In recent years, many electroencephalogram (EEG) devices have become portable and wireless. Given the requirements for mobility, EEG instruments need to be smaller, lighter, and power-efficient with reduced noise and offset. An EEG signal is very weak and its amplitude More
In recent years, many electroencephalogram (EEG) devices have become portable and wireless. Given the requirements for mobility, EEG instruments need to be smaller, lighter, and power-efficient with reduced noise and offset. An EEG signal is very weak and its amplitude ranging is from 20 to 200 µV and its frequency ranges from 0.1 to 100 Hz. Besides, the skin-electrode interface creates a large DC offset voltage which can be in the order of ±300 mV. These two challenges can disturb the main signal and reduce the detection accuracy. At the input of amplification section of many EEG circuits, the chopping technique has been applied to convert DC input signals into AC signals. The main transconductance amplifier as EEG instrumentation amplifier (IA) in most of the previous works is the folded cascode amplifier. In this paper, we proposed a circuit which designed in 0.18 CMOS technology and its amplifier is a two-stage fully recycling chopper stabilized folded cascode amplifier that operates at low supply voltage and its input-referred noise is decreased by enhancing transconductance while it has a large slew rate, a high DC gain and an improved gain bandwidth. These features significantly decrease the noise and offset without a considerable increase in the power required by the circuit. In the post-layout simulation the amplifier achieves a midband gain of 60 dB and a -3dB bandwidth in the range of 0.1-210 Hz. the chip area with pads is 512×512 μm2. The adjustable LPF has a 100 Hz cut-off frequency.
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To be effective, leaders need flexible access to their whole brain. Understanding how the human brain works and knowing how to best use the logical and social brain can provide a critical course for effective leadership. The present research by investigating 30 people f More
To be effective, leaders need flexible access to their whole brain. Understanding how the human brain works and knowing how to best use the logical and social brain can provide a critical course for effective leadership. The present research by investigating 30 people from government and non-government sector managers and simultaneously recording the brain waves and heart wave rhythm of these people while performing the cognitive task of evaluating the response control and risk-taking of decision-making CGT (CANTAB software) with the Procomp 2 coherence evaluation device EEG describes heart wave rhythm (HRV) based on neuroscience and neurocardiology. The findings showed that the correlation coefficient test of brain waves and heart waves in both resting and performing cognitive tasks had significant differences from each other, so that there is no similarity between the coherence of EEG and ECG in the resting state with the time of performing the cognitive task of CGT among government and non-government sector workers. was not observed. These results show that the changes in brain and heart waves of people during rest and decision time are different from each other, so that the changes in brain waves and heart waves and the cooperation of these waves with each other depend on the environmental, mental and emotional conditions of people.
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The purpose of this article is to classify electroencephalogram signals into two types of epilepsy and healthy.To achieve the highest accuracy, various techniques have been used. The desired characteristics of these signals can be extracted by Wavelet Transform and Empi More
The purpose of this article is to classify electroencephalogram signals into two types of epilepsy and healthy.To achieve the highest accuracy, various techniques have been used. The desired characteristics of these signals can be extracted by Wavelet Transform and Empirical Mode Decomposition methods.These two methods are compared in terms of impact in the classification process. To reduce the dimensions of the feature space, Independent and principal Component Analysis methods can be used. Then, in order to reduce the effect of noise on electroencephalogram signal analysis, a smoothing method can be applied.Finally, by using Support Vector machine classifier, the existing data classified.These steps were tested for an existing data set, including 5 groups of single channel electroencephalogram signals. Results show that the empirical decomposition method has high efficiency and accuracy to extract the characteristics and classification of signals. Accordingly, the accuracy and sensitivity of both combinations of "empirical mode decomposition - independent component analysis" and "empirical mode decomposition - principal component analysis", after data smoothing, as a new approach to extraction and classification of features are 100%. The output of this system is used to control and treat the disease.
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این مقاله در پی معرفی دو سیستم کلاسبندی مبتنی بر شبکههای عصبی Fuzzy Artmap برای تشخیص اتوماتیک حدودهای ناگهانی در امواج الکترد آلفا نوگرافی (EEG) 19 کانال اشخاص میباشد. این الگوریتم سریع و نتایح قابل قبولی عرضه مینمایند. سیگنالهای EEG به چهار زیر باند با استف More
این مقاله در پی معرفی دو سیستم کلاسبندی مبتنی بر شبکههای عصبی Fuzzy Artmap برای تشخیص اتوماتیک حدودهای ناگهانی در امواج الکترد آلفا نوگرافی (EEG) 19 کانال اشخاص میباشد. این الگوریتم سریع و نتایح قابل قبولی عرضه مینمایند. سیگنالهای EEG به چهار زیر باند با استفاده از تبدیل ویولت گسسته تقسیم بندی شدهاند. وروردیهای شبکه شامل دو ویژگی متفاوت هستند که از زیرباندهای 3 و 4 استخراج میشوند. عملکرد این کلاسبندی کنندهها در این مقاله معرفی شده و باهم و دیگر سیستمها مشابه مطابق با مقادیر حساسیت، ویژگی و انتخاب پذیری مقایسه گشتهاند.
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