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This paper presents an EEG study for coherence and phase synchrony in mild cognitive impairment (MCI) subjects. MCI is characterized by cognitive decline, which is an early stage of Alzheimer’s disease (AD). AD is a neurodegenerative disorder with symptoms such as memory loss and cognitive impairment. EEG coherence is a statistical measure of correlation between signals from electrodes spatially separated on the scalp. The magnitude of phase synchrony is expressed in the phase locking value (PLV), a statistical measure of neuronal connectivity in the human brain. Brain signals were recorded using an Emotiv Epoc 14-channel wireless EEG at a sampling frequency of 128 Hz. In this study, we used 22 elderly subjects consisted of 10 MCI subjects and 12 healthy subjects as control group. The coherence between each electrode pair was measured for all frequency bands (delta, theta, alpha and beta). In the MCI subjects, the value of coherence and phase synchrony was generally lower than in the healthy subjects especially in the beta frequency. A decline of intrahemisphere coherence in the MCI subjects occurred in the left temporo-parietal-occipital region. The pattern of decline in MCI coherence is associated with decreased cholinergic connectivity along the path that connects the temporal, occipital, and parietal areas of the brain to the frontal area of the brain. EEG coherence and phase synchrony are able to distinguish persons who suffer AD in the early stages from healthy elderly subjects.

Epilepsy is a disease that attacks the nerves. To detect epilepsy, it is necessary to analyze the results of an EEG test. In this study, we compared the naive bayes, random tree forest and K-nearest neighbor (KNN) classification algorithms to detect epilepsy. The raw EEG data were pre-processed before doing feature extraction. Then, we have done the training in three algorithms: KNN Classification, naïve bayes classification and random tree forest. The last step was validation of the trained machine learning. Comparing those three classifiers, we calculated accuracy, sensitivity, specificity, and precision. The best trained classifier is KNN classifier (accuracy: 92.7%), rather than random tree forest (accuracy: 86.6%) and naïve bayes classifier (accuracy: 55.6%). Seen from precision performance, KNN Classification also gives the best precision (82.5%) rather than Naïve Bayes classification (25.3%) and random tree forest (68.2%). But, for the sensitivity, Naïve Bayes classification is the best with 80.3% sensitivity, compare to KNN 73.2% and random tree forest (42.2%). For specificity, KNN classification gives 96.7% specificity, then random tree forest 95.9% and Naïve bayes 50.4%. The training time of naïve bayes was 0.166030 sec, while training time of random tree forest was 2.4094sec and KNN was the slower in training that was 4.789 sec. Therefore, KNN Classification gives better performance than naïve bayes and random tree forest classification.

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Abstract. The aim of this study is to investigate and analyze the differences of power spectral distribution in various frequency bands between healthy subjects and schizophrenic patients. Subjects in this study were 8 people consisting of 4 schizophrenic patients and 4 healthy subjects. Subjects were recorded from 12 electrodes with Electroencephalography (EEG). EEG signals were recorded during a resting eye-closed state for 4-6 minutes. Data were extracted and analyzed by centering and filtering, then performed using Welch Periodogram technique for the spectral estimation with a Hamming window. The results of this study showed that delta power spectral in schizophrenic patients increased ten times from healthy subjects; theta power spectral in schizophrenic patients increased three times from healthy subjects; alpha power spectral in schizophrenic patients decreased with an increase of one third of healthy subjects. These results were confirmed by Kolmogorov-Smirnov test showing there were significant differences between schizophrenic and healthy subjects on delta, theta and alpha brain wave. Based on the results of Brain Mapping analysis showed that there was significant increasing in the activity of delta waves and theta waves in frontal lobe of schizophrenics, whereas the alpha waves indicated a decrease in the occipital lobe in all schizophrenic patients.

Abstract. Significant land use changes due to rapid development, a central issue in Bandar Lampung and high rainfall intensity are the main triggers for frequent flooding. This study was carried out to define design rainfall intensity based on analysis of hourly temporal rainfall pattern for calculating design discharge, predict the impact of land use changes on flood peaks, and predict the impact of infiltration well on flood peak reduction. The results showed that rainfall distribution pattern for storm duration of 4 h are 40, 35, 20 and 5% for the first, second, third and fourth hour, respectively. Analysis on land use changes underlined that if 30% of the catchment area is maintained for green land then flood peaks can be decreased. However, with city development, land conversions are intended for settlements, industries and trading areas which will increase flood peaks significantly. Application of infiltration well in the catchment can reduce surface runoff depends on the density and dimension of the well. The results suggest that using infiltration well with diameters between 0.8 to 1.4 m which are applied each in every 4000 m2 of land area will reduce flood peaks from 6.9 to 12.6%. While the application of infiltration well with density of 500 m2 will reduce flood peaks from 55.21 to 99.8%. Commitment and relevant government policies and community participation will encourage to undertake flood reduction measures.

Brainwave is widely used as an indicator of brain activity and can be detected by electroencephalography (EEG). The development of EEG device has become more advanced along with the invention of low-cost tiny electronic modules and wireless technology. This research aimed to develop a low-cost wireless modular device for brainwave acquisition based on Arduino microcontroller. The system was designed into sensor block for brainwave receiver and conditioning, and mainboard block for data processing. Dry-active electrode was developed as the sensor, followed by preamplifier module which was also installed at the sensor block. Active filter and DRL circuits were developed on the mainboard part. Arduino UNO was used as the main processor of the device. The developed modules were then evaluated using signal generator to examine the module characteristics and consistency. As the result, the preamplifier module was detected to reach 40.34 dB on gain ability. The cutoff frequency on the active filter module was calculated on 31 Hz. Furthermore, Arduino UNO was identified to have a consistency on input and output voltage.