Normal EEG Segments Research Articles

Experimental research on real-time acquisition and monitoring of wearable EEG based on TGAM module

Long-term sleep disorders can reduce the body’s immunity, induce various diseases, and seriously endanger human health Multi-conducting EEG (Electroencephalogram) sleep monitoring is the gold standard for evaluating the quality of sleep all night. However, this method is professional and complicated, and it is difficult to apply to daily sleep monitoring. This paper first designed a wearable EEG acquisition system based on TGAM module. ‘Through the parameter initialization setting of the TGAM module and the Bluetooth module and the analysis of the communication protocol between the modules, the wearable EEG acquisition system was successfully built. Secondly, the extraction of different rhythm waves of EEG signals is realized by several FIR (Finite Impulse Response) bandpass filters. Principal component analysis was used to screen feature quantities. The related experiments were carried out by collecting the EEG signals of several testers through the new sensor TGAM. Finally, event-related potential analysis and event-related simultaneous analysis were used to complete the treatment of EEG segments and normal EEG segments of sleep apnea events, and normalized EEG was identified by normalized standard deviation analysis and triple normalized standard deviation analysis. The results show that the technical requirements of wearable EEG equipment can be met in terms of algorithm calculation and result performance, which can provide theoretical support for sleep monitoring.

A novel approach for noise removal and distinction of EEG recordings

Abstract This paper presents a novel approach for the analysis of electroencephalography (EEG) signals. It is based on the distribution of the eigenvalues of a scaled Hankel matrix, which can enable us to determine the number of eigenvalues required for noise removal and signal extraction in singular spectrum analysis. This paper examines the applicability of the approach to discriminate between epileptic seizure and normal EEG signals, the extraction of attractive patterns, the filtering of EEG signals and the elimination of the noise included in the signals. Various criteria are used as features to distinguish between epileptic and normal EEG segments. The results indicate the capability of the approach for noise removal in both EEG signals, and for discrimination between them.

Ictal EEG classification based on amplitude and frequency contours of IMFs

Electroencephalogram (EEG) signal serves is a powerful tool in epilepsy detection. This study decomposes intrinsic mode functions (IMFs) into amplitude envelope and frequency functions on a time-scale basis using the analytic function of Hilbert transform. IMFs results from the empirical mode decomposition of EEG signals. Features such as energy and entropy parameters were calculated from the amplitude contour of each IMF. Other features, such as interquartile range, mean absolute deviation and standard deviation are also computed for their instantaneous frequencies. Discriminative features were extracted using a large database to classify healthy and ictal EEG signals. Normal EEG segments were differentiated from the seizure attack in individual IMF features, multiple features with individual IMF, and individual features with multiple IMFs. Discriminating capability of three Cases was tested. (i) Artificial neural network and (ii) adaptive neuro-fuzzy inference system classification were used to identify EEG segments with seizure attacks. ANOVA was used to analyze statistical performance. Energy and entropy-based features of instantaneous amplitude and standard deviation of instantaneous frequency of IMF2 and IMF1 have 100% accuracy, sensitivity, and specificity. Good performance with a single feature that represents information of the whole data was obtained. The result involved less complicated computation than other time–frequency analysis techniques.

Idiopathic generalized epilepsy: magnetic stimulation of motor cortex time-locked and unlocked to 3-Hz spike-and-wave discharges.

In 20 patients with idiopathic generalized epilepsy who showed typical 3-Hz spike-and-wave (SW) EEG complexes, we studied the corticospinal motor output with a transcranial electromagnetic stimulator. First we measured the corticospinal discharge threshold for both hemispheres in the patient group and compared it with that of 10 age- and sex-matched volunteers. Threshold was significantly higher in the patient group, regardless of whether subjects were treated with antiepileptic drugs (AEDs). In 4 patients with very frequent SW paroxysms, we were able to study motor evoked potential (MEP) changes time-locked to epileptic EEG transients. The EEG signal was recorded bipolarly (C3-P3, C4-P4) by scalp needle-electrodes. For a given stimulus intensity, we collected and measured MEPs occurring during the spike or the wave portion of the SW complexes. Data were compared with those of MEPs obtained time-locked to normal EEG segments. MEP size was significantly decreased when the cortical stimulus was time-locked to the wave component, and was decreased or unchanged when the stimulus was time-locked to the spike. Magnetic stimulation never produced remarkable side effects.