steps in the creation of next generation wearable EEG units, showing that a wide range of research avenues are present.
Brain ; Electrodes ; Electroencephalography ; Head ; Methods ; Neurons ; Scalp

Aiming at the issue of motor imagery electroencephalography (EEG) pattern recognition in the research of brain-computer interface (BCI), a power feature method based on discrete wavelet packet decomposition is proposed for the channels C3 and C4. Firstly, a six-border Butterworth filter is used to denoise the two-channel EEG signals. Secondly, two-channel EEG signals are decomposed to five levels using Daubechies wavelet and the fourth level and the fifth level are chosen to reconstruct the signals and compute its power feature. Finally, linear discriminant analysis (LDA) is utilized to classify the feature and the Kappa value is utilized to measure the accuracy of the classifier. This method is applied to the standard dataset BCICIV_2b-gdf of BCI Competition 2008, and experimental results show that this method reflect the feature of event-related synchronization and event-related desynchronization obviously and it is an effective way to classify the EEG patterns in the research of BCI.
Brain-Computer Interfaces ; Electroencephalography ; instrumentation ; methods

Anesthesia as a necessary procedure in the process of surgical operation could restrain the response of patients to the damage stimulation; However, improper anesthesia could also result in severe misfortune for patients. At the present time, one kind of monitor technology assuring highly effectual anesthesia is exigently required in clinical practice and many researchers have actively undertaken investigations to seek the parameters predicting the depth of anesthesia (DOA). Electroencephalogram (EEG) assumes a dominant position in the current researches on detecting the depth of anesthesia. In this paper, the achievements of detecting the depth of anesthesia by means of EEG are systematically reviewed and the potentials are anticipated.
Anesthesia ; Anesthesiology ; methods ; Electroencephalography ; Humans ; Monitoring, Physiologic

The basic theory of Higher Order Spectral Analysis and the most generally used Bispectrum are introduced in the paper. By certain experiments of EEG signal acquisition and bispectrum analysis, it is showed that the Higher Order Spectrum has an advantage over power spectrum, which is based on Second Order Statistics, in processing non-linear signal and restraining Gauss noise signal.
Electroencephalography ; methods ; Signal Processing, Computer-Assisted

Sensory conflict impacts postural control, yet its effect on cortico-muscular interaction remains underexplored. We aimed to investigate sensory conflict's influence on the cortico-muscular network and postural stability. We used a rotating platform and virtual reality to present subjects with congruent and incongruent sensory input, recorded EEG (electroencephalogram) and EMG (electromyogram) data, and constructed a directed connectivity network. The results suggest that, compared to sensory congruence, during sensory conflict: (1) connectivity among the sensorimotor, visual, and posterior parietal cortex generally decreases, (2) cortical control over the muscles is weakened, (3) feedback from muscles to the cortex is strengthened, and (4) the range of body sway increases and its complexity decreases. These results underline the intricate effects of sensory conflict on cortico-muscular networks. During the sensory conflict, the brain adaptively decreases the integration of conflicting information. Without this integrated information, cortical control over muscles may be lessened, whereas the muscle feedback may be enhanced in compensation.
Humans ; Muscle, Skeletal ; Electromyography/methods* ; Electroencephalography/methods* ; Brain ; Brain Mapping

Accurate source localization of the epileptogenic zone (EZ) is the primary condition of surgical removal of EZ. The traditional localization results based on three-dimensional ball model or standard head model may cause errors. This study intended to localize the EZ by using the patient-specific head model and multi-dipole algorithms using spikes during sleep. Then the current density distribution on the cortex was computed and used to construct the phase transfer entropy functional connectivity network between different brain areas to obtain the localization of EZ. The experiment result showed that our improved methods could reach the accuracy of 89.27% and the number of implanted electrodes could be reduced by (19.34 ± 7.15)%. This work can not only improve the accuracy of EZ localization, but also reduce the additional injury and potential risk caused by preoperative examination and surgical operation, and provide a more intuitive and effective reference for neurosurgeons to make surgical plans.
Humans ; Scalp ; Brain Mapping/methods* ; Epilepsy/diagnosis* ; Electroencephalography/methods* ; Brain

The difference between sleep and wakefulness is critical for human health. Sleep takes up one third of our lives and remains one of the most mysterious conditions; it plays an important role in memory consolidation and health restoration. Distinct neural behaviors take place under awake and asleep conditions, according to neuroimaging studies. While disordered transitions between wakefulness and sleep accompany brain disease, further investigation of their specific characteristics is required. In this study, the difference is objectively quantified by means of network controllability. We propose a new pipeline using a public intracranial stereo-electroencephalography (stereo-EEG) dataset to unravel differences in the two conditions in terms of system neuroscience. Because intracranial stereo-EEG records neural oscillations covering large-scale cerebral areas, it offers the highest temporal resolution for recording neural behaviors. After EEG preprocessing, the EEG signals are band-passed into sub-slow (0.1‍-‍1 Hz), delta (1‍-‍4 Hz), theta (4‍-‍8 Hz), alpha (8‍-‍13 Hz), beta (13‍-‍30 Hz), and gamma (30‍-‍45 Hz) band oscillations. Then, dynamic functional connectivity is extracted from time-windowed EEG neural oscillations through phase-locking value (PLV) and non-overlapping sliding time windows. Next, average and modal network controllability are implemented on these time-varying brain networks. Based on this preliminary study, it appears that significant differences exist in the dorsolateral frontal-parietal network (FPN), salience network (SN), and default-mode network (DMN). The combination of network controllability and dynamic functional networks offers new insight for characterizing distinctions between awake and asleep stages in the brain. In other words, network controllability captures the underlying brain dynamics under both awake and asleep conditions.
Humans ; Wakefulness ; Electroencephalography/methods* ; Brain Mapping/methods* ; Brain